
==== Front
PeerJ Comput Sci
PeerJ Comput Sci
peerj-cs
PeerJ Computer Science
2376-5992
PeerJ Inc. San Diego, USA

cs-2170
10.7717/peerj-cs.2170
Artificial Intelligence
Data Mining and Machine Learning
Emerging Technologies
Neural Networks
Schizophrenia diagnosis based on diverse epoch size resting-state EEG using machine learning
Alazzawı Athar 1
http://orcid.org/0000-0001-7249-4976
Aljumaili Saif 1
http://orcid.org/0000-0003-3014-9626
Duru Adil Deniz 2
http://orcid.org/0000-0001-6578-1969
Uçan Osman Nuri 1
Bayat Oğuz 1
http://orcid.org/0000-0002-4383-0472
Coelho Paulo Jorge 34
http://orcid.org/0000-0002-3394-6762
Pires Ivan Miguel 5impires@it.ubi.pt

1 Electrical and Computer Engineering, School of Engineering and Natural Sciences, Altinbaş University, Istanbul, Turkey
2 Neuroscience and Psychology Research in Sports Lab, Faculty of Sport Science, Marmara University Istanbul, Istanbul, Turkey
3 Polytechnic Institute of Leiria, Leiria, Portugal
4 Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra), Coimbra, Portugal
5 Instituto de Telecomunicações, Escola Superior de Tecnologia e Gestão de Águeda, Universidade de Aveiro, Águeda, Portugal
Angiulli Giovanni
20 8 2024
2024
10 e21704 3 2024
11 6 2024
© 2024 Alazzawı et al.
2024
Alazzawı et al.
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.

Schizophrenia is a severe mental disorder that impairs a person’s mental, social, and emotional faculties gradually. Detection in the early stages with an accurate diagnosis is crucial to remedying the patients. This study proposed a new method to classify schizophrenia disease in the rest state based on neurologic signals achieved from the brain by electroencephalography (EEG). The datasets used consisted of 28 subjects, 14 for each group, which are schizophrenia and healthy control. The data was collected from the scalps with 19 EEG channels using a 250 Hz frequency. Due to the brain signal variation, we have decomposed the EEG signals into five sub-bands using a band-pass filter, ensuring the best signal clarity and eliminating artifacts. This work was performed with several scenarios: First, traditional techniques were applied. Secondly, augmented data (additive white Gaussian noise and stretched signals) were utilized. Additionally, we assessed Minimum Redundancy Maximum Relevance (MRMR) as the features reduction method. All these data scenarios are applied with three different window sizes (epochs): 1, 2, and 5 s, utilizing six algorithms to extract features: Fast Fourier Transform (FFT), Approximate Entropy (ApEn), Log Energy entropy (LogEn), Shannon Entropy (ShnEn), and kurtosis. The L2-normalization method was applied to the derived features, positively affecting the results. In terms of classification, we applied four algorithms: K-nearest neighbor (KNN), support vector machine (SVM), quadratic discriminant analysis (QDA), and ensemble classifier (EC). From all the scenarios, our evaluation showed that SVM had remarkable results in all evaluation metrics with LogEn features utilizing a 1-s window size, impacting the diagnosis of Schizophrenia disease. This indicates that an accurate diagnosis of schizophrenia can be achieved through the right features and classification model selection. Finally, we contrasted our results to recently published works using the same and a different dataset, where our method showed a notable improvement.

Electroencephalogram
Schizophrenia
SVM
KNN
QDA
FCT-Fundação para a Ciência e TecnologiaUIDB/50008/2020 FCT/MECFEDER-PT2020 Partnership AgreementUIDB/00308/2020 This work was supported by FCT-Fundação para a Ciência e Tecnologia, I.P. by project reference UIDB/50008/2020, and DOI identifier https://doi.org/10.54499/UIDB/50008/2020. This work was also funded by FCT/MEC through national funds and co-funded by the FEDER-PT2020 partnership agreement under the project UIDB/00308/2020 (DOI 10.54499/UIDB/00308/2020). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
==== Body
pmcIntroduction

The brain’s nervous system controls human general behaviors; it plays a fundamental role in people’s lives, including their decisions, lifestyles, emotions, and judgments. Brain diseases are electrical abnormalities whose severity varies based on several factors, including the patient’s age, disorder location in the brain family medical history, and other undefined elements that distinguish one patient from another. For instance, diseases that affect the neural cells include Alzheimer’s, epilepsy, schizophrenia (SZ), Parkinson’s, etc. These disorders have varied symptoms, each affecting a specific area and function of the brain, which is discovered and diagnosed using various tools and tests.

Schizophrenia is a brain disorder burdensome the nerve cells; its symptoms appear obviously between the ages of 16 and 30 (Huang et al., 2019; Li et al., 2023). The evoked symptoms include delusions, hallucinations, depression, anxiety (Jauhar, Johnstone & McKenna, 2022), etc. People who have schizophrenia may have an abnormal perspective of the world around them (Zhu et al., 2022). Schizophrenia is one of the most severe illnesses that can eradicate the brain’s neural system. The WHO organization’s official report declared that there are more than 21 million subjects, of which around 1% suffer from mental diseases, all over the world (Sadeghi et al., 2022). These symptoms can be utilized to identify the patient’s condition into positive and negative (or cognitive) categories; thus, timely detection is considered an essential part of the recovery process for patients (Aslan & Akin, 2020). The neuroimaging techniques field provided the knowledge underlying symptoms of schizophrenia and other brain disorders, inspiring and accelerating scientific development to reach new goals in the medical field. The conventional technique for diagnosing ScZ is based entirely on the unique patient’s response and the experience psychiatrists have, which makes this process extremely subjective, biased, and time-consuming.

In recent years, electroencephalography devices (EEG) have shown a magnificent contribution to dealing with nervous system diseases and diagnosing them, such as epilepsy (Al-Azzawi, 2021; Al-azzawi et al., 2022), Alzheimer’s disease (Al-Jumaili et al., 2023; Ferdowsi et al., 2024; Kim et al., 2024; Nour, Senturk & Polat, 2024), and schizophrenia (Khare, Bajaj & Acharya, 2023). EEG was the ideal instrument for capturing the electrical activity of the brain using a variety of electrodes because of its non-invasive nature, which provides a high and complex brain dimensionality that contains a huge amount of data. EEG penetrates and collects the brain’s electrical activity, enabling the ability to diagnose illnesses that had hitherto eluded specialists. These recorded signals are then clearly sent to the outside world, which handles them in various ways that support the scientific objective. On the same side, in the medical field, machine learning (ML) is one of the most significant techniques that provides a sophisticated understanding of handling, processing, and analyzing various dataset types such as brain signals, genetic information, and medical images (Al-Jumaili et al., 2021; Al-Jumaili, Duru & Uçan, 2021). Besides, ML could diagnose, detect, predict, and classify different diseases precisely. ML handles schizophrenia detection in a new computational method compared to traditional methods, where signals’ hidden information is extracted from the data using a feature extraction technique.

Furthermore, the extracted features can reduce the signal’s dimensions, quantifying the most related data. In addition, these properties are used with either the frequency domain or the time domain (Aksöz et al., 2022; Gosala et al., 2023). Subsequently, many types of algorithms are utilized to enhance the precision of the findings of the analysis, diagnostic, and detection procedures. Linear discriminant analysis (LDA), support vector machines (SVM), and K-nearest neighbors (KNN) for instance. Since the precision ratio from previously published studies is generally fairly acceptable, we attempted to significantly improve the accuracy by proposing a method which summarize the most significant proposed methodological contributions in the detection of schizophrenia by EEG signals as follows: To propose a highly effective method for classifying epoched EEG signals of SZ and healthy controls using machine learning algorithms in terms of decreased complexity.

To investigate the effects of augmentation techniques using two methods SNR and stretch on classification accuracy.

To compare the classification performance of less number of electrode usage with varying epoch lengths of 1-, 2-, and 5-s.

Related work

There have been several publications and articles about the classification of schizophrenia; one of these articles was done by Hartini & Rustam (2021), classified the Schizophrenia dataset which had two groups (schizophrenic and non-schizophrenic). RBF and polynomial kernel functions were utilized, and they applied four different k-fold validation methods (3, 5, 7, and 10). The best outcome obtained when K = 10 was 69%. de Miras et al. (2023), to acquire machine learning classifiers for schizophrenia based on resting-state EEG data, they have assessed if machine learning techniques may aid in the diagnosis of the disorder. They have also developed a processing pipeline. They tested five machine learning algorithms: support vector machines (SVM), k-nearest neighbors (kNN), logistic regression (LR), decision trees (DT), random forest (RF), and SVM. SVM produced the best classification results (89%) when it came to separating patients with schizophrenia from healthy subjects. Further, Hassan, Hussain & Qaisar (2023), employed a multi-channel EEG signal dataset in their study to identify schizophrenia. They created a channel selection mechanism based on a thorough performance analysis of the convolutional neural network (CNN) while taking into account the unique EEG channels in various brain regions. To train the classification model, they combined several machine learning (ML) classifiers with CNN. Their results demonstrate that a hybridization of CNN and logistic regression (LR) utilizing three channels—T4, T3, and Cz—achieves 90% and 98% accuracy, respectively. Siuly et al. (2020) used EEG signal data with two groups, which are schizophrenia and healthy subjects. They applied empirical mode decomposition to the signals and then applied the Kruskal-Wallis test to select the most significant features. Then, all features were fed to the SVM to classify them. The highest result achieved was 93.21%. In their research, Khare et al. (2020) got 88% with SVM out of the five classification techniques used, including KNN, DA, ensemble method, and decision tree. They applied kurtosis, variance, root mean square, mean, and minimum as feature extraction methods on the EEG signal of the schizophrenia dataset and then used the Kruskal-Wallis test to select the best features. Additionally, Jain et al. (2022), classified the EEG signals into schizophrenia and control groups. Empirical Wavelet Transform has been used in their study to decompose the signals and they applied three different types of entropy (sample entropy, Shannon entropy, and log energy entropy) as feature extraction. Then four techniques were used (support vector machine (SVM), k-nearest neighbor (KNN), linear discriminant (LD), and neural network) with two types of K-folds (5 and 10) to achieve their goal. The best classification accuracy they achieved was 87% using SVM, KNN, and a neural network with k = 5.

Meanwhile, in this study, we proposed a new technique for schizophrenia disorder classification to distinguish schizophrenia patients from healthy people by using EEG signals. The EEG dataset is applied to the band-pass filter as a preprocessing phase, which blocks signals at undesirable frequencies from passing through in preparation for schizophrenia disorder classification. After preprocessing, the filtered EEG signals are split into the delta, theta, alpha, beta, and gamma frequency sub-bands. The suggested method’s primary objective is to precisely categorize schizophrenia by extracting the perceptive features of the Fast Fourier Transform, approximate entropy, log energy entropy, Shannon entropy, and kurtosis for every frequency band related to the EEG data signals. Also, each EEG band has been normalized by L2-normalization methods, where the normalization of EEG signals helps accuracy and performance enhancement in conventional ML models. Finally, these features of the EEG signals are fed into the three supervised machine learning classifiers used.

Based on the results obtained in previous studies and according to Table 1, which highlights the researchers’ use of various classifiers to classify schizophrenia disorder, we observe that different datasets with various techniques had been used, and the results were differentiated and did not meet expectations. Therefore, we propose a method that has the ability to further increase the accuracy of the diagnosis of schizophrenia.

10.7717/peerj-cs.2170/table-1 Table 1 Studies classified schizophrenia patients and healthy controls using different techniques.

Ref.	Year	Features	Electrode no.	Number of subjects	Classifiers	Acc%	
Patients	Control	
Devia et al. (2019)	2019	Event-related potentials	32	11	9	LDA	71	
Phang et al. (2019a)	2019	CNN	16	45	39	SVM	92.87	
Torres Naira & Del Alamo (2020)	2019	PCC	16	45	39	CNN	90	
Rajesh & Kumar (2021)	2021	SLBP	16	45	39	Logitboost	91.66	
de Miras et al. (2023)	2023	Multiple features techniques	31	11	20	KNN	87	
Kim et al. (2021)	2021	Multiple features techniques	19	14	14	SVM	75.64	
Ko & Yang (2022)	2022	Gramian angular field	9	49	32	VGGNet	93.20	
Buettner et al. (2019)	2019	FFT	19	14	14	Random forest	80	
WeiKoh et al. (2022)	2022	Local configuration pattern	19	14	14	KNN	97.20	
Lillo, Mora & Lucero (2022)	2022	Micro-states	19	14	14	CNN	93	
ArivuSelvan & Moorthy (2020)	2020	Thalamic	NA	115	76	ANN	83	
Park et al. (2020)	2020	Hippocampus	NA	86	66	LR, AB, XGBoost, and SVM	80.4	
Santos Febles et al. (2022)	2020	NA	64	54	54	Multi-kernel SVM	83	
De Rosa et al. (2022)	2022	Hippocampus	NA	20	20	RF	95	
Chin et al. (2018)	2018	NA	NA	141	71	SVM	92	
Sutcubasi et al. (2019)	2019	NA	NA	93	23	ANN	81.25	
Vyškovský, Schwarz & Kašpárek (2019)	2019	NA	NA	52	52	MLP	73.12	
Baygin et al. (2023)	2023	TQWT	NA	NA	NA	KNN	99.20	
Khare, Bajaj & Acharya (2023)	2023	Margenau–Hill time-frequency distribution	19	14	14	CNN	97.4	
16	45	39	99	
64	49	32	96	
Khare, Bajaj & Acharya (2023)	2021	SPWVD	64	49	32	CNN	93.36	

Materials and Methods

In the following paragraphs, this article will present the impact of the window size and the band-pass filter on classifying schizophrenia EEG signals using four traditional ML models. For the classification technique, firstly, we discuss the dataset availability, the preprocessing technique, and finally, the ML models used for SZ classification.

Dataset

The dataset used in our approach is a publicly available EEG dataset (Olejarczyk & Wojciech, 2017). The signals employed contain 28 subjects, 14 from each group: patients with paranoid schizophrenia and healthy controls under resting state with eyes closed condition.

Table 2 illustrates the EEG dataset details, where the montage was performed using a standard 10–20 system and the dataset was compiled using the following 19 EEG channels: Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, and O2 (Sazgar et al., 2019).

10.7717/peerj-cs.2170/table-2 Table 2 The details of the datasets used.

Features	Values	
Total subjects	28	
HC	14	
SZ	14	
Males (SZ)	7	
Females (SZ)	7	
Males (HC)	7	
Females (HC)	7	
Mean age (SZ)	28.1 ± 3.7 years	
Mean age (HC)	27.75 ± 3.15 years	
Mean age (Male SZ)	27.9 ± 3.3 years	
Mean age (Male HC)	26.8 ± 2.9 years	
Mean age (Female SZ)	28.3 ± 4.1 years	
Mean age (Female HC)	28.7 ± 3.4 years	
EEG segment	15 min	
No. of segments	21,702	
No. of segments without artefacts	30	
No. of channels	19	
Sampling Freq. (Hz)	250	

Processing and feature extraction

EEG signals may contain a variety of noises that have a noticeable impact on classification accuracy; therefore, preprocessing is an essential step in diminishing noise (artifacts). Thus, various filters can be applied to enhance the signal quality. Initially, we used a frequency filter to decompose the EEG signal and eliminate redundant frequencies. The bandpass filter divides data into five frequency sub-bands: delta rhythm (0.1–4 Hz), theta rhythm (5–9 Hz), alpha rhythm (10–14 Hz), beta rhythm (15–31 Hz), and gamma rhythm (32–100 Hz), by allowing only certain frequencies to pass through readily. This helps to filter out frequencies that are too high or too low, where there are a lot of high-frequency and noise components in the original brain data, and filtering functions help to identify the EEG’s detailed information.

These settings achieve the best possible compromise between reducing artifacts that are outside the relevant range of human EEG signals and preventing the development of new artifacts.

We applied the bandpass filter to the EEG data before it was segmented into specific window length. Figure 1 shows the results before and after applying the band-pass filter.

10.7717/peerj-cs.2170/fig-1 Figure 1 Decomposition of EEG signals using a band-pass filter.

Due to the limited number of subjects, we applied two augmented methods to the dataset involved: AWGN and Stretch to increase the size of the data by generating more training data, which positively effect intensive classifier learning. First, we applied AWGN which is a kind of noise that is utilized to add random noise to the EEG by adding Gaussian noise (signal-to-noise ratio (SNR)). Normally, the addition of SNR is in dB, and we applied a 10dB value with Signal power (measured). Equation (1) illustrates the mathematical representation of AWGN.

(1) SNR=10.log10(PsignalPnoise).

Second, we applied a stretching method to augment the signal, which in turn increases the training of the classifier. In addition, we used it to examine our proposed approach to classifying schizophrenia subjects. We performed the stretch method on the x-axis with a 20% time stretch for the original signal and employed it for the 19 channels.

Due to the biophysical properties such as conductivity differences of brain, skull and scalp tissues, it is not straightforward to investigate the cortical activity from the measured scalp potentials with a few electrodes. On the other hand, increasing the number of scalp electrodes requires more attention to decrease the impedance which is one of the crucial signal quality issues. Thus, the determination of the best-performing electrodes may allow for faster and cost-effective data collection. For this reason, we used different scenarios by reducing features using the MRMR feature selection technique. The MRMR algorithm is a sequential feature selection technique that locates an ideal set of characteristics that are both mutually and maximally different (Ding & Peng, 2005). The MRMR maximizes the feature set’s relevance to the response variable and minimizes its redundancy.

Since there is not a single, accepted rule for feature limitations, the number of features is open to debate. We discussed two feature reduction scenarios to examine our proposed model, which are eight, and five the best features.

In terms of window length, there are no standard criteria used by researchers to segment signals into a specific window size, while many studies have been conducted using different epochs window sizes (Kim et al., 2021; Sun et al., 2021; Vázquez, Maghsoudi & Mariño, 2021; Agarwal & Singhal, 2023; de Miras et al., 2023; Ranjan, Sahana & Bhandari, 2024). In order to investigate which time window is optimal, we used three different epoch window sizes for classification, and since the brain signals contain many frequencies, the choice of window size is crucial in affecting the final results: Therefore, a bandpass filter was used. The distribution range of the signal was between (0.1 to 100), where low frequencies (such as delta and theta) require a large window size. In contrast, higher frequencies (eg, beta, gamma) require a small window size. Therefore, the choices of the epoch sizes were (1, 2, and 3 s) to capture all the frequencies and evaluate the proposed method if it has the ability to obtain promising results. Also, in terms of computational efficiency, we evaluate the window size effect on feature extraction and the final results of the classifier, besides the time duration needed to predict speed and training time for the same classifier.

Bear in mind that determining classification accuracy depends on the ability to extract features from EEG signals, which is not just a difficult phase but also a critical and challenging step in the classification process. For schizophrenia, EEG patterns can be extracted using both time-domain and frequency-domain feature techniques. Time-domain feature extraction techniques use changes in signal time series to analyze EEG data, whereas non-linear analysis techniques have recently been widely used to analyze EEG data. As the next step to the preprocessing phase, various methods are used with EEG signals, such as fuzzy entropy, permutation entropy, symbolic dynamics-based entropy, sample entropy, time-frequency distributions, wavelet transform, and eigenvector methods to extract features.

In this study, six extraction methods were calculated for each window epoch to extract the hidden features. We implemented two cases: features implemented with the band-pass filter and features implemented without. Specifically, FFT, log energy entropy, kurtosis, and Shannon entropy were implemented with the band-pass filter; in contrast, approximate entropy was applied in both cases with the band-pass filter and without.

Fast Fourier transform

Fast Fourier transform (FFT) has changed the world, is one of the most important algorithms developed of all time, and is the enabling piece of technology in most digital communication, audio, image compression, and signals. The real reason for depending on FFT is its quick and effective method of denoising data. The FFT feature has been implemented on the SZ EEG signals to convert the time domain to the frequency domain (Sedik, Marey & Mostafa, 2023). It helps measure the power spectrum of the data from the frequency band. Equation (2) below illustrates the mathematical formulation of FFT, where X(K) means the Fourier coefficient of x(n), and the odd n and even n are compatible with odd numbers and even numbers with the frequency of K, respectively.

(2) X(K)=∑n=0N−1⁡X[n]WNkn=∑neven⁡x(n)nwNkn+∑nodd⁡x(n)wNkn

K=0,1……,N−1,

Approximate entropy

Entropy is the most frequently used feature to measure time-domain features and is also widely used in disease detection. Approximate entropy (ApEn) is defined as a measurement of the regularity or randomness of data in a time series and is used for short-length data due to its lower sensitivity to noise. Equation (3) shows the mathematical formula, where r stands for the similarity criterion, e stands for (the length of the data segment being compared), and N stands for (the length of data).

(3) ApEn(E,r,N)=1(N−e+1)∑i−1N−e+1⁡logCie(r)−1N−e∑i=1N−e⁡logCie+1(r).

Shannon entropy

Shannon and Weaver established entropy for information theory, which characterizes a signal’s complexity, irregularity, uncertainty, or unpredictability. ShnEn is a time-domain complexity metric that does not rely on the signal spectrum. A discrete signal’s ShnEn may be computed as Eq. (4):

(4) Hsh=−∑i=1Na⁡PilogPi.

Na is the total number of amplitude values in the signal with the I range, and pi is the probability of the signal with the ai amplitude.

In practice, instead of calculating all amplitude values, the probability density function of the signal is predicted using the histogram. Signal amplitudes are split into k bins to generate the normalized form of Hsh, and entropy is computed as Eq. (5):

(5) ShEn=HShogk

Log energy entropy

The ShnEn and log energy entropy (LogEn) are calculated using the entropy-based wavelet packet decomposition proposed by Coifman and Wickerhauser. Entropy is utilized as the feature method because it can be used to determine how random the information is Prasanna, George & Subathra (2024). LogEn, which is represented by Eq. (6), is a commonly used metric in signal processing that can extract relevant information:

(6) E=∑n⁡log(wi,jn2)

where (wi,jn2) are the computed WPD coefficients.

Kurtosis

The word kurtosis, which means ‘tailedness’, comes from the Greek word ‘kyrtos’ or ‘kurtos’ (The MathWorks, Inc., 2022). It is considered one of the shape measurements and is historically defined as the peakedness of a distribution. Nowadays, it is just clarifying the tail extremity, which means either existing outliers (for the sample kurtosis) or the propensity to produce outliers (for the kurtosis of a probability distribution). A normal distribution will have a kurtosis of three, called mesokurtic, while a distribution of more than three is called leptokurtic and called platykurtic, which is less than three. Kurtosis has been used to find the normality of data for statistical analysis from range 1 to infinity throughout the below Eq. (7) (The MathWorks, Inc., 2022).

(7) κ=E(x−μ)4σ4

After the feature extraction step, and due to the features extracted having different scales, normalization is important to ensure the feature will be in the same range, which can improve training speed and performance. The normalized features are input for the classification: SVM, KNN, QDA, and EC. FFT, ApEn, LogEn, ShnEn, and kurtosis are all computed using a MATLAB routine.

Normalization

Standardization and normalization are the two most common feature scaling methods in machine learning. Normalization is regarded as a crucial phase in the production of data. Generally, normalization is the most widely used method with data in linear transformations. Feature scaling, whose values range from 0 to 1, makes comparing data easier. Therefore, we utilized the L2 normalization method in our approach, and the outcomes served as inputs for the next stage, classification, which raised and improved our accuracy results.

Classification

The classification of individuals with schizophrenia was done using the following four classifiers widely used in classifying EEG signal datasets. The baseline techniques were employed as part of the proposed ML method described in this section: SVM, KNN, QDA, and EC. The following section will give a quick overview of each of these approaches.

Support vector machine

In recent years, many articles have used SVM to classify various datasets. The most critical component in SVM is the hyperplane, which affects the accuracy of the result based on the optimal hyperplane used to classify the dataset into different groups. Furthermore, the class boundaries are subject to finding the ultra-optimal level, which removes some irrelevant data from the training data set to reduce classification error. SVM employs a variety of kernels, including Polynomial, Gaussian, Radial Basis Function (RBF), Laplace RBF, Sigmoid, and Anove RBF, etc., which are named a (kernel trick). However, as shown in the mathematical expression in Eq. (8), the two essential parameters are W and (Z-B), where W represents the transport vector and B is the displacement of that vector. The distance from Z to the hyperplane is:

(8) D(z)=W∙Z−B,whereZ∈{AifD(z)>0BifD(z)<0.

K-nearest neighbors

KNN is a non-parametric classification method that uses a distance check to discover the classifier’s closest neighbors. The Euclidean distance equation is used to calculate distance, and the mathematical formula is presented below in Eq. (9). To classify a certain (X), the classifier check measures the distance between the (X) and the other data during the training phase. It assigns it a specific label (K) indicating its class, and KNN will do the same with all data until all data is.

(9) dis(x1,x2)=∑i=1n⁡(x1i−x2i)2.

Quadratic discriminant analysis

Quadratic discriminant analysis (QDA) is a machine learning and statistical classification classifier that uses quadric surfaces to classify two or more classes of different types of information. It is a more appropriate form than a linear classifier. QDA, in particular, employs a Gaussian distribution for each class. As demonstrated by mathematical Eq. (10). P(x|y=k) is modeled as a multivariate Gaussian distribution with density, and d is the number of features.

Figure 2 illustrates the pipeline for the categorization method applied in this research using the entropy family, FFT, and kurtosis.

10.7717/peerj-cs.2170/fig-2 Figure 2 Classification EEG signal data with schizophrenia disorder in two labels: schizophrenia and healthy control.

(10) P(x|y=k)=1(2π)d2|∑k|12exp(−12(x−μk)t∑k−1⁡(x−μk)).

Ensemble classifier

It is a machine learning method that combines more than one model to provide an accurate classification. There are different types of algorithms used such as decision trees, neural networks, support vector machines, or k-nearest neighbors. It relies on each model’s advantages to provide an outcome that is more robust and durable than any of the models alone. Ensemble classifier (EC) can handle the complexity of a multidimensional dataset and reduce the variance and bias of classification results, by overcoming the weak points of each classifier (Ranjbari et al., 2021).

Evaluation metrics

In this section, we explain the method that we used to validate each model independently. We used multiple forms of performance rating scales by using a confusion matrix which is one of the most used methods to examine, evaluate, and represent the classifier efficacy. The results were obtained through the classifiers using four sorts of outcomes, namely true positive (TP), true negative (TN), false positive (FP), and false negative (FN). Table 3 shows the formulas used to compute the assessment metrics: accuracy, sensitivity, specificity, precision, negative predictive value (NPV), F1-Score, and Matthew’s correlation coefficient (MCC) (Chicco & Jurman, 2023).

10.7717/peerj-cs.2170/table-3 Table 3 Evaluation metrics.

Accuracy=(TP+TN)/(TP+FP+TN+FN)	
Sensitivity=TP/(TP+FN)	
Specificity=TN/(TN+FN)	
Precision(PPV)=TP/(TP+FP)	
NegativePredictiveValue(NPV)=TN/(TN+FN)	
F1−score=(2∗TP)/(2∗TP+FP+FN)	
MCC=((TP∗TN)−(FP∗FN))/√((TP+FP)(TP+FN)(TN+FP)(TN+FN))	

Results

This section presents the results of our proposed method. This article investigated the role of the window epoch size and the impact of the band-pass filter in schizophrenia EEG signal classification using the database from (Olejarczyk & Wojciech, 2017). First, the used dataset had two classes: 14 patients with schizophrenia condition and 14 with healthy conditions, and the dataset was available and could be accessed. The recording signals were collected from 19 channels, and all the outcomes were evaluated according to the frequencies of 250 Hz. In addition, we investigated the impact of the band-pass filter on the signals that had been recorded by EEG, which affected the performance of the models. In addition to the traditional techniques, we used data-augmented methods (AGW and stretched signals) and the MRMR features selection method using the eight and five best features.

Then, we looked at the effectiveness of six feature extraction techniques (FFT, ApEn, LogEn, ShnEn, and kurtosis) side by side with SVMs, KNNs, QDA, and EC.

After extracting the attributes, we normalized the data using the L2-normalization method, and then the algorithms were trained using three epoch sizes (1, 2, and 5 s) to assess more effectively. This produced three scenarios using machine learning algorithms for classifying schizophrenia disorder according to each epoch’s size.

One-second epoch size

In the first scenario, we used a 1-s epoch window size and relied on the performance of the six features. These were used as an effective tool for EEG signal analysis and have been applied to diagnose neurological diseases.

SVM and EC outperformed the other two classifiers with 99% accuracy when implementing LogEn; in contrast, KNN, and QDA obtained 98%, and 96% accuracy values, respectively. Table S1 presents the actual and predicted values of the confusion matrix for the four classifiers, while Table 4 demonstrates the calculated parameters: sensitivity, specificity, precision, NPV, F1-Score, and MCC. The results obtained with the highest accuracy for the above parameters were 99% for SVM and EC. According to all the above results, KNN was in second place, while QDA came last.

10.7717/peerj-cs.2170/table-4 Table 4 One-second epoch size classification performance results of four classifiers.

CT	FE	Evaluation metrics	
Acc	Sen	Spe	Pre	NPV	F1-score	MCC	Prediction speed (sec)	Training time (sec)	
SVM	FFT	97	97	97	97	98	97	95	11,879.4	250.2	
KNN	95	95	95	95	95	95	90	1,196.39	108.48	
QDA	93	95	93	91	95	93	87	105,281.1	3.769	
EC	96	96	97	97	96	96	93	124.4	1,262.4	
SVM	ApEn	82	87	78	78	86	82	65	5,295.21	875.15	
KNN	73	85	63	68	82	75	49	1,221.87	298.98	
QDA	75	98	53	66	97	79	57	63,405.56	5.025	
EC	85	81	89	87	84	84	71	676	155	
SVM	ApEn + Bandpass	82	86	77	78	86	82	64	104,405.87	1,453.77	
KNN	73	84	62	67	81	75	46	5,236.17	21.4	
QDA	74	98	53	66	97	79	57	674,686.27	1.11	
EC	81	74	92	94	70	83	66	14,347.6	1,326.4	
SVM	Shannon entropy	87	83	93	94	81	88	76	114,062.06	3,434.53	
KNN	82	76	88	89	74	82	64	833.21	238.26	
QDA	92	92	92	93	90	93	84	94,346.25	4.578	
EC	95	94	96	96	95	95	91	28,210.4	2,641.6	
SVM	Log energy entropy	99	99	99	99	99	99	99	31,065	2,370.53	
KNN	98	98	99	99	98	99	97	390.78	321.09	
QDA	96	94	99	99	93	97	93	137,986.41	5.36	
EC	99	99	99	99	99	99	98	113.1	1,280.4	
SVM	Kurtosis	73	71	75	78	68	74	47	1,319.60	96.82	
KNN	63	78	45	63	63	70	25	7,330.22	2,659.32	
QDA	68	59	80	78	61	67	39	117,144.71	4.076	
EC	71	76	67	70	73	73	43	12,829.4	1,286.9	
Note:

Where Classifier Types (CT), Feature Extraction (FE), Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), Precision (Pre).

Moreover, Fig. S1 illustrates the ROC curves obtained, particularly the 1-s epoch size with pure dataset (without augmented or reduced features), using the six features’ methods, including the ApEn with and without Bandpass-Filter for the SZ dataset. These diagrams visually represent the classification performance, showing how the four classifiers used on the SZ dataset are between the true positive rate (sensitivity) and the false positive rate (specificity).

For utilizing the SNR signal as a dataset applied to the feature extraction methods, Table S2 represents the confusion matrices for all classifiers. When using the results obtained from the SNR augmented method, they were all ideal using LogEn feature with Band-pass filter. Further, Table 5 illustrates the measured parameters for classifiers and shows 99% for all. Moreover, the prediction speed and training time were fairly acceptable.

10.7717/peerj-cs.2170/table-5 Table 5 One-second epoch size classification performance results with SNR for four classifiers.

CT	FE	Evaluation metrics	
Acc	Sen	Spe	Pre	NPV	F1-score	MCC	Prediction speed (sec)	Training time (sec)	
SVM	FFT	97	97	98	97	98	97	95	18,340.3	190	
KNN	96	95	96	95	96	95	92	424.4	1,054.4	
QDA	95	93	97	96	94	95	91	32,623.1	21.1	
EC	97	96	97	97	97	97	94	31.7	5,436.5	
SVM	ApEn	72	66	78	78	67	71	45	3,962.8	851.7	
KNN	70	63	79	80	61	71	42	4,113.2	1,104.4	
QDA	65	62	67	58	70	60	29	281,495	1.9	
EC	69	63	75	73	65	68	39	11,821.7	2,369.5	
SVM	ApEn + Bandpass	72	68	77	75	70	71	45	1,152.3	1,688.6	
KNN	69	62	78	79	60	70	41	586.1	3,191.7	
QDA	69	61	81	84	57	71	42	39,658.7	24.1	
EC	67	64	70	63	71	64	34	5,181.5	4,721.9	
SVM	Shannon entropy + Bandpass	90	86	95	94	87	90	81	2,894.6	821.8	
KNN	95	94	95	94	95	94	90	400.1	3,061.6	
QDA	69	59	96	98	45	74	49	60,912.9	15.3	
EC	99	99	99	99	99	99	98	38,264.8	3,527.2	
SVM	Log energy entropy + Bandpass	99	99	99	99	99	99	99	47,392.1	98.1	
KNN	99	99	99	99	99	99	99	554.5	405.4	
QDA	99	99	99	99	99	99	99	34,577.3	24.5	
EC	99	99	99	99	99	99	99	40	5,756.9	
SVM	Kurtosis + Bandpass	67	63	70	65	68	64	34	826.5	1,924.4	
KNN	62	59	64	54	68	56	23	785.2	3,338	
QDA	59	58	59	34	79	43	16	32,784.7	29.2	
EC	63	57	72	74	54	65	30	30,899.4	4,791.8	
Note:

Where Classifier Types (CT), Feature Extraction (FE), Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), Precision (Pre).

Figure S2 illustrates the ROC curves obtained, particularly the 1-s epoch size and applying the SNR augmented method on the dataset, using the six features’ ‘methods’. These graphs visually represent the classification performance, showing how the four classifiers used on the SZ dataset are between the true positive rate (sensitivity) and the false positive rate (specificity). The effectiveness of an individual classifier is represented by each curve on the ROC graph.

Table S3 shows the confusion matrices for all classifiers when using the Stretch signal as a dataset for feature extraction. When employing the Stretch-augmented technique, all the results were optimal when combined with the LogEn feature and a Band-pass filter. Furthermore, Table 6 provides the measured parameters for classifiers, all accuracies were fairly acceptable for all classifiers. Likewise, the prediction speed and training duration were also adequate.

10.7717/peerj-cs.2170/table-6 Table 6 One-second epoch size classification performance results with stretch for four classifiers.

CT	FE	Evaluation metrics	
Acc	Sen	Spe	Pre	NPV	F1-score	MCC	Prediction speed (sec)	Training time (sec)	
SVM	FFT	97	97	97	97	97	97	94	14,815.9	336.8	
KNN	96	96	95	95	96	95	92	222.5	2,293.3	
QDA	93	90	95	95	90	92	86	42,727.4	27.2	
EC	96	95	97	97	95	96	93	30.2	7,720.7	
SVM	ApEn	92	92	92	90	94	91	84	3,580.1	781.1	
KNN	90	91	89	87	93	89	81	1,828.3	1,965.6	
QDA	81	84	87	86	85	85	72	350,875.9	3.8	
EC	91	89	92	90	91	90	81	507.8	2,325.6	
SVM	ApEn + Bandpass	79	72	87	87	72	79	60	973	1,948.4	
KNN	71	61	93	95	51	75	50	297.5	3,079.9	
QDA	71	61	97	98	49	75	53	42,705	32.3	
EC	77	67	92	93	63	78	58	12	11,851.3	
SVM	Shannon entropy + Bandpass	87	80	95	94	80	86	75	886.2	2,874.4	
KNN	93	92	95	94	93	933	87	254.5	4,914.9	
QDA	63	55	96	98	35	71	41	57,873.2	23.5	
EC	97	96	98	98	97	97	95	23,228.9	6,893.9	
SVM	Log energy entropy + Bandpass	99	99	99	99	99	99	98	20,876.8	566.4	
KNN	98	98	99	99	98	98	97	256.5	30,776.2	
QDA	96	92	99	99	93	96	92	31,882.6	26.8	
EC	99	99	99	99	99	99	98	11.1	38,298.1	
SVM	Kurtosis + Bandpass	73	67	82	82	66	74	49	499.8	2,660.4	
KNN	69	65	72	67	70	66	38	330.8	4,418.7	
QDA	49	47	87	98	100	63	17	38,595	30.3	
EC	73	66	83	83	65	74	49	10,616.1	7,548.9	
Note:

Where Classifier Types (CT), Feature Extraction (FE), Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), Precision (Pre).

The ROC curves generated using the six features’ methods and the stretch approach applied to the dataset are shown in Fig. S3, with special attention to the 1-s epoch size. The four classifiers employed on the SZ dataset are represented visually in these graphs and the effectiveness of an individual classifier is represented by each curve on the ROC graph.

The latest developments indicate that utilizing EEG with fewer features, possibly even as few as five or eight, could completely transform the technology’s usability and affordability. This technique aims to find the optimal features to work with, which saves setup time, and complexity. In the concept of this study, two scenarios are used to reduce the features by selecting eight and five best features. Tables S4 and S5 show the results of the confusion matrix obtained by using the features after reducing to five and eight and using these features as inputs to the classifiers. Tables 7 and 8 show the ability of the classifiers to classify with fewer features, as the results obtained were good compared to the results obtained using 19 channels.

10.7717/peerj-cs.2170/table-7 Table 7 One-second epoch size classification performance results with five electrodes for four classifiers.

CT	FE	Evaluation metrics	
Acc	Sen	Spe	Pre	NPV	F1-score	MCC	Prediction speed (sec)	Training time (sec)	
SVM	FFT	92	90	94	93.7	90.8	91.9	84	21,765	501.3	
KNN	90.8	86.7	95	94.6	87.6	90.5	82	1,350.7	471.2	
QDA	81.7	71.8	92.9	92	74	80	65.6	186,543	2.5	
ENS	91.9	91	92.7	93	90.6	92	83.8	98.2	1,870	
SVM	ApEn	74	65.5	84.7	84	66	73.7	50	3,980.7	398.3	
KNN	72.6	68.8	77	78.6	67	73	45.9	1,492.1	740.1	
QDA	67.5	68	65	87.6	36	76.6	28	312,847.1	22.1	
ENS	72	64.5	81.5	80	66.6	71	46	17,681.4	727.6	
SVM	ApEn + Bandpass	75.6	68	85	85	68	75.6	53	4,978.5	786.3	
KNN	67.8	70	57.7	88	29.6	78	22	2,650	2,590.1	
QDA	69.7	62	95.5	97.8	42.9	75.9	48	243,101.9	2.9	
ENS	73.7	67	88	92.9	54	78	51	51,469.9	1,322.8	
SVM	Shannon entropy+ Bandpass	71.9	68.6	84	94	41.9	79	43.6	4,299	2,598.6	
KNN	90.9	85.7	96.6	96.5	86	90.8	82	1,987.9	2,655.1	
QDA	58.5	42.5	87	85	45.9	56.8	30	220,989	13.9	
ENS	96.8	95.7	97.9	97.6	96	96.7	93.7	41,987.9	3,001.8	
SVM	Log energy entropy + Bandpass	97.7	96.6	98.9	99	96	97.8	95.5	35,987.3	26,932	
KNN	97	94.7	99.6	99.6	95	97	94.5	1,150.7	1,100.9	
QDA	91.9	85.6	99	98.9	85.9	91.8	84.8	67,542.9	22.9	
ENS	98.9	98.8	99	98.8	99	98.8	97.9	89.9	26,987	
SVM	Kurtosis + Bandpass	67.5	64.6	73	82.7	50.9	72.6	35.6	4,562	968.9	
KNN	63	50	81.9	79.8	53.7	61.7	32.9	2,879.9	1,192.3	
QDA	48.8	48	59	93.6	8	63.5	3	296,150	2	
ENS	68.9	62.5	85.5	91.7	47	74	43	43,617.9	2,000	
Note:

Where Classifier Types (CT), Feature Extraction (FE), Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), Precision (Pre).

10.7717/peerj-cs.2170/table-8 Table 8 One-second epoch size classification performance results with eight electrodes for four classifiers.

CT	FE	Evaluation metrics	
Acc	Sen	Spe	Pre	NPV	F1-score	MCC	Prediction speed (sec)	Training time (sec)	
SVM	FFT	95	94.5	96	96.6	94	95.5	90.8	3,780.4	209.1	
KNN	94.9	95	94	95	94	95	89.6	870	554	
QDA	90.7	88	92.9	92.6	88.9	90	81.5	153,476.1	15.7	
ENS	95.6	93.6	97.8	97.9	93	95.7	91	66.9	2,867	
SVM	ApEn	82	77	87	86.7	78	81.7	64.6	6,850.8	459	
KNN	81.8	79.7	83.9	82.8	80.9	81	63.8	13,654	550	
QDA	69.8	70.9	66	87.8	39.6	78	31.8	269,483.9	27.9	
ENS	80	79	81.8	88.5	68.9	83.6	59	27,691.1	659	
SVM	ApEn + Bandpass	76.7	84.7	46.5	85.6	44.8	85	30.8	3,547.9	12.3	
KNN	70.6	68.7	73	78.9	61.8	73	41	889.3	1,679.3	
QDA	68.9	88.9	61	46.9	93	61	45	91,734.5	27	
ENS	75.8	81	57.5	86	48.7	83.7	36.9	55.9	3,879.3	
SVM	Shannon entropy + Bandpass	80.8	78	83.9	86	75	82	61.8	7,132.8	845.1	
KNN	94.8	93.8	95.8	95.7	94	94.8	89.7	2,029.2	1,590.9	
QDA	59.9	51	97.7	99	31	67.7	38	16,573.8	2.8	
ENS	97.8	96.6	98.9	98.8	97	97.7	95.7	19,831.3	3,981.4	
SVM	Log energy entropy + Bandpass	98.6	99	97.7	97.9	99	98.6	97	65,741.8	155.2	
KNN	98.9	98.9	98.9	98.8	98.9	98.9	97.8	2,803.9	380	
QDA	94.8	90.5	99.5	99.5	90.5	94.8	90	166,790.1	4	
ENS	98.9	98.9	98.9	99	98.8	99	97.9	55.3	2,500.8	
SVM	Kurtosis + Bandpass	68.6	62.6	76.5	77.9	60.7	69	38.9	2,980.6	2,691	
KNN	63.7	59	68	67	61	63	27.9	1,877.8	1,190.1	
QDA	49.8	47.7	90.5	98.9	8	64	16.5	124,531.5	5.9	
ENS	68.7	65	74	80.7	56.5	72	38.5	12,671.9	2,098.1	
Note:

Where Classifier Types (CT), Feature Extraction (FE), Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), Precision (Pre).

Figures S4 and S5 display the ROC curves produced by applying the reduction features strategy to the dataset and the six feature approaches. The 1-s epoch size should be noted. These graphs show the four classifiers used on the SZ dataset visually, with each curve on the ROC graph representing the efficacy of a single classifier.

Two-second epoch size

The next step of the proposed method is to classify schizophrenia by the selected features with a 2-s epoch window size. In the process of training and testing the EEG data using the four algorithms with the 19-electrode dataset. The confusion matrix of the proposed method shown in Table S6, summarizes the actual and predicted values obtained from the experiments. Whereas the results are shown in Table 9, which compares the accuracy resulting from the classification of schizophrenia in the proposed model with other learning models. These results show that the proposed method has an average accuracy of 99% in classifying healthy and schizophrenia subjects and has misclassified only one of the test samples. On the other hand, the prediction speed with 2-s epoch for both higher classifiers (EC and SVM) were only 204.5921 and 42,483.20 s. Again, the SVM and EC classifiers got identical accuracy utilizing the Log Energy Entropy feature, and compared with the other metrics values, KNN came in second, and QDA came in last.

10.7717/peerj-cs.2170/table-9 Table 9 Two-second epoch size classification performance results of four classifiers.

CT	FE	Evaluation metrics	
Acc	Sen	Spe	Pre	NPV	F1-score	MCC	Prediction speed (sec)	Training time (sec)	
SVM	FFT	97	97	97	96	97	96	94	78,870.56	14.18	
KNN	94	94	95	93	95	93	88	1,109.58	55.65	
QDA	93	90	96	94	92	92	86	133,488.53	1.87	
EC	96	95	97	96	96	95	92	203.4	332.7	
SVM	ApEn + Bandpass	77	73	81	79	76	76	54	1,7452.77	39.71	
KNN	71	62	88	91	55	74	48	844.01	77.51	
QDA	71	61	97	98	49	75	52	138,362.86	1.753	
EC	76	68	89	91	65	77	57	211.1	315.9	
SVM	ApEn	86	84	88	86	87	85	72	57,978.78	444.40	
KNN	85	83	88	85	85	84	70	8,527.96	9.11	
QDA	78	69	90	91	66	79	58	319,534.75	1.45	
EC	85	81	89	87	83	84	70	1,033.5	52.7	
SVM	Shannon entropy	60	78	58	20	95	32	23	1,700.017	123.67	
KNN	90	87	92	91	88	89	79	1,245.70	58.06	
QDA	69	61	95	97	45	75	49	50,695.47	3.97	
EC	96	95	97	97	96	96	93	32,070	147.2	
SVM	Log energy entropy	99	99	99	99	99	99	99	42,483.20	26.64	
KNN	99	98	99	99	99	99	98	1,096.95	57.44	
QDA	97	94	100	100	95	97	94	133,697.57	1.78	
EC	99	98	98	99	99	99	98	204.5921	388.2029	
SVM	Kurtosis	73	70	76	70	76	70	46	43,297.83	52.54	
KNN	67	67	67	52	79	59	33	6,695.99	11.71	
QDA	51	48	89	98	13	64	20	162,253.80	1.56	
EC	74	68	81	80	69	73	49	16,782.5	423.9	
Note:

Where Classifier Types (CT), Feature Extraction (FE), Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), Precision (Pre).

Although SVM and EC produce superior ROC values for SZ, KNN often performs better in class distinction than SVM. In Fig. S6, when comparing SVM and EC to other classifier techniques, QDA performs worse in the data because its ROC curve is often lower.

Then, the 2-s epoch window size was applied to the data that was augmented by using the SNR method. The confusion matrix results of our technique are clarified in Table S7, whereas the performance parameters recorded greatly varying rates with the six features utilized, as illustrated in Table 10. In contrast, the four classifiers using log energy entropy with a bandpass filter obtained equal and ideal values, which is a result of the 2-s epoch window size that was used.

10.7717/peerj-cs.2170/table-10 Table 10 Two-second epoch size classification performance results with SNR for four classifiers.

CT	FE	Evaluation metrics	
Acc	Sen	Spe	Pre	NPV	F1-score	MCC	Prediction speed (sec)	Training time (sec)	
SVM	FFT	96	95	96	95	96	95	91	26,290.9	90.2	
KNN	94	95	93	92	96	93	88	954.8	334.4	
QDA	94	93	95	95	94	94	89	78,916.6	7.8	
EC	96	96	97	96	96	96	93	102.5	1,194.1	
SVM	ApEn	74	73	75	69	79	71	48	5,980.7	207.9	
KNN	73	71	74	67	77	69	45	6,348.8	284.6	
QDA	64	70	62	35	88	47	28	189,720.1	1.1	
EC	72	69	74	68	75	69	43	503.7	658.7	
SVM	ApEn + Bandpass	78	74	81	79	77	76	56	2,919.6	243.5	
KNN	74	67	84	85	65	75	51	1,017.8	347.5	
QDA	70	62	84	87	56	72	45	51,116	8.3	
EC	75	68	83	83	68	75	52	104.9	1,194	
SVM	Shannon entropy + Bandpass	91	86	96	95	87	90	82	8,940	120.2	
KNN	96	96	96	96	96	96	92	2,208.3	543.3	
QDA	71	61	96	97	49	75	52	39,289.4	16.8	
EC	99	99	99	99	99	99	98	16,290.4	2,479.8	
SVM	Log energy entropy + Bandpass	99	99	99	99	99	99	99	44,797	41.8	
KNN	99	99	99	99	99	99	99	1,585.7	95.5	
QDA	99	99	99	99	99	99	99	39,550.8	13.3	
EC	99	99	99	99	99	99	99	63.9	1,408	
SVM	Kurtosis + Bandpass	69	65	71	66	71	65	37	1,616.7	447.4	
KNN	61	62	61	38	80	47	21	836.8	5,14.4	
QDA	58	67	57	14	94	24	15	57,521.9	7.9	
EC	66	60	73	72	61	66	34	13,041.8	1,274.7	
Note:

Where Classifier Types (CT), Feature Extraction (FE), Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), Precision (Pre).

For visual representation, the ROC is considered one of the methods used to check the performance of classifiers when comparing different classes. Figure S7 illustrates the ROC curves produced by applying the SNR approach to the dataset and the six attributes. The graphs show the four classifiers used on the SZ dataset visually, with each curve on the ROC graph representing the success rate of a single classifier.

In addition to the SNR technique, the dataset underwent the stretch augmentation method, a technique for enhancing classification accuracy by diversifying the data’s plausible changes. It was a way to tackle the small data issue and has shown an encouraging result. The Confusion Matrix results are shown in Table S8. Here is a breakdown of how well the augmented method performed: The accuracy of this technique, which combines ML models and feature extraction methods, is determined by the percentage of successful classifications, as shown in Table 11, which illustrates the performance of the classifiers. The accuracies varied for the six features; in contrast, the LogEn feature gained with SVM, KNN, and EC 99%. However, with an accuracy of 97%, the QDA also performs admirably.

10.7717/peerj-cs.2170/table-11 Table 11 Two-second epoch size classification performance results with stretch for four classifiers.

CT	FE	Evaluation metrics	
Acc	Sen	Spe	Pre	NPV	F1-score	MCC	Prediction speed (sec)	Training time (sec)	
SVM	FFT	96	95	96	95	96	95	92	25,162.8	100.8	
KNN	94	93	94	93	94	93	88	587.3	527.9	
QDA	92	90	94	92	91	91	84	60,337.6	12.6	
ENS	95	95	96	95	95	95	91	83.3	1,445	
SVM	ApEn	95	95	95	95	96	95	91	9,231.9	179.3	
KNN	94	95	94	93	96	94	89	2,597.8	477.8	
QDA	87	86	87	85	88	85	74	403,344.1	2.2	
ENS	95	94	96	95	95	95	90	912.2	701	
SVM	ApEn + Bandpass	83	78	88	86	81	82	67	4,478.3	244.4	
KNN	76	68	85	84	69	75	53	637.2	684.1	
QDA	73	62	96	97	55	75	55	60,083.6	23.3	
ENS	82	73	94	94	74	82	68	79	1,641.1	
SVM	Shannon entropy + Bandpass	88	82	95	95	82	88	77	7,891.3	289.2	
KNN	94	93	95	94	94	93	88	1,793.6	792.1	
QDA	67	58	96	97	41	72	46	40,134.3	19.2	
ENS	97	97	98	98	97	97	95	21,243.5	1,947.5	
SVM	Log energy entropy + Bandpass	99	99	99	99	99	99	99	52,458.3	76.7	
KNN	99	99	99	99	99	99	98	2,132.5	190.5	
QDA	97	95	99	99	96	97	95	36,103.9	16	
ENS	99	99	99	99	99	99	99	53.5	1,976.8	
SVM	Kurtosis + Bandpass	73	69	78	75	72	72	47	2,777	554.3	
KNN	68	66	69	60	74	63	35	1,378.5	775.1	
QDA	50	47	85	97	12	64	17	38,185.8	41.2	
ENS	75	66	88	90	63	76	54	44,475.1	1,295.8	
Note:

Where Classifier Types (CT), Feature Extraction (FE), Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), Precision (Pre).

The ROC curves obtained via the SNR technique on the dataset and the six characteristics are shown in Fig. S8. The four classifiers utilized on the SZ dataset can be seen using such graphs, where each curve on the ROC graph denotes the achievement efficiency of a specific classifier.

Features reduction was applied to the dataset to investigate the possibility of achieving maximum accuracy with a minimum feature number. We applied a 2-s epoch window size to the dataset with feature reduction, which sped up computational time and improved the overall efficiency of EEG testing. This procedure was done in two different cases: the highest five and eight features. Tables S9 and S10 show the results obtained using the confusion matrix, which will show the performance of the classifiers with four outcomes: true positive, true negative, false positive, and false negative. As well as the accuracy shown in Tables 12 and 13 of all classifiers and feature extraction methods. The highest results were obtained in both cases (five and eight features) using the features of Log Energy Entropy. While FFT ranked second in terms of the highest results, the other feature outcomes were varied. The effect of reducing the features from eight to (five had no significant impact on the results, as the results were very similar, especially when using log energy entropy. EC achieves better results than the other classifiers, whereas the others were acceptable in both cases. Figures S9 and S10 show the ROC curves to determine which classification algorithm is most suitable for both current cases. With high precision and recall for both electrodes above, the classifiers appear to perform well in properly classifying schizophrenia, and results showed that the model can learn effectively.

10.7717/peerj-cs.2170/table-12 Table 12 Two-second epoch size classification performance results with five electrodes for four classifiers.

CT	FE	Evaluation metrics	
Acc	Sen	Spe	Pre	NPV	F1-score	MCC	Prediction speed (sec)	Training time (sec)	
SVM	FFT	91	88	93	90.6	91.5	89.5	81.9	11,392.9	167.3	
KNN	90	87.7	92	90	89.9	89	80	2,048.9	231	
QDA	79.6	73.5	85.8	84	75.8	78.5	59.7	164,357	455.9	
ENS	91.6	98	57	92.6	84	95	65	255.9	470.8	
SVM	ApEn	83	82	84	80	85	81.5	66	11,987	60.9	
KNN	82.5	82.8	82	84	80.9	83	65	43,994	123.9	
QDA	66	58	78	79.5	56	67	36	173,921	19.1	
ENS	81.5	82	80	80	82	81	63	13,999.2	161.9	
SVM	ApEn + Bandpass	82.6	83.5	81.8	81	84	82	65	12,963.1	109.5	
KNN	72.9	64.8	88.9	92	56	76	50.9	5,106.3	166.1	
QDA	72.9	61.6	94.5	95.5	56	74.9	54	254,110.8	2.1	
ENS	80.9	74.5	91	92.9	69.5	82.7	64	348	423.7	
SVM	Shannon entropy + Bandpass	72	63	89.5	91.7	56.9	74.7	50.6	26,567.5	269	
KNN	93.8	93	94	94.7	92.9	94	87.7	8,410.3	299.1	
QDA	58.9	61.6	53.5	72.6	41	66.6	14	155,539	2	
ENS	97	98	95.9	96	97.9	97	94	26,718.3	569.9	
SVM	Log energy entropy + Bandpass	98.6	98	99	99	98	98.6	97	104,827.1	98	
KNN	98	97.9	98	98	98	98	96	7,180.3	107.2	
QDA	94.8	96	93	93	96	94.7	89.6	159,914.8	17.8	
ENS	98.7	99	98	98	99	98.5	97	195.5	488.7	
SVM	Kurtosis + Bandpass	70	67.6	73	71.8	69	69.6	41	8,970.6	149.1	
KNN	62	59	90	98	20	74	30	624,973	209.8	
QDA	50.7	48	61	85.5	20	61.8	7	162,115	2.2	
ENS	72	62.5	83	81	65.5	70.7	46	15,123	631.3	
Note:

Where Classifier Types (CT), Feature Extraction (FE), Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), Precision (Pre).

10.7717/peerj-cs.2170/table-13 Table 13 Two-second epoch size classification performance results with eight electrodes for four classifiers.

CT	FE	Evaluation metrics	
Acc	Sen	Spe	Pre	NPV	F1-score	MCC	Prediction speed (sec)	Training time (sec)	
SVM	FFT	94.9	93.5	96	95	94.7	94	89.7	32,176.5	98	
KNN	93.6	93.5	93.7	94	93	93.8	87	1,167.8	271.9	
QDA	87.5	83	91	89.8	85.6	86	75	105,423.2	1.5	
ENS	95.8	95	96.6	96.6	95	95.8	91.7	110.9	920.7	
SVM	ApEn	90	92	88.7	90.9	90.8	91.7	81.5	7,690	188.1	
KNN	90.6	91.8	88.5	93	86.8	92	80	14,108	205	
QDA	77.7	81	72	81	72	81	53.7	112,851	16.3	
ENS	88.8	88	89	89	88	88.8	77.7	17,121	231.5	
SVM	ApEn + Bandpass	81.6	76	89	90.9	72.6	82.9	64.5	131	182,345	
KNN	75.5	63	90	88.7	67	73.8	54.6	2,849.8	175	
QDA	74	96.6	67	47	98.5	63.5	54	141,822.1	2	
ENS	82.7	79	84.6	74	88	76.7	63	130	673.8	
SVM	Shannon entropy + Bandpass	82.9	74	94	94	73.7	83	68	112.5	219,342	
KNN	96.9	96	97.6	97.6	96	96.9	93.9	7,342.2	241	
QDA	72	60.5	97	97.7	53.7	74.7	54	96,512.3	13	
ENS	97.6	95.9	99	99	96	97.6	95	349	1,023.9	
SVM	Log energy entropy + Bandpass	98.7	99	98	98	99	98.7	97.5	82,751.2	32	
KNN	98.8	98.9	98.7	99	98	99	97.5	6,789.7	58.1	
QDA	97.7	97	98	98.7	96	98	95	98,451.8	2	
ENS	98.9	99	98.9	99	98	99	97.8	147.4	644	
SVM	Kurtosis + Bandpass	70	63	78.8	79.5	62	70	41.9	5,999	171.7	
KNN	63.5	61	65.9	64	62.8	62.7	27	5,055.7	254.3	
QDA	51.5	47	89.5	97.6	15.5	63.9	22	74,523	14.9	
ENS	73.9	68	80	80.5	68	74	48.7	15,521.6	709.9	
Note:

Where Classifier Types (CT), Feature Extraction (FE), Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), Precision (Pre).

Five-second epoch size

In the last scenario, a 5-s epoch size was used, in the first case, the data was used without changing or modifying anything (no augmentation or features reduction). As shown in Table 14, the four classifiers are identical to the two scenarios above and got the highest result using the SVM with the Log Energy Entropy feature. For the highest accuracy here, the sensitivity, specificity, precision, NPV, F1-Score, and MCC were 98%, 99%, 98%, 98%, and 97% for each parameter, respectively. Also, we illustrated the confusion matrix values in Table S11. Thus, we demonstrated that our model yielded high classification performance, and the area under the curve rate of each ROC is higher than 99%, as presented in Fig. S11.

10.7717/peerj-cs.2170/table-14 Table 14 Five-second epoch size classification performance results of four classifiers.

CT	FE	Evaluation metrics	
Acc	Sen	Spe	Pre	NPV	F1-score	MCC	Prediction speed (sec)	Training time (sec)	
SVM	FFT	95	95	96	95	96	95	91	62,512.1	5.5	
KNN	92	92	93	91	93	92	85	2,861.3	10.7	
QDA	93	91	95	94	92	92	86	111,541.6	1.8	
EC	95	93	96	95	94	94	90	316.7	89.5	
SVM	ApEn	78	71	91	92	66	80	60	9,289.1	8.5	
KNN	72	63	90	93	52	75	49	2,418.4	9	
QDA	73	64	95	97	53	77	54	56,887.9	1.5	
EC	83	79	86	83	83	81	65	1,293.9	18.6	
SVM	ApEn + Band-pass	85	82	87	84	85	83	69	73,877.4	31.7	
KNN	84	84	83	77	89	80	66	9,025	9.4	
QDA	77	67	90	90	67	77	57	43,113	3.9	
EC	77	70	89	91	65	79	58	16,629.5	53.9	
SVM	Shannon entropy	85	89	83	77	92	83	70	56,088.3	138.8	
KNN	89	86	93	91	88	89	79	2,021.8	16.1	
QDA	71	62	94	96	51	75	52	48,728.9	4.1	
EC	96	95	97	97	96	96	93	16,963.8	58.1	
SVM	Log energy entropy	99	99	99	99	99	99	98	75,324.9	2.7	
KNN	99	98	99	99	98	98	97	3,365.5	8.6	
QDA	98	95	100	100	96	98	95	120,410.5	1.3	
EC	98	98	99	99	98	98	97	16,881.6	59.6	
SVM	Kurtosis	70	65	80	71	76	68	46	22,516.1	10.4	
KNN	69	63	74	73	65	68	38	10,477.2	6.3	
QDA	54	49	89	97	18	66	25	145,563.7	1.7	
EC	69	63	78	79	62	70	41	17,675.1	64.7	
Note:

Where Classifier Types (CT), Feature Extraction (FE), Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), Precision (Pre).

Table S12 shows the confusion matrices for each classifier when using the SNR signal as a dataset for the feature extraction techniques. Each of the outcomes from the SNR approach worked perfectly when the LogEn feature with the band-pass filter was used. Furthermore, Table 15 displays 99% for all classifiers and indicates the measured parameters. Also, the training duration plus prediction speed were satisfactory. The ROC curves are displayed in Fig. S12 to help identify the best classification algorithm.

10.7717/peerj-cs.2170/table-15 Table 15 Five-second epoch size classification performance results of four classifiers with SNR augmented method.

CT	FE	Evaluation metrics	
Acc	Sen	Spe	Pre	NPV	F1-score	MCC	Prediction speed (sec)	Training time (sec)	
SVM	FFT	94	94	94	93	95	93	94	23,731	22.3	
KNN	92	93	92	89	95	91	92	1,892.1	100.4	
QDA	93	92	94	93	93	92	93	42,874.9	2.1	
EC	95	95	95	94	96	95	95	260.7	271.8	
SVM	ApEn	91	91	91	89	92	90	82	27,528.2	20.6	
KNN	90	90	91	89	92	89	81	8,871.1	59.7	
QDA	77	83	74	63	89	72	56	156,474.5	0.76	
EC	92	90	93	92	92	91	84	1,746.3	96.4	
SVM	ApEn + Bandpass	87	85	88	85	88	85	74	22,700.2	22.7	
KNN	83	81	85	82	84	82	67	2,428.7	48.6	
QDA	79	72	87	87	72	79	60	58,816.2	2.5	
EC	88	84	92	91	86	88	77	234	243.1	
SVM	Shannon entropy + Bandpass	91	86	96	96	87	90	82	22,872.7	18.3	
KNN	97	97	97	96	98	97	95	7,718.8	56.5	
QDA	75	65	95	96	58	78	58	36,348.4	1.7	
EC	99	99	99	99	99	99	98	16,828.4	283.8	
SVM	Log energy entropy + Bandpass	99	99	99	99	99	99	99	49,353.9	13.7	
KNN	99	99	99	99	99	99	99	5,558.3	34.2	
QDA	99	99	100	100	99	99	99	50,101.1	2.2	
EC	99	99	99	99	99	99	99	272.2	185.6	
SVM	Kurtosis + Bandpass	71	68	74	68	74	68	42	20,627.9	43.2	
KNN	64	60	66	57	69	59	27	1,905.3	71.1	
QDA	59	59	60	35	79	44	17	49,366.9	2.6	
EC	72	66	77	76	68	71	44	12,206.5	343.2	
Note:

Where Classifier Types (CT), Feature Extraction (FE), Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), Precision (Pre).

When employing the stretched signals as a dataset for feature extraction approaches, Table S13 displays the confusion matrices for each classifier. Using the LogEn functionality with the band-pass filter allowed all the Stretch approach’s results to function flawlessly. In addition, Table 16 shows the measured parameters and shows 99% for all classifiers. In terms of prediction speed, the EC classifier has excellent accuracy. Impressively, the algorithm’s stability and dependability were demonstrated effectively with prediction speed. Moreover, Fig. S13 illustrates the ROC obtained stretch method.

10.7717/peerj-cs.2170/table-16 Table 16 Five-second epoch size classification performance results of four classifiers with stretch signals.

CT	FE	Evaluation metrics	
Acc	Sen	Spe	Pre	NPV	F1-score	MCC	Prediction speed (sec)	Training time (sec)	
SVM	FFT	94	94	95	93	95	94	89	21,108.7	28.4	
KNN	92	94	90	88	95	91	84	2,407.5	67.4	
QDA	92	91	93	92	93	92	85	69,198.1	2.2	
EC	94	94	94	92	95	93	88	212.1	263.9	
SVM	ApEn	94	94	95	94	95	94	89	193,844.3	282.6	
KNN	95	95	95	93	96	94	90	7,429.2	56.2	
QDA	88	87	89	87	89	87	76	234,666.4	0.8	
EC	96	95	96	95	96	95	92	992.4	107.1	
SVM	ApEn + Bandpass	92	89	93	92	91	91	84	21,119.6	24.7	
KNN	89	85	93	92	86	88	79	1,420.4	81.8	
QDA	80	71	94	95	68	81	64	60,932.5	2.5	
EC	94	91	97	96	92	93	88	237.4	234.5	
SVM	Shannon entropy + Bandpass	90	85	96	95	86	90	81	29,809	21.3	
KNN	96	96	96	95	96	96	92	7,885	63.8	
QDA	73	63	95	96	53	76	54	79,655.8	1.5	
EC	98	97	98	97	98	97	96	27,852.6	357.2	
SVM	Log energy entropy + Bandpass	99	99	99	99	99	99	99	39,936.6	26.3	
KNN	99	99	99	99	99	99	99	5,976.8	37.3	
QDA	99	98	99	99	99	99	98	61,461.4	2.5	
EC	99	99	99	99	99	99	99	177.3	279.8	
SVM	Kurtosis + Bandpass	74	70	79	76	73	73	49	19,357.3	58.2	
KNN	67	67	67	55	78	60	34	4,651.1	77.7	
QDA	55	50	83	94	23	65	24	70,696.9	2.1	
EC	78	72	86	86	72	78	59	11,186.7	375	
Note:

Where Classifier Types (CT), Feature Extraction (FE), Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), Precision (Pre).

The confusion matrices computed for the datasets consisting of reduced features, are displayed in Tables S14 and S15, respectively. Compared to the findings utilizing 19 electrodes, Tables 17 and 18 demonstrate the classifiers’ capacity to classify with fewer features since the former’s results were superior and close to the results when 19 electrodes were used. Finally, Figs. S14 and S15 show the ROC for the last scenario.

10.7717/peerj-cs.2170/table-17 Table 17 Five-second epoch size classification performance results with five electrodes for four classifiers.

CT	FE	Evaluation metrics	
Acc	Sen	Spe	Pre	NPV	F1-score	MCC	Prediction speed (sec)	Training time (sec)	
SVM	FFT	88.6	84.8	92	91	86	87.9	77	36,398.2	19.5	
KNN	90	90	91	91	90.6	90.7	81.6	8,167.8	28.1	
QDA	82	82	81.9	85.7	78	84	64	101,433	1.4	
ENS	90.8	87	92.8	87.9	92.5	87.6	80	505.7	98.2	
SVM	ApEn	85.8	85.5	86	89.9	80.6	87.6	71	36,288	25.4	
KNN	84.8	83.7	85.7	82	86.8	83	69	42,568	28.1	
QDA	65	67.7	64	46.9	80.8	55	29	89,231.5	12.9	
ENS	80.6	72	86	77	82.5	74.8	59	12,184.2	44	
SVM	ApEn + Bandpass	87	87.9	85.8	91	79.9	89.8	72.7	69,544.4	21.9	
KNN	82.9	81.5	84	82	83.6	81.8	65.8	7,745.6	29.9	
QDA	70.9	69	83	96	29.9	80	37	64,465	1.5	
ENS	87.8	83.7	92	91	84.7	87	75.9	598.8	92	
SVM	Shannon entropy + Bandpass	76.9	71.8	89	94	57	81	55.9	26,398	61.9	
KNN	95	93	97	97	93	95	90	14,132.5	68.3	
QDA	72	68.7	79	86.9	55.8	76.7	45	81,321.1	2.1	
ENS	97.8	98	97	98	96.5	98	95	15,212.6	139.3	
SVM	Log energy entropy + Bandpass	98.6	98	99	99	98	98.6	97	91,126.7	9.1	
KNN	97.8	96	99	99	96	97.7	95.6	15,123	12.9	
QDA	91.7	86	98	98	85	91.9	84	183,989	1.1	
ENS	98.9	99	98.9	98.8	99	98.9	97.9	456.5	92.9	
SVM	Kurtosis + Bandpass	71	58.6	83	76	68.6	66	43	39,912.3	430	
KNN	65	59.9	71	70.9	60	64.9	31	9,169.5	50.8	
QDA	57.9	55	67.7	85	30	66.9	19	83,304	2.2	
ENS	73	66.6	80	81	66	73	47.5	11,659.1	166.9	
Note:

Where Classifier Types (CT), Feature Extraction (FE), Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), Precision (Pre).

10.7717/peerj-cs.2170/table-18 Table 18 Five-second epoch size classification performance results with eight electrodes for four classifiers.

CT	FE	Evaluation metrics	
Acc	Sen	Spe	Pre	NPV	F1-score	MCC	Prediction speed (sec)	Training time (sec)	
SVM	FFT	92.5	94	90.8	91	93.7	92.7	85	26,322.1	17	
KNN	93	92.8	93	91.9	93.9	92	85.9	5,121.8	27	
QDA	88.7	90.8	86.7	86.5	90.9	88.6	77.5	52,143.1	2.9	
ENS	94.8	94.6	95	92.6	96	93.6	89	404	129	
SVM	ApEn	93.5	93.5	93.5	91.6	95	92.5	86.8	42,561.7	28	
KNN	93	94	92	92.8	93.8	93.6	86.6	28,679	19	
QDA	78.8	86	71.7	74.6	84.5	80	58.5	69,534.8	14.9	
ENS	90.8	91	90.6	94.5	85	92.7	80.5	6,980.3	35.5	
SVM	ApEn + Bandpass	90	89	91	90.8	89.5	89.9	80	43,616.2	13.3	
KNN	87	83	91	91	83.6	87	74.6	5,761.7	28.2	
QDA	75.8	63	95	95	62.6	76	58	15,432.8	1	
ENS	92.8	90	95.6	96	89	93	85.7	533.1	110.6	
SVM	Shannon entropy + Bandpass	82.8	74	93.6	93.5	74.5	82.7	67.9	45,943.2	27.3	
KNN	96.9	95	98	97.9	96	96.5	93.7	13,144.9	48.2	
QDA	69	60	94	96.9	45	74	47.8	119,905	0.7	
ENS	97	97.6	97	96	98	97	94.6	26,179.5	138	
SVM	Log energy entropy + Bandpass	98.5	97.6	99	99	98	98	97	105,432	4	
KNN	98.9	98.6	99	99	98.6	98.9	97.9	137,432.5	5	
QDA	98.9	99	98.5	98.8	99	99	97.9	90,543.9	1	
ENS	98.8	98	99	99	98	98.7	97.7	421	94	
SVM	Kurtosis + Bandpass	73.7	72	75.8	80.6	66	76	47	19,697	91.9	
KNN	69	64.6	72.9	66.8	70.9	65.7	37.7	8,813.4	40	
QDA	55	49	76	87.7	30	62.9	21	74,699.9	1	
ENS	77	65	87.5	82	74	72.6	54	13,087	159	
Note:

Where Classifier Types (CT), Feature Extraction (FE), Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), Precision (Pre).

Accordingly, and for more investigation of the classifier’s ability and to make sure from the evaluation, we used the error bar to illustrate data variability because when you have small data, the range of errors expands. The variability or uncertainty of the outcome values can be known through error bars, with the standard deviation, standard error, and confidence interval error. How standard deviations describe the data you gathered changes from one measurement to the next. On the other hand, standard errors indicate how much the mean result may vary if the entire experiment were redone. Error bars create lines with bar charts extending from the edge or the center of the displayed data point. The length of an error bar aids in revealing the degree of uncertainty surrounding a data point. While a big error bar would suggest that the values are more dispersed and less dependable, a small error bar indicates that the data are concentrated, indicating that the plotted averaged value is more likely. We measured the error bars for all cases (standard, stretch, SNR, MRMR features reduction) that were used in this study. In Fig. 3, for the first case in which the data used was standard data, the error bars are relatively small if the window size is 2 s compared to the window size of 1 and 3 s, indicating low variance.

10.7717/peerj-cs.2170/fig-3 Figure 3 The mean error bars for the accuracy of the classification SZ using three Epoch window sizes with four classifiers based on standard dataset.

The error bars in Fig. 4, for the second scenario in which the SNR method was used, the error rate is greater than the first case in all cases, and this indicates that when using this technique (SNR), the error rate will be greater.

10.7717/peerj-cs.2170/fig-4 Figure 4 The mean error bars for the accuracy of the classification SZ using three Epoch window sizes with four classifiers based on the SNR dataset.

While the third case is when using a stretch dataset, Fig. 5 shows the three windows used (1, 2, and 5), where the error rate was varied in the window of 1 and 2 s, but the difference is very clear when the window was 5 s in size.

10.7717/peerj-cs.2170/fig-5 Figure 5 The mean error bars for the accuracy of the classification SZ using three Epoch window sizes with four classifiers based on the stretch dataset.

In the last case, when using the MRMR method, the results were close in both cases, especially in one and two window sizes Epoch, counter to the five the difference was clear. Figures 6 and 7 indicate that in the case of five features, the possibility of obtaining good results while reducing the impact and thus enabling greater consistency in outcomes between several windows. In general, the results obtained were promising and indicate that it is possible to classify schizophrenia even in the case of reduced features.

10.7717/peerj-cs.2170/fig-6 Figure 6 The mean error bars for the accuracy of the classification SZ using three Epoch window sizes with four classifiers based on the five electrodes dataset.

10.7717/peerj-cs.2170/fig-7 Figure 7 The mean error bars for the accuracy of the classification SZ using three epoch window sizes with four classifiers based on the eight electrodes dataset.

According to the findings obtained from the entropy attribute, the best explanatory electrodes were found to be concentrated in the frontal, central, and parietal areas. The frequency of the occipital entropy was distributed among the theta and beta bands while frontal entropy was observed in beta and gamma bands. Frontal gamma band power was shown to be a discriminator between SZ and healthy groups by Mitra et al. (2015). On the other hand, resting state theta band power was shown to increase in SZ patients (Iglesias-Tejedor et al., 2022). The literature findings of the resting state EEG of the SZ patients mostly overlap with the reduced features that we obtained. Thus, it seems as a promising approach to reduce the features to be used in the classification of SZ from healthy group. On the other hand, alpha band entropy of the parietal electrode was one of the main features that discriminates the resting state EEG of SZ group from healthy ones.

From the three scenarios above, it can be observed that the window size significantly influences the accuracy values since when the epoch was 1-s in size, accuracy was at its best and fell as the epoch size gradually increased or decreased. The second significant result was the evaluation metrics related to window epoch size that got the best accuracy.

Finally, visual assessment assists physicians in the identification of the signal of the disorder and its location in the brain. The power spectrum density can illustrate the difference between the signals of a patient suffering from schizophrenia compared to the signals of a healthy one. The healthy signals will appear homogeneous and constant, whereas the subject signals exhibit inconsistencies and odd rhythms.

Discussion

For a long time, it was believed that schizophrenia affected both sexes equally often. However, according to more current evidence, males are more likely than women to suffer from schizophrenia (González-Rodríguez et al., 2023). The way that diseases progress depending on a person’s gender is becoming more and more recognized in medicine. Men typically experience the early signs of schizophrenia before women do. It is well known that there are gender variations in schizophrenia, which are said to have an impact on several areas, such as symptom profiles, illness progression, and disease start (Ayesa-Arriola et al., 2020).

The age range when it occurs most frequently in men is 18 to 25, but for women, it happens around 4 years later (Gogos et al., 2019; Zorkina et al., 2021). As well, during menopause, women again face a second wave of disease (Barker & Vigod, 2023). Schizophrenia progresses more severely in men (Esposito et al., 2024). Although gender differences in schizophrenia are usually noted after diagnosis, a recent review indicates that they may exist before clinically detectable symptoms appear, with men showing worse premorbid functioning than women, such as more social disengagement, isolation, and inadequate self-care (Hoffman et al., 2022).

Apart from the gender disparities in social and behavioral areas, males are more likely than women to exhibit anomalies in brain morphology when diagnosed with schizophrenia. Gross anatomical examinations, for instance, reveal more severe frontal and temporal lobe atrophy in men and larger ventricular enlargement (Petric et al., 2024). Moreover, males have more anomalies in the microstructure of white matter (Chatterjee et al., 2020).

SZ comprises disorders with somewhat overlapping brain correlates and phenomenology (Nagy et al., 2023). Moreover, SZ appears to result from intricate interactions between endogenous and exogenous variables that influence neurodevelopment. Schizophrenia and other psychotic diseases are characterized by five essential characteristics. Delusions, hallucinations, wildly chaotic or aberrant motor action, disorganized thought processes (inferred from speech) (Baandrup, 2020). Treating SCZ at its earliest stage may assist in slowing down the disease’s course.

Many articles have been published aimed at classifying schizophrenia diseases using different types of techniques such as MRI (Tyagi, Singh & Gore, 2022), genetics (DNA) (Yu et al., 2022), eye tracking (Lim, Mountstephens & Teo, 2022), facial features (Rahman et al., 2021, Wang & Wang, 2021), tracking handwriting (Rashid et al., 2020), speech (Lopez-Bernal et al., 2022), and schizophrenia EEG signal (Krishnan et al., 2020). The last one is the technique used to classify schizophrenia. Since EEG signals are complicated nonlinear dynamic signals, it might be difficult to isolate their constituent parts precisely. In our study, we confirmed the concordance of brain states within different diagnostic features and across three different window sizes, which is a crucial step in linking brain states and capturing the differences inside functional brain activities.

This section summarizes and discusses various work scenarios that have been done and compares article results for classifying schizophrenic patients and healthy controls using the same dataset and different datasets. Also, this part compares state-of-the-art automated identification methods for SZ EEG data. Table 19 shows the most researchers use a single dataset to classify EEG signals with SZ, as well as present a comparison of article results classifying schizophrenic patients and healthy controls using different datasets using different machine-learning techniques for EEG signals. Selection feature extraction playing an essential role in improving classification accuracy, we proposed a method that combined the most important feature methods with a bandpass filter. That’s why our results outperformed other researchers and they could not get higher results even when using a bandpass filter. Using a bandpass filter in combination with various preprocessing approaches, the methods by Kim, Lee & Lee (2020), Azizi, Hier & Wunsch (2021), Kim et al. (2021), Keihani et al. (2022), fall short of achieving the remarkable accuracy seen by Najafzadeh et al. (2021), Aydemir et al. (2022), Agarwal & Singhal (2023).

10.7717/peerj-cs.2170/table-19 Table 19 Comparison of the sensitivity (Sen)/specificity (Spec)/ accuracy (Acc) values as a result of our proposed method with articles using various datasets.

Study	Year	Electrodes no.	Classes	Dataset	Applied algorithms	Acc%	Spec	Sen	
Patients	Control	
de Miras et al. (2023)	2023	31	2	9♂, 20♀	13♂, 7♀	SVM	89	90	63	
Zhang (2019)	2019	64	2	49	32	RF	81	NA	NA	
Li et al. (2019)	2019	16	2	37♂, 10♀	14♂, 11♀	SVM	90.48	91.30	89.47	
Winterburn et al. (2019)	2019	NA	2	31♂, 19♀	32♂, 18♀	Non-linear SVM	73.5	56.3	62.5	
	2020	8	2	48	24	RF	68	NA	NA	
	2022	NA	2	158	76	Ensemble	87	65	98	
	2022	64	2	41♂, 8♀	67♂, 14♀	HDSS	92.93	91.06	97.15	
Baradits, Bitter & Czobor (2020)	2020	256	2	33♂, 37♀	47♂, 28♀	SVM	82.7	81.43	82.67	
	2023	32	2	310	205	XGB	94	NA	NA	
Zandbagleh et al. (2022)	2022	64	2	13	11	SVM	89.21	90.3	88.2	
Ciprian et al. (2021)	2021	20	2	37♂, 25♀	38♂, 32♀	KNN	96.92	98.57	95	
Phang et al. (2019b)	2019	16	2	45	39	MDC-CNN	93.06	93.33	93.33	
Sun et al. (2021)	2021	64	2	36♂, 18♀	31♂, 24♀	Hybrid DNN	99.22	NA	NA	
Guo et al. (2021)	2021	64	2	49	32	CNN	92	NA	NA	
Bretones et al. (2023)	2023	32	2	215♂, 97♀	176♂, 144♀	RBF	93.40	NA	NA	
Present	2024	19	2	7♂, 7♀	7♂, 7♀	SVM	99.9	99.9	99.9	

Next, Table 20 displays the outcomes of the articles that classified schizophrenia disorder using the same dataset. Various factors affected the final results of classification, such as the number of electrodes, size of the sample rate, number of subjects, and many others. Thus, the results we observed in this research related to the features and characteristics that were used and to the normalization techniques that were applied to the extracted features. Based on our results, our proposed technique can classify healthy and schizophrenic subjects and show clear differences in their brain activity based on power spectrum and topography images. Thus, our method showed outperformance when compared to these articles.

10.7717/peerj-cs.2170/table-20 Table 20 Comparison of the sensitivity (Sen)/specificity (Spec)/ accuracy (Acc) values as a result of our proposed method with articles using the same datasets.

Study	Year	Electrodes no.	Classes	Dataset	Applied algorithms	Acc%	Spec%	Sen%	
Patients	Control	
Akbari et al. (2021)	2021	19	2	7♂, 7♀	7♂, 7♀	KNN	94.8	95.2	94.3	
Jahmunah et al. (2019)	2019	SVM	92.91	NA	NA	
Prabhakar, Rajaguru & Lee (2020)	2020	AdaBoost	98.77	NA	NA	
Shalbaf, Bagherzadeh & Maghsoudi (2020)	2020	SVM	98.60	96.92	99.65	
Oh et al. (2019)	2019	SoftMax	98.07	98.17	97.32	
Shoeibi et al. (2021)	2021	Sigmoid	99.25	NA	NA	
Aslan & Akin (2020)	2022	SoftMax	99.5	NA	NA	
Das & Pachori (2021)	2021	SVM	98.9	98.8	99.1	
Krishnan et al. (2020)	2020	SVM	93	93.33	98	
Buettner et al. (2020)	2020	Random forest	96.77	NA	NA	
Chandran, Sreekumar & Subha (2021)	2021	SVM	99	NA	99	
Racz et al. (2020)	2020	Random forest	89.29	NA	NA	
Sharma & Acharya (2021)	2021	KNN	97.20	98.06	96.49	
Bagherzadeh, Shahabi & Shalbaf (2022)	2022	Boosted Trees	96.26	97.02	95.48	
Hassan, Hussain & Qaisar (2023)	2023	LR	98	97	99	
Gosala et al. (2023)	2023	DT	97.98	97.72	98.2	
Kumar et al. (2023)	2023	AdaBoost	99.36	100	98.8	
Agarwal & Singhal (2023)	2023	BT	96.12	96.99	95.20	
Ours	2024	SVM	99.9	99.9	99.9	

For time-series datasets, the augmentation includes techniques such as generating data, shuffling features, time warping, and adding white Gaussian noise to signal (SNR) to the original dataset. Among these, we used two different techniques: the SNR and the stretch method to increase sample size which finally led to improving training and testing classifiers. Then, a bandpass filter was applied to the augmented data and the sub-bands were used as input to feature extraction to obtain the hidden information between the signals to prepare it as input to the classifiers.

For the augmentation approach, first, according to which was calculated using the LogEn method with the four classifiers, and the results for the performance showed that LogEn outperformed the other feature indices with higher computational efficiency. Second, in terms of accuracy, the results were obtained by using the stretch method and the Log Energy Entropy features with all classifiers, ranged between 96 to 99, which is the highest results obtained. FFT was second in terms of accuracy, and SVM obtained the highest accuracy of 97%, while the lowest accuracy obtained by using QDA was 92%. For the rest classifiers, the results ranged from 92% to 96%, which is relatively close to the first method. The rest of the classifiers, the results obtained in all criteria were acceptable. From the affirmative results, we concluded the window size that obtained the highest results was 1 s.

The final implementation was to use f the MRMR features selection methods by selection (the eight and five best features). The highest results were also for the log energy entropy in both cases (five and eight features). The results ranged from 97.9 to 98.9 in all epoch sizes for the classifiers (SVM, KNN, EC) in the case of eight-features in all window sizes except the classifier QDA, the accuracy results obtained ranged from 94.8% to 98.9%. Next, the five-features results were less when using eight-features, as observed in window length 1 s. The results were 1% smaller in the classifiers (SVM, KNN). For the rest of the classifiers, the results were the same. It is noted that the results obtained using FFT in both cases (five- features and eight- features) were relatively close, as the difference was slight at 3% in Window lengths 1 and 2 s, while when using window length 5 s the ratio difference was 4%. For the rest of the features and classifiers used, the results are lower than those mentioned above. It is very noticeable that the best result was obtained by using window 5 s when dealing with the eight features, and this indicates the possibility of reducing the features to a certain percentage without affecting the accuracy considerably.

Noteworthy, in a recent study, by Haider et al. (2024), applied the same dataset used with the electrode reduction method (6-electrode) which in turn led to features reduction also, they used the penalized sequential dictionary learning (PSDL) technique to classify the SZ subjects and achieved 89.12%. Interestingly, our results indicated that our MRMR features selection methods (five and eight features) have captured important information that is captured by the three used epochs, yielding higher overall accuracy rates. Eventually, the abovementioned results, the LogEn features calculated with SVM ns EC classifiers recorded as the best method for classifying schizophrenic patients. This indicates that the proposed method was better according to all the criteria by which it was measured.

The proposed SVM in high-dimensional spaces is very effective, and for the function of decision, it uses a subset of training points, which makes it efficient in memory. In contrast, the LogEn technique gives the most accurate characteristics for EEG classification with an absolute error as low as 0.01. LogEn values are signal features that characterize the degree of EEG complexity. Moreover, LogEn determines the optimum number of hidden neurons in hidden layers. LogEn allows us to identify seizure activity from seizure-free epileptic activity with excellent accuracy, even with few characteristics. Therefore, combining SVM and LogEn could be a reason for significant performance improvement. The results show that classifiers alone are unsuccessful at recognizing SZ. However, integrating SVM and log energy entropy features dramatically enhanced SZ detection performance. As shown in Table 11, our proposed architecture has maximum classification accuracy when LogEn values are used as features.

Our method can support the clinical auxiliary diagnosis by quick diagnosis: by examining EEG rhythms, which could point to abnormalities connected to the disorder, such methods may aid in confirming the diagnosis of schizophrenia with higher accuracy. A Scientific Study: gaining insight into the neurological basis of schizophrenia aids current scientific endeavors. Physicians may discover more about the processes behind the illness and recognize brainwaves. Due to the high accuracy achieved, it is possible to integrate this method into a decision support system that will have a minimum error rate.

In this study, some limitations are listed below. The data set is open access instead of a collected private dataset or one gained from cohort studies. Even though the number of subjects was low, we used the epoching approach that enabled us to increase the size of the input data for machine learning.

The dataset in this article aimed to identify the condition rather than to assess its degree of severity. Thus, it does not provide the stage of schizophrenia (prodromal, active, and residual).

Conclusion

A person’s life and behavior are affected according to the changing electrical activity of the brain, which the EEG can observe. It may be claimed that a healthy brain functions more actively than a brain affected by schizophrenia.

In this study, we proposed a method to classify schizophrenia based on using an EEG signal dataset that contains 28 subjects: 14 suffering from schizophrenia and 14 healthy controls. Due to the variations of the EEG signal, we applied a band-pass filler to decompose the EEG signal into five sub-bands. Next, we implemented six feature extraction methodologies, we applied the first four (ApEn, LogEn, ShnEn, and kurtosis) to the band-pass filter, and then we used FFT and ApEn again without band-pass filters to compare the impact of the filter on the results.

Normalization was used for all features deduced to be on the same scale using the L2 normalization technique. Consequently, we fed the features to the SVM, EC, KNN, and QDA classifiers. We implemented these classifiers using three window sizes (1, 2, and 5 s). Based on the obtained results, we can see a significant variation in the features of EEG signals between healthy subjects and schizophrenia patients, as presented in the tables above. Since, there are many factors affect why the LogEn achieved the most beneficial feature results, which are: 1) Dimensional reduction: by applying the energy logarithm of the EEG signal and then computing the Entropy, this reduces the features space, which is reflected in subsequent efficient computation. 2) artifact elimination: the logarithm aids in eliminating the noise in the signals by emphasizing the magnitude of EEG signals. 3) Sensitivity of the features: it can sense the distribution frequency bands, which is very valuable with EEG signals, due to using a bandpass filter to divide the signal into sub-bands (delta, theta, alpha, beta, and gamma). 4) Non-linear Dynamics: normally, EEG signals are non-linear and complex; thus, the LogEn deals with the signals dynamically due to its ability to capture features from signals with complexity and irregularity. Furthermore, the LogEn strength is the ability of the probability distribution, which calculates the power spectral density. Then the calculated values are normalized to the total signal energy, and the captured values are used as probabilities. In our study, LogEn allows us to identify SZ with excellent accuracy, even with few characteristics. The LogEn technique gives the most accurate characteristics for EEG classification with a minimum error as low as 0.01. From all classifiers, integrating SVM and EC with LogEn was the most effective with high-dimensional spaces, and for the function of decision, which makes it efficient in memory. Thus, the highest results were achieved using features extracted by LogEn with SVM and EC classifiers.

Based on the results obtained in this study, we conclude: Using these feature extraction methods with this type of signal can reach high results.

Window epoch sizes could enhance classification precision.

The outcomes fluctuate depending on the window size. As a result, the signal decomposition window-size epoch is important for identifying tiny changes in the EEG recording.

For future work, we will apply wavelet transform to convert the signal to time-frequency images and then apply these images to deep convolutional neural networks.

Supplemental Information

10.7717/peerj-cs.2170/supp-1 Supplemental Information 1 One-Second Epoch Size Confusion Matrix Results.

10.7717/peerj-cs.2170/supp-2 Supplemental Information 2 One-Second Epoch Size Confusion Matrix Results with SNR.

10.7717/peerj-cs.2170/supp-3 Supplemental Information 3 One-Second Epoch Size Confusion Matrix Results with Stretch.

10.7717/peerj-cs.2170/supp-4 Supplemental Information 4 One-Second Epoch Size Confusion Matrix Results with 5 electrodes.

10.7717/peerj-cs.2170/supp-5 Supplemental Information 5 One-Second Epoch Size Confusion Matrix Results with 8 electrodes.

10.7717/peerj-cs.2170/supp-6 Supplemental Information 6 Two-Second Epoch Size Confusion Matrix Results.

10.7717/peerj-cs.2170/supp-7 Supplemental Information 7 Two-Second Epoch Size Confusion Matrix Results with SNR.

10.7717/peerj-cs.2170/supp-8 Supplemental Information 8 Two-Second Epoch Size Confusion Matrix Results with Stretch.

10.7717/peerj-cs.2170/supp-9 Supplemental Information 9 Two-Second Epoch Size Confusion Matrix Results with 5 electrodes.

10.7717/peerj-cs.2170/supp-10 Supplemental Information 10 Two-Second Epoch Size Confusion Matrix Results with 8 electrodes.

10.7717/peerj-cs.2170/supp-11 Supplemental Information 11 Five-Second Epoch Size Confusion Matrix Results.

10.7717/peerj-cs.2170/supp-12 Supplemental Information 12 Five-Second Epoch Size Confusion Matrix Results with SNR augmented method.

10.7717/peerj-cs.2170/supp-13 Supplemental Information 13 Five-Second Epoch Size Confusion Matrix Results with Stretch signals.

10.7717/peerj-cs.2170/supp-14 Supplemental Information 14 Five-Second Epoch Size Confusion Matrix Results with 5 electrodes.

Five-Second Epoch Size Confusion Matrix Results with 5 electrodes

10.7717/peerj-cs.2170/supp-15 Supplemental Information 15 Five-Second Epoch Size Confusion Matrix Results with 8 electrodes.

10.7717/peerj-cs.2170/supp-16 Supplemental Information 16 One-second epoch size receiver operating characteristics curves (ROC) for the four classifiers with six features extraction types.

10.7717/peerj-cs.2170/supp-17 Supplemental Information 17 One-second epoch size receiver operating characteristics curves (ROC) with SNR for the four classifiers with six features extraction types.

10.7717/peerj-cs.2170/supp-18 Supplemental Information 18 One-second epoch size receiver operating characteristics curves (ROC) with Stretch for the four classifiers.

10.7717/peerj-cs.2170/supp-19 Supplemental Information 19 One-second epoch size receiver operating characteristics curves (ROC) with 5 electrodes for the four classifiers with six features extraction types.

10.7717/peerj-cs.2170/supp-20 Supplemental Information 20 One-second epoch size receiver operating characteristics curves (ROC) with 8 electrodes for the four classifiers with six features extraction types.

10.7717/peerj-cs.2170/supp-21 Supplemental Information 21 Two-second epoch size receiver operating characteristics curves (ROC) for the four classifiers.

10.7717/peerj-cs.2170/supp-22 Supplemental Information 22 Two-second epoch size receiver operating characteristics curves (ROC) with SNR for the four classifiers.

10.7717/peerj-cs.2170/supp-23 Supplemental Information 23 Two-second epoch size receiver operating characteristics curves (ROC) with Stretch for the four classifiers.

10.7717/peerj-cs.2170/supp-24 Supplemental Information 24 Two-second epoch size receiver operating characteristics curves (ROC) for the 5 electrodes with four classifiers.

10.7717/peerj-cs.2170/supp-25 Supplemental Information 25 Two-second epoch size receiver operating characteristics curves (ROC) for the 8 electrodes with four classifiers.

10.7717/peerj-cs.2170/supp-26 Supplemental Information 26 Five-second epoch size receiver operating characteristics curves (ROC) for the four classifiers with six features extraction types.

10.7717/peerj-cs.2170/supp-27 Supplemental Information 27 Five-second epoch size receiver operating characteristics curves (ROC) with SNR for the four classifiers with six features extraction types.

10.7717/peerj-cs.2170/supp-28 Supplemental Information 28 Five-second epoch size receiver operating characteristics curves (ROC) with Stretch for the four classifiers with six features extraction types.

10.7717/peerj-cs.2170/supp-29 Supplemental Information 29 Five-second epoch size receiver operating characteristics curves (ROC) with 5 Electrodes for the four classifiers with six features extraction types.

10.7717/peerj-cs.2170/supp-30 Supplemental Information 30 Five-second epoch size receiver operating characteristics curves (ROC) with 8 Electrodes for the four classifiers with six features extraction types.

Additional Information and Declarations

Competing Interests

Author Contributions

Data Availability

Ivan Miguel Pires and Paulo Jorge Coelho are Academic Editors for PeerJ Computer Science.

Athar Alazzawı conceived and designed the experiments, performed the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Saif Aljumaili conceived and designed the experiments, performed the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Adil Deniz Duru conceived and designed the experiments, performed the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Osman Nuri Uçan conceived and designed the experiments, performed the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Oğuz Bayat conceived and designed the experiments, performed the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Paulo Jorge Coelho conceived and designed the experiments, performed the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Ivan Miguel Pires conceived and designed the experiments, performed the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

The following information was supplied regarding data availability:

The EEG in schizophrenia dataset is available at: Olejarczyk, Elzbieta; Jernajczyk, Wojciech, 2017, “EEG in schizophrenia”, https://doi.org/10.18150/repod.0107441, RepOD, V1.

The code is available at Zenodo: Alazzawi, A., Aljumaili, S., Duru, A. D., Uçan, O. N., Bayat, O., Coelho, P. J., & Pires, I. M. (2024). Schizophrenia diagnosis based on diverse epoch size using EEG signal on resting-state. Zenodo. https://doi.org/10.5281/zenodo.11075184.
==== Refs
References

Agarwal & Singhal (2023) Agarwal M Singhal A Fusion of pattern-based and statistical features for Schizophrenia detection from EEG signals Medical Engineering & Physics 2023 112 1 103949 10.1016/j.medengphy.2023.103949 36842772
Akbari et al. (2021) Akbari H Ghofrani S Zakalvand P Sadiq MT Schizophrenia recognition based on the phase space dynamic of EEG signals and graphical features Biomedical Signal Processing and Control 2021 69 102917 10.1016/j.bspc.2021.102917
Aksöz et al. (2022) Aksöz A Akyüz D Bayir F Yildiz NC Orhanbulucu F Latifoğlu F Analysis and classification of schizophrenia using event related potential signals Computer Science 2022 6 3 32 36 10.53070/bbd.1173093
Al-Azzawi (2021) Al-Azzawi A Fourier transform based epileptic seizure features classification using scalp electrical measurements using KNN and SVM 2021 Lisansüstü Eğitim İstanbul Altınbaş Üniversitesi
Al-azzawi et al. (2022) Al-azzawi AHAl Al-jumaili S Ibrahim AA Duru AD Classification of epileptic seizure features from scalp electrical measurements using KNN and SVM based on Fourier Transform 2022 AIP Conference Proceedings AIP Publishing LLC
Al-Jumaili et al. (2021) Al-Jumaili S Al-Azzawi A Duru AD Ibrahim AA Covid-19 X-ray image classification using SVM based on Local Binary Pattern 2021 2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) Piscataway IEEE
Al-Jumaili et al. (2023) Al-Jumaili S Al-Azzawi A Uçan ON Duru AD Classification of the level of Alzheimer’s disease using anatomical magnetic resonance images based on a novel deep learning structure Diagnosis of Neurological Disorders Based on Deep Learning Techniques 2023 United States CRC Press 29 46
Al-Jumaili, Duru & Uçan (2021) Al-Jumaili S Duru AD Uçan ON Covid-19 Ultrasound image classification using SVM based on kernels deduced from Convolutional neural network 2021 2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) Piscataway IEEE
ArivuSelvan & Moorthy (2020) ArivuSelvan K Moorthy ES Analysing thalamus and its sub nuclei in MRI brain image to distinguish schizophrenia subjects using back propagation neural network International Journal of Internet Technology and Secured Transactions 2020 10 1–2 196 210 10.1504/IJITST.2020.104571
Aslan & Akin (2020) Aslan Z Akin M Automatic detection of schizophrenia by applying deep learning over spectrogram images of EEG signals Traitement du Signal 2020 37 2 235 244 10.18280/ts.370209
Aydemir et al. (2022) Aydemir E Dogan S Baygin M Ooi CP Barua PD Tuncer T Acharya UR CGP17Pat: automated schizophrenia detection based on a cyclic group of prime order patterns using EEG signals Healthcare, MDPI 2022 10 4 643 10.3390/healthcare10040643
Ayesa-Arriola et al. (2020) Ayesa-Arriola R de la Foz VOG Setién-Suero E Ramírez-Bonilla ML Suárez-Pinilla P Son JMV Vázquez-Bourgon J Juncal-Ruiz M Gómez-Revuelta M Tordesillas-Gutiérrez D Crespo-Facorro B Understanding sex differences in long-term outcomes after a first episode of psychosis NPJ Schizophrenia 2020 6 1 33 10.1038/s41537-020-00120-5 33219222
Azizi, Hier & Wunsch (2021) Azizi S Hier DB Wunsch DC Schizophrenia classification using resting state EEG functional connectivity: source level outperforms sensor level 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Piscataway IEEE
Baandrup (2020) Baandrup L Polypharmacy in schizophrenia Basic & Clinical Pharmacology & Toxicology 2020 126 3 183 192 10.1111/bcpt.13384 31908124
Bagherzadeh, Shahabi & Shalbaf (2022) Bagherzadeh S Shahabi MS Shalbaf A Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal Computers in Biology and Medicine 2022 146 105570 10.1016/j.compbiomed.2022.105570 35504218
Baradits, Bitter & Czobor (2020) Baradits M Bitter I Czobor P Multivariate patterns of EEG microstate parameters and their role in the discrimination of patients with schizophrenia from healthy controls Psychiatry Research 2020 288 112938 10.1016/j.psychres.2020.112938 32315875
Barker & Vigod (2023) Barker LC Vigod SN Reproductive health among those with schizophrenia spectrum disorders: an overview of considerations related to the premenopausal period, pregnancy and postpartum, and the menopausal transition, with a focus on recent findings Current Psychiatry Reports 2023 25 12 793 802 10.1007/s11920-023-01472-2 37906350
Baygin et al. (2023) Baygin M Barua PD Chakraborty S Tuncer I Dogan S Palmer E Tuncer T Kamath AP Ciaccio EJ Acharya UR CCPNet136: automated detection of schizophrenia using carbon chain pattern and iterative TQWT technique with EEG signals Physiological Measurement 2023 44 035008 10.1088/1361-6579/acb03c
Bretones et al. (2023) Bretones CS Parra CR Cascón J Borja AL Sotos. JM Automatic identification of schizophrenia employing EEG records analyzed with deep learning algorithms Schizophrenia Research 2023 261 36 46 10.1016/j.schres.2023.09.010 37690170
Buettner et al. (2019) Buettner R Hirschmiller M Schlosser K Rössle M Fernandes M Timm IJ High-performance exclusion of schizophrenia using a novel machine learning method on EEG data 2019 2019 IEEE International Conference on E-Health Networking, Application & Services (HealthCom) IEEE 1 6
Buettner et al. (2020) Buettner R Beil D Scholtz S Djemai A Development of a machine learning based algorithm to accurately detect schizophrenia based on one-minute EEG recordings Proceedings of the 53rd Hawaii International Conference on System Sciences 2020
Chandran, Sreekumar & Subha (2021) Chandran AN Sreekumar K Subha D EEG-based automated detection of schizophrenia using long short-term memory (LSTM) network 2021 Advances in Machine Learning and Computational Intelligence: Proceedings of ICMLCI 2019 Springer 229 236
Chatterjee et al. (2020) Chatterjee I Kumar V Rana B Agarwal M Kumar N Impact of ageing on the brain regions of the schizophrenia patients: an fMRI study using evolutionary approach Multimedia Tools and Applications 2020 79 33–34 24757 24779 10.1007/s11042-020-09183-z
Chicco & Jurman (2023) Chicco D Jurman G The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification BioData Mining 2023 16 1 1 23 10.1186/s13040-023-00322-4 36639651
Chin et al. (2018) Chin R You AX Meng F Zhou J Sim K Recognition of schizophrenia with regularized support vector machine and sequential region of interest selection using structural magnetic resonance imaging Scientific Reports 2018 8 1 13858 10.1038/s41598-018-32290-9 30218016
Ciprian et al. (2021) Ciprian C Masychev K Ravan M Manimaran A Deshmukh A Diagnosing schizophrenia using effective connectivity of resting-state EEG data Algorithms 2021 14 5 139 10.3390/a14050139
Das & Pachori (2021) Das K Pachori RB Schizophrenia detection technique using multivariate iterative filtering and multichannel EEG signals Biomedical Signal Processing and Control 2021 67 102525 10.1016/j.bspc.2021.102525
De Rosa et al. (2022) De Rosa A Fontana A Nuzzo T Garofalo M Maio AD Punzo D Copetti M Bertolino A Errico F Rampino A de Bartolomeis A Usiello A Machine Learning algorithm unveils glutamatergic alterations in the post-mortem schizophrenia brain Schizophrenia 2022 8 1 8 10.1038/s41537-022-00231-1 35217646
de Miras et al. (2023) de Miras JR Ibáñez-Molina AJ Soriano MF Iglesias-Parro S Schizophrenia classification using machine learning on resting state EEG signal Biomedical Signal Processing and Control 2023 79 17 104233 10.1016/j.bspc.2022.104233
Devia et al. (2019) Devia C Mayol-Troncoso R Parrini J Orellana G Ruiz A Maldonado PE Egana JI EEG classification during scene free-viewing for schizophrenia detection IEEE Transactions on Neural Systems and Rehabilitation Engineering 2019 27 6 1193 1199 10.1109/TNSRE.2019.2913799 31034418
Ding & Peng (2005) Ding C Peng H Minimum redundancy feature selection from microarray gene expression data Journal of Bioinformatics and Computational Biology 2005 3 02 185 205 10.1142/S0219720005001004 15852500
Esposito et al. (2024) Esposito CM De Cagna F Caldiroli A Capuzzi E Ceresa A Paolo MDI Auxilia AM Capellazzi M Tagliabue I Cirella L Gender differences in clinical and biochemical parameters among patients hospitalized for schizophrenia: towards precision medicine European Archives of Psychiatry and Clinical Neuroscience 2024 274 1093 1103 10.1007/s00406-023-01644-4 37436457
Ferdowsi et al. (2024) Ferdowsi M Liu H Kwan BH Goh CH Automated detection of Alzheimer’s disease using EEG signal processing and machine learning Artificial Intelligence Enabled Signal Processing Based Models for Neural Information Processing 2024 US CRC Press 118 135
Gogos et al. (2019) Gogos A Ney LJ Seymour N Van Rheenen TE Felmingham KL Sex differences in schizophrenia, bipolar disorder, and post-traumatic stress disorder: are gonadal hormones the link? British Journal of Pharmacology 2019 176 21 4119 4135 10.1111/bph.14584 30658014
González-Rodríguez et al. (2023) González-Rodríguez A Natividad M Seeman MV Paolini JP Balagué A Román E Izquierdo E Pérez A Vallet A Salvador M Schizophrenia: a review of social risk factors that affect women Behavioral Sciences 2023 13 7 581 10.3390/bs13070581 37504028
Gosala et al. (2023) Gosala B Kapgate PD Jain P Chaurasia RN Gupta M Wavelet transforms for feature engineering in EEG data processing: an application on Schizophrenia Biomedical Signal Processing and Control 2023 85 2 104811 10.1016/j.bspc.2023.104811
Guo et al. (2021) Guo Z Wu L Li Y Li B Deep neural network classification of EEG data in schizophrenia 2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) 2021 IEEE 1322 1327
Haider et al. (2024) Haider U Hanif M Rashid A Qaisar SM Subasi A EEG-based schizophrenia classification using penalized sequential dictionary learning in the context of mobile healthcare Biomedical Signal Processing and Control 2024 90 3 105856 10.1016/j.bspc.2023.105856
Hartini & Rustam (2021) Hartini S Rustam Z Schizophrenia classification using fuzzy kernel c-means Journal of Physics: Conference Series, IOP Publishing 2021 1752 012039 10.1088/1742-6596/1752/1/012039
Hassan, Hussain & Qaisar (2023) Hassan F Hussain SF Qaisar SM Fusion of multivariate EEG signals for schizophrenia detection using CNN and machine learning techniques Information Fusion 2023 92 14 466 478 10.1016/j.inffus.2022.12.019
Hoffman et al. (2022) Hoffman GE Ma Y Montgomery KS Bendl J Jaiswal MK Kozlenkov A Peters MA Dracheva S Fullard JF Chess A Sex differences in the human brain transcriptome of cases with schizophrenia Biological Psychiatry 2022 91 1 92 101 10.1016/j.biopsych.2021.03.020 34154796
Huang et al. (2019) Huang Y Wang Y Wang H Liu Z Yu X Yan J Yu Y Kou C Xu X Lu J Prevalence of mental disorders in China: a cross-sectional epidemiological study The Lancet Psychiatry 2019 6 3 211 224 10.1016/S2215-0366(18)30511-X 30792114
Iglesias-Tejedor et al. (2022) Iglesias-Tejedor M Díez Á Llorca-Bofí V Núñez P Castaño-Díaz C Bote B Segarra R Sanz-Fuentenebro J Molina V Relation between EEG resting-state power and modulation of P300 task-related activity in theta band in schizophrenia Progress in Neuro-Psychopharmacology and Biological Psychiatry 2022 116 6 110541 10.1016/j.pnpbp.2022.110541 35218880
Jahmunah et al. (2019) Jahmunah V Oh SL Rajinikanth V Ciaccio EJ Cheong KH Arunkumar N Acharya UR Automated detection of schizophrenia using nonlinear signal processing methods Artificial Intelligence in Medicine 2019 100 101698 10.1016/j.artmed.2019.07.006 31607349
Jain et al. (2022) Jain S Thakkar H Singh BK Tikka SK Singh LK Electroencephalograph (EEG) signal analysis for the Detection of Schizophrenia using Empirical Wavelet Transform 2022 2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T) Piscataway IEEE
Jauhar, Johnstone & McKenna (2022) Jauhar S Johnstone M McKenna PJ Schizophrenia Lancet (London, England) 2022 399 10323 473 486 10.1016/S0140-6736(21)01730-X 35093231
Keihani et al. (2022) Keihani A Sajadi SS Hasani M Ferrarelli F Bayesian optimization of machine learning classification of resting-state EEG microstates in schizophrenia: a proof-of-concept preliminary study based on secondary analysis Brain Sciences 2022 12 11 1497 10.3390/brainsci12111497 36358423
Khare, Bajaj & Acharya (2023) Khare SK Bajaj V Acharya UR SchizoNET: a robust and accurate Margenau-Hill time-frequency distribution based deep neural network model for schizophrenia detection using EEG signals Physiological Measurement 2023 44 3 035005 10.1088/1361-6579/acbc06
Khare et al. (2020) Khare SK Bajaj V Siuly S Sinha G Classification of schizophrenia patients through empirical wavelet transformation using electroencephalogram signals Modelling and Analysis of Active Biopotential Signals in Healthcare 2020 1 1 26 10.1088/978-0-7503-3279-8
Kim et al. (2021) Kim K Duc NT Choi M Lee B EEG microstate features for schizophrenia classification PLOS ONE 2021 16 5 e0251842 10.1371/journal.pone.0251842 33989352
Kim et al. (2024) Kim S-K Kim H Kim SH Kim JB Kim L Electroencephalography-based classification of Alzheimer’s disease spectrum during computer-based cognitive testing Scientific Reports 2024 14 1 1 16 10.1038/s41598-024-55656-8 38167627
Kim, Lee & Lee (2020) Kim J-Y Lee HS Lee S-H EEG source network for the diagnosis of schizophrenia and the identification of subtypes based on symptom severity—a machine learning approach Journal of Clinical Medicine 2020 9 12 3934 10.3390/jcm9123934 33291657
Ko & Yang (2022) Ko D-W Yang J-J EEG-Based schizophrenia diagnosis through time series image conversion and deep learning Electronics 2022 11 14 2265 10.3390/electronics11142265
Krishnan et al. (2020) Krishnan PT Raj ANJ Balasubramanian P Chen Y Schizophrenia detection using MultivariateEmpirical Mode Decomposition and entropy measures from multichannel EEG signal Biocybernetics and Biomedical Engineering 2020 40 3 1124 1139 10.1016/j.bbe.2020.05.008
Kumar et al. (2023) Kumar TS Rajesh KN Maheswari S Kanhangad V Acharya UR Automated schizophrenia detection using local descriptors with EEG signals Engineering Applications of Artificial Intelligence 2023 117 105602 10.1016/j.engappai.2022.105602
Li et al. (2019) Li F Wang J Liao Y Yi C Jiang Y Si Y Peng W Yao D Zhang Y Dong W Xu P Differentiation of schizophrenia by combining the spatial EEG brain network patterns of rest and task P300 IEEE Transactions on Neural Systems and Rehabilitation Engineering 2019 27 4 594 602 10.1109/TNSRE.2019.2900725 30802869
Li et al. (2023) Li M Chen Y Hu X Wang S The preferences for the telemedicine and standard health care services from the perspective of the patients with schizophrenia BMC Psychiatry 2023 23 1 361 10.1186/s12888-023-04885-8 37226168
Lillo, Mora & Lucero (2022) Lillo E Mora M Lucero B Automated diagnosis of schizophrenia using EEG microstates and Deep Convolutional Neural Network Expert Systems with Applications 2022 209 118236 10.1016/j.eswa.2022.118236
Lim, Mountstephens & Teo (2022) Lim JZ Mountstephens J Teo J Eye-tracking feature extraction for biometric machine learning Frontiers in Neurorobotics 2022 15 796895 10.3389/fnbot.2021.796895 35177973
Lopez-Bernal et al. (2022) Lopez-Bernal D Balderas D Ponce P Molina A A state-of-the-art review of EEG-based imagined speech decoding Frontiers in Human Neuroscience 2022 16 867281 10.3389/fnhum.2022.867281 35558735
Mitra et al. (2015) Mitra S Nizamie SH Goyal N Tikka SK Evaluation of resting state gamma power as a response marker in schizophrenia Psychiatry and Clinical Neurosciences 2015 69 10 630 639 10.1111/pcn.12301 25854748
Nagy et al. (2023) Nagy Á Dombi J Fülep MP Rudics E Hompoth EA Szabó Z Dér A Búzás A Viharos ZJ Hoang AT Maczák B Vadai G Gingl Z László S Bilicki V Szendi I The actigraphy-based identification of premorbid latent liability of schizophrenia and bipolar disorder Sensors 2023 23 2 958 10.3390/s23020958 36679755
Najafzadeh et al. (2021) Najafzadeh H Esmaeili M Farhang S Sarbaz Y Rasta SH Automatic classification of schizophrenia patients using resting-state EEG signals Physical and Engineering Sciences in Medicine 2021 44 3 855 870 10.1007/s13246-021-01038-7 34370274
Nour, Senturk & Polat (2024) Nour M Senturk U Polat K A novel hybrid model in the diagnosis and classification of Alzheimer’s disease using EEG signals: deep ensemble learning (DEL) approach Biomedical Signal Processing and Control 2024 89 1 105751 10.1016/j.bspc.2023.105751
Oh et al. (2019) Oh SL Vicnesh J Ciaccio EJ Yuvaraj R Acharya UR Deep convolutional neural network model for automated diagnosis of schizophrenia using EEG signals Applied Sciences 2019 9 14 2870 10.3390/app9142870
Olejarczyk & Wojciech (2017) Olejarczyk E Wojciech J EEG in schizophrenia 2017 2022 https://repod.icm.edu.pl/dataset.xhtml?persistentId=doi:10.18150/repod.0107441
Park et al. (2020) Park YW Choi D Lee J Ahn SS Lee S-K Lee S-K Bang M Differentiating patients with schizophrenia from healthy controls by hippocampal subfields using radiomics Schizophrenia Research 2020 223 337 344 10.1016/j.schres.2020.09.009 32988740
Petric et al. (2024) Petric PS Ifteni P Miron AA Sechel G Teodorescu A Brain abnormalities in Schizophrenia: a comparative imagistic study Medicina 2024 60 4 564 10.3390/medicina60040564 38674210
Phang et al. (2019a) Phang CR Noman F Hussain H Ting CM Ombao H A multi-domain connectome convolutional neural network for identifying schizophrenia from EEG connectivity patterns IEEE Journal of Biomedical and Health Informatics 2019a 24 5 1333 1343 10.1109/JBHI.2019.2941222 31536026
Phang et al. (2019b) Phang C-R Ting C-M Noman F Ombao H Classification of EEG-based brain connectivity networks in schizophrenia using a multi-domain connectome convolutional neural network ArXiv preprint 2019b 10.48550/arXiv.1903.08858
Prabhakar, Rajaguru & Lee (2020) Prabhakar SK Rajaguru H Lee SW A framework for schizophrenia EEG signal classification with nature inspired optimization algorithms IEEE Access 2020 8 39875 39897 10.1109/ACCESS.2020.2975848
Prasanna, George & Subathra (2024) Prasanna J George ST Subathra M Classification of gait signals for detection of neurodegenerative diseases using log energy entropy and ANN classifier Computational Techniques in Neuroscience 2024 New York CRC Press 101 112
Racz et al. (2020) Racz FS Stylianou O Mukli P Eke A Multifractal and entropy-based analysis of delta band neural activity reveals altered functional connectivity dynamics in schizophrenia Frontiers in Systems Neuroscience 2020 14 49 10.3389/fnsys.2020.00049 32792917
Rahman et al. (2021) Rahman MM Sarkar AK Hossain MA Hossain MS Islam MR Hossain MB Quinn JM Moni MA Recognition of human emotions using EEG signals: a review Computers in Biology and Medicine 2021 136 2 104696 10.1016/j.compbiomed.2021.104696 34388471
Rajesh & Kumar (2021) Rajesh KN Kumar TS Schizophrenia Detection in Adolescents from EEG Signals using Symmetrically weighted Local Binary Patterns 2021 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) IEEE 963 966
Ranjan, Sahana & Bhandari (2024) Ranjan R Sahana BC Bhandari AK Deep learning models for diagnosis of schizophrenia using EEG signals: emerging trends, challenges, and prospects Archives of Computational Methods in Engineering 2024 31 1 40 10.1007/s11831-023-10047-6
Ranjbari et al. (2021) Ranjbari S Khatibi T Vosough Dizaji A Sajadi H Totonchi M Ghaffari F CNFE-SE: a novel approach combining complex network-based feature engineering and stacked ensemble to predict the success of intrauterine insemination and ranking the features BMC Medical Informatics and Decision Making 2021 21 1 1 29 10.1186/s12911-020-01362-0 33388057
Rashid et al. (2020) Rashid M Sulaiman N Abdul Majeed APP Musa RM Ab. Nasir AF Bari BS Khatun S Current status, challenges, and possible solutions of EEG-based brain-computer interface: a comprehensive review Frontiers in Neurorobotics 2020 14 515104 10.3389/fnbot.2020.00025
Sadeghi et al. (2022) Sadeghi D Shoeibi A Ghassemi N Moridian P Khadem A Alizadehsani R Teshnehlab M Gorriz JM Khozeimeh F Zhang YD An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: methods, challenges, and future works Computers in Biology and Medicine 2022 146 2 105554 10.1016/j.compbiomed.2022.105554 35569333
Santos Febles et al. (2022) Santos Febles E Ortega Ontivero M Sosa Valdes M Sahli H Machine learning techniques for the diagnosis of schizophrenia based on event-related potentials Frontiers in Neuroinformatics 2022 16 893788 10.3389/fninf.2022.893788
Sazgar et al. (2019) Sazgar M Young MG Sazgar M Young MG Overview of EEG, electrode placement, and montages Absolute Epilepsy and EEG Rotation Review: Essentials for Trainees 2019 Cham Springer 117 125
Sedik, Marey & Mostafa (2023) Sedik A Marey M Mostafa H WFT-Fati-Dec: enhanced fatigue detection AI system based on wavelet denoising and fourier transform Applied Sciences 2023 13 5 2785 10.3390/app13052785
Shalbaf, Bagherzadeh & Maghsoudi (2020) Shalbaf A Bagherzadeh S Maghsoudi A Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals Physical and Engineering Sciences in Medicine 2020 43 1229 1239 10.1007/s13246-020-00925-9 32926393
Sharma & Acharya (2021) Sharma M Acharya UR Automated detection of schizophrenia using optimal wavelet-based l 1 norm features extracted from single-channel EEG Cognitive Neurodynamics 2021 15 661 674 10.1007/s11571-020-09655-w 34367367
Shoeibi et al. (2021) Shoeibi A Sadeghi D Moridian P Ghassemi N Heras J Alizadehsani R Khadem A Kong Y Nahavandi S Zhang Y-D Gorriz JM Automatic diagnosis of schizophrenia in EEG signals using CNN-LSTM models Frontiers in Neuroinformatics 2021 15 777977 10.3389/fninf.2021.777977 34899226
Siuly et al. (2020) Siuly S Khare SK Bajaj V Wang H Zhang Y A computerized method for automatic detection of schizophrenia using EEG signals IEEE Transactions on Neural Systems and Rehabilitation Engineering 2020 28 11 2390 2400 10.1109/TNSRE.2020.3022715 32897863
Sun et al. (2021) Sun J Cao R Zhou M Hussain W Wang B Xue J Xiang J A hybrid deep neural network for classification of schizophrenia using EEG Data Scientific Reports 2021 11 1 4706 10.1038/s41598-021-83350-6 33633134
Sutcubasi et al. (2019) Sutcubasi B Metin SZ Erguzel TT Metin B Tas C Arikan MK Tarhan N Anatomical connectivity changes in bipolar disorder and schizophrenia investigated using whole-brain tract-based spatial statistics and machine learning approaches Neural Computing and Applications 2019 31 4983 4992 10.1007/s00521-018-03992-y
The MathWorks, Inc. (2022) The MathWorks, Inc Kurtosis 2022 https://www.mathworks.com/help/stats/kurtosis.html#mw_e811e34c-665e-48c5-bef8-6a435f0c3f9f
Torres Naira & Del Alamo (2020) Torres Naira CA Del Alamo CJL Classification of people who suffer schizophrenia and healthy people by EEG signals using Deep Learning 2020 10 10 0101067 10.14569/IJACSA.2019.0101067
Tyagi, Singh & Gore (2022) Tyagi A Singh VP Gore MM Machine learning approaches for the detection of schizophrenia using structural mri 2022 International Conference on Advanced Network Technologies and Intelligent Computing Cham Springer
Vázquez, Maghsoudi & Mariño (2021) Vázquez MA Maghsoudi A Mariño IP An interpretable machine learning method for the detection of schizophrenia using EEG signals Frontiers in Systems Neuroscience 2021 15 652662 10.3389/fnsys.2021.652662 34122021
Vyškovský, Schwarz & Kašpárek (2019) Vyškovský R Schwarz D Kašpárek T Brain morphometry methods for feature extraction in random subspace ensemble neural network classification of first-episode schizophrenia Neural Computation 2019 31 5 897 918 10.1162/neco_a_01180 30883275
Wang & Wang (2021) Wang J Wang M Review of the emotional feature extraction and classification using EEG signals Cognitive Robotics 2021 1 1 29 40 10.1016/j.cogr.2021.04.001
WeiKoh et al. (2022) WeiKoh JE Rajinikanth V Vicnesh J Pham TH Oh SL Yeong CH Sankaranarayanan M Kamath A Bairy GM Barua PD Cheong KH Application of local configuration pattern for automated detection of schizophrenia with electroencephalogram signals Expert Systems 2022 41 5 e12957 10.1111/exsy.12957
Winterburn et al. (2019) Winterburn JL Voineskos AN Devenyi GA Plitman E de la Fuente-Sandoval C Bhagwat N Graff-Guerrero A Knight J Chakravarty MM Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning? A multi-method and multi-dataset study Schizophrenia Research 2019 214 3 10 10.1016/j.schres.2017.11.038 29274736
Yu et al. (2022) Yu H Florian T Calhoun V Ye DH Deep learning from imaging genetics for schizophrenia classification 2022 2022 IEEE International Conference on Image Processing (ICIP) Piscataway IEEE
Zandbagleh et al. (2022) Zandbagleh A Mirzakuchaki S Daliri MR Premkumar P Sanei S Classification of low and high schizotypy levels via evaluation of brain connectivity International Journal of Neural Systems 2022 32 4 2250013 10.1142/S0129065722500137 35236254
Zhang (2019) Zhang L EEG signals classification using machine learning for the identification and diagnosis of schizophrenia 2019 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) IEEE 4521 4524
Zhu et al. (2022) Zhu Y Nakatani H Yassin W Maikusa N Okada N Kunimatsu A Abe O Kuwabara H Yamasue H Kasai K Application of a machine learning algorithm for structural brain images in chronic schizophrenia to earlier clinical stages of psychosis and autism spectrum disorder: a multiprotocol imaging dataset study Schizophrenia Bulletin 2022 48 3 563 574 10.1093/schbul/sbac030 35352811
Zorkina et al. (2021) Zorkina Y Morozova A Abramova O Reznik A Kostyuk G Sex differences in social functioning of patients with schizophrenia depending on the age of onset and severity of the disease Early Intervention in Psychiatry 2021 15 5 1197 1209 10.1111/eip.13063 33040482
