
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39227730
71583
10.1038/s41598-024-71583-0
Article
Increased coherence predicts medical refractoriness in patients with temporal lobe epilepsy on monotherapy
Hwang Sungeun 1
Shin Youmin 23
Sunwoo Jun-Sang 4
Son Hyoshin 5
Lee Seung-Bo 6
Chu Kon 78
Jung Ki-Young 78
Lee Sang Kun 78
Kim Young-Gon younggon2.kim@gmail.com

2911
Park Kyung-Il ideopki@gmail.com

810
1 https://ror.org/053fp5c05 grid.255649.9 0000 0001 2171 7754 Department of Neurology, Ewha Womans University Mokdong Hospital, Seoul, Republic of Korea
2 https://ror.org/01z4nnt86 grid.412484.f 0000 0001 0302 820X Department of Transdisciplinary Medicine, Seoul National University Hospital, Seoul, Republic of Korea
3 https://ror.org/04h9pn542 grid.31501.36 0000 0004 0470 5905 Interdisciplinary Program in Bio-Engineering, Seoul National University, Seoul, Republic of Korea
4 https://ror.org/013e76m06 grid.415735.1 0000 0004 0621 4536 Department of Neurology, Kangbuk Samsung Hospital, Seoul, Republic of Korea
5 https://ror.org/01fpnj063 grid.411947.e 0000 0004 0470 4224 Department of Neurology, Catholic University of Korea, Seoul, Republic of Korea
6 https://ror.org/00tjv0s33 grid.412091.f 0000 0001 0669 3109 Department of Medical Informatics, Keimyung University School of Medicine, Daegu, Republic of Korea
7 https://ror.org/01z4nnt86 grid.412484.f 0000 0001 0302 820X Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea
8 https://ror.org/04h9pn542 grid.31501.36 0000 0004 0470 5905 Department of Neurology, Seoul National University College of Medicine, Seoul, Republic of Korea
9 https://ror.org/04h9pn542 grid.31501.36 0000 0004 0470 5905 Department of Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
10 https://ror.org/01z4nnt86 grid.412484.f 0000 0001 0302 820X Department of Neurology, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea
11 https://ror.org/01z4nnt86 grid.412484.f 0000 0001 0302 820X Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
4 9 2024
4 9 2024
2024
14 205303 7 2024
29 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Among patients with epilepsy, 30–40% experience recurrent seizures even after adequate antiseizure medications therapies, making them refractory. The early identification of refractory epilepsy is important to provide timely surgical treatment for these patients. In this study, we analyze interictal electroencephalography (EEG) data to predict drug refractoriness in patients with temporal lobe epilepsy (TLE) who were treated with monotherapy at the time of the first EEG acquisition. Various EEG features were extracted, including statistical measurements and interchannel coherence. Feature selection was performed to identify the optimal features, and classification was conducted using different classifiers. Functional connectivity and graph theory measurements were calculated to identify characteristics of refractory TLE. Among the 48 participants, 34 (70.8%) were responsive, while 14 (29.2%) were refractory over a mean follow-up duration of 38.5 months. Coherence feature within the gamma frequency band exhibited the most favorable performance. The light gradient boosting model, employing the mutual information filter-based feature selection method, demonstrated the highest performance (AUROC = 0.821). Compared to the responsive group, interchannel coherence displayed higher values in the refractory group. Interestingly, graph theory measurements using EEG coherence exhibited higher values in the refractory group than in the responsive group. Our study has demonstrated a promising method for the early identification of refractory TLE utilizing machine learning algorithms.

Keywords

Electroencephalography
Machine learning
Optimized feature selection
Prediction
Refractory epilepsy
Temporal lobe epilepsy
Subject terms

Biomarkers
Medical research
Neurology
http://dx.doi.org/10.13039/501100003710 Korea Health Industry Development Institute RS-2023-00265638 issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

Epilepsy is a neurological disease characterized by recurrent seizures1. The primary treatment modality for epilepsy is anti-seizure medication (ASM) and regular maintenance of ASM is required to minimize seizure recurrence, even in patients who experience infrequent seizures.

Numerous cohort studies have revealed that optimal ASM provides seizure freedom in 60–70% of patients with newly diagnosed epilepsy2. That is, the remaining 30–40% experience recurrent seizures even after adequate ASM therapy and are therefore classified as having refractory epilepsy. The International League Against Epilepsy Task Force proposed a consensus definition of refractory (or drug-resistant) epilepsy as “failure of adequate trials of two tolerated and appropriately chosen and used ASM schedules (whether as monotherapies or in combination) to achieve sustained seizure freedom”3. This definition has been used to facilitate early identification of refractory epilepsy. Consequently, it encourages the exploration of alternative treatment modalities, including epilepsy surgery, neuromodulation, and ketogenic diet4.

Ideally, the earlier the refractoriness is determined, the sooner epileptologists can consider alternative treatment options, such as surgery, in addition to rigorous medical treatment. Moreover, earlier resective surgery was correlated with better seizure outcome5. The likelihood of achieving seizure control decreases substantially with an increasing number of ASM trials6. Therefore, surgical treatment should be considered after failure of two adequate ASM regimens to achieve better seizure outcome. Seizures relapse in approximately 50% of patients after the failure of the first ASM regimen. Therefore, the early stages of ASM treatment are critical for identifying refractory epilepsy. For this purpose, researchers have used test results from drug-naïve patients to predict medical refractoriness. A previous study involving 287 drug-naïve patients incorporated clinical data, dichotomized imaging data, and EEG results7. However, individuals who used more than one ASM throughout the follow-up period were excluded from the study, potentially limiting the generalizability of the findings to a broader clinical population. Another study utilized claims data from a cohort of 582,258 patients to predict medical refractoriness8. However, in this study, refractory epilepsy was operationally defined as the prescription of more than four ASMs due to the paucity of information on seizure occurrence.

Another challenge in assessing refractoriness and the occurrence of seizures is the sole reliance on the patient’s memory. Recent research has shown that more than half of focal impaired awareness seizures or nocturnal seizures go unnoticed and are not reported9. This underscores the need for objective tools such as electroencephalography (EEG) or imaging to observe the current status or predict refractoriness.

Several clinical factors, such as early onset of epilepsy, symptomatic or cryptogenic epilepsy, multiple seizure types, many seizures before ASM treatment, and a family history of epilepsy, have been reported to be associated with refractory epilepsy in previous studies2,10. In a recent meta-analysis, EEG abnormality was a consistent predictive factor for refractory epilepsy11. Both slow waves and epileptiform discharges have been associated with refractory epilepsy in newly diagnosed patients with epilepsy12–14.

Recently, machine learning (ML) algorithms have been employed in patients with epilepsy. Researchers utilized ML algorithms to monitor seizure15 or to predict epilepsy outcomes. Many researchers have used diverse features that were previously established by conventional statistical methods7,16. In these models, the EEG results were presented as categorical variables (i.e., normal, non-epileptiform abnormality, or epileptiform discharge). Presurgical clinical, electrographic, neuropsychological, imaging, and surgical data were used to predict surgical outcomes in patients with temporal lobe epilepsy17,18. Some studies utilized features from raw EEG data to predict treatment responses to levetiracetam19,20. However, only a few studies have used EEG-based features to predict medical refractoriness.

Lin et al. built an SVM model to predict medical refractoriness in 23 children with idiopathic epilepsy21. They extracted 24 EEG features from nine categories (autoregressive modeling predictive error, decorrelation time, energy, entropy, Hjorth, relative power, spectral edge, statistic, and energy of the wavelet coefficients). Gain ratio measure was adopted for feature selection. Wang et al. also developed an SVM model to predict medical refractoriness in a group of 164 drug-naive children and adults with epilepsy22. This model utilized a combination of clinical characteristics and EEG functional connectivity features (phase-lag index). For feature selection, RFE was applied. Although these studies used various features, only SVM classifier was utilized. Also, they included a heterogeneous group of patients with both focal and generalized epilepsy.

In an earlier study, the first ASM led to a seizure-free rate of 47%, the second ASM achieved a seizure-free rate of 13%, and the third option resulted in a seizure-free rate of only 4%10. Considering these statistics, patients who fail to reach a seizure-free status with initial monotherapy seem to have a likelihood of seizure freedom of less than 20% after further ASM trials. Therefore, we generated an ML model using EEG-based features to predict medical refractoriness in patients with temporal lobe epilepsy on initial monotherapy.

Results

Demographic and clinical characteristics

Forty-eight patients with unilateral TLE treated with monotherapy between 2014 and 2021 were identified; 33 (68.8%) patients had left-sided TLE, and 15 (31.3%) had right-sided TLE. The age of epilepsy onset was 44.9 ± 19.2 years old (mean ± standard deviation), and the age in the EEG study was 54.1 ± 15.5 years old. The follow-up duration from the EEG study to the last follow-up (when the final outcome was determined) was 38.5 ± 21.8 months. Of the 48 patients, 34 (70.8%) were responsive, and 14 (29.2%) were refractory to ASM treatment at the last follow-up. Hippocampal sclerosis was identified in 5 (10.4%) patients, trauma in 5 (10.4%) patients, and hemorrhage in 5 (10.4%) patients. The most frequently used ASM in the EEG study was levetiracetam (N = 20, 41.7%), followed by oxcarbazepine (N = 9, 18.8%) and lacosamide (N = 8, 16.7%). No demographic or clinical characteristics were significantly different between the responsive and refractory groups (Table 1). Length of EEG analyzed could be found in Supplementary Table S1.Table 1 Demographic and clinical characteristics of the responsive and refractory groups.

	Responsive group (N = 34)	Refractory group (N = 14)	p-value	
Sex (N, %)	0.830a	
 Male	19 (55.9%)	9 (64.3%)		
 Female	15 (44.1%)	5 (35.7%)	
Age of epilepsy onset (years, mean ± s.d.)	45.4 ± 19.5	43.9 ± 19.0	0.809b	
Age at EEG study (years, mean ± s.d.)	53.4 ± 16.7	55.9 ± 12.6	0.626b	
Follow-up duration (months, mean ± s.d.)	39.0 ± 22.5	37.2 ± 21.0	0.800b	
Seizure types (N, %)	0.810a	
 Focal seizures only	20 (58.8%)	7 (50.0%)		
 Focal and focal to bilateral tonic–clonic seizures	14 (41.2%)	7 (50.0%)	
Etiology (N, %)	0.692c	
 Hippocampal sclerosis	3 (8.8%)	2 (14.3%)		
 Trauma	4 (11.8%)	1 (7.1%)	
 Hemorrhage	2 (5.9%)	3 (21.4%)	
 Cerebral infarction	1 (2.9%)	1 (7.1%)	
 Moyamoya disease	2 (5.9%)	0 (0.0%)	
 Encephalitis	1 (2.9%)	1 (7.1%)	
 Focal cortical dysplasia	1 (2.9%)	0 (0.0%)	
 Cavernous malformation	1 (2.9%)	0 (0.0%)	
 Unknown	19 (55.9%)	6 (42.9%)	
History of febrile convulsion (N, %)	2 (5.9%)	0 (0.0%)	0.895c	
History of CNS infection (N, %)	1 (2.9%)	0 (0.0%)	1.000c	
Epileptic focus (N, %)	0.441a	
 Left	25 (73.5%)	8 (57.1%)		
 Right	9 (26.5%)	6 (42.9%)	
ASM at EEG study (N, %)	0.361c	
 Levetiracetam	16 (47.1%)	4 (28.6%)		
 Oxcarbazepine	7 (20.6%)	2 (14.3%)	
 Lacosamide	4 (11.8%)	4 (28.6%)	
 Valproic acid	4 (11.8%)	1 (7.1%)	
 Carbamazepine	2 (5.9%)	2 (14.3%)	
 Lamotrigine	0 (0.0%)	1 (7.1%)	
 Topiramate	1 (2.9%)	0 (0.0%)	
Seizure frequency at EEG study (per month, mean ± s.d.)	0.5 ± 1.7	0.6 ± 0.5	0.788b	
IED on first EEG (N, %)	15 (44.1%)	10 (71.4%)	0.160a	
EEG electroencephalography, s.d. standard deviation, ASM antiseizure medication, IED interictal epileptic discharge, CNS central nervous system.

aChi-square test.

bMann‒Whitney U test.

cFisher’s exact test.

Predictive performance across various frequency bands and features

The overall flowchart of the analysis is provided in Supplementary Fig. S1. Figure 1 shows the predictive performance of the responsive and refractory groups across different frequency bands using various features extracted from EEG signals. On average, features based on interchannel connectivity, such as Pearson’s correlation coefficient and coherence, outperformed those derived from single-channel information, including the Hjorth parameter, statistical measures, energy metrics, and zero-crossing rate. In a comparative evaluation of the highest AUROC values among the various frequency bands and features, single-channel features yielded an average AUROC of 0.518, whereas interchannel features yielded an average AUROC of 0.611. Notably, the coherence feature with the gamma frequency attained the highest AUROC over the fivefold (0.635 ± 0.131).Fig. 1 Comparative prediction performance across various features. (A) Hjorth parameter. (B) Statistical measures. (C) Energy metrics. (D) Zero-crossing rate. (E) Interchannel Pearson correlation coefficient. (F) Interchannel coherence. The mean AUROC for each feature is indicated by a blue line, with the corresponding 95% confidence intervals depicted by green lines. A red vertical line marks the feature achieving the highest AUROC in each feature, highlighting the coherence feature within the gamma frequency band as the top performer with an AUROC of 0.635. AUROC average area under the receiver operating characteristic curve.

Predictive performance across various machine learning models and feature selection methods

In the analysis of coherence features within the gamma frequency, which demonstrated the highest performance in Fig. 1, 190 features were extracted and subsequently analyzed. When implementing feature selection across various ML models, the optimal performance was achieved using the mutual information filter-based feature selection method, in conjunction with LGB (Fig. 2), with the extraction of 25 features. At the window level, the model exhibited an AUROC of 0.774 (95% CI 0.643–0.904), accuracy of 0.757 (95% CI 0.659–0.855), sensitivity of 0.667 (95% CI 0.457–0.876), specificity of 0.807 (95% CI 0.687–0.926), positive predictive value of 0.681 (95% CI 0.522–0.840), and negative predictive value of 0.818 (95% CI 0.726–0.910). Advancing to a patient-level evaluation via soft voting, an AUROC of 0.821 (95% CI 0.654–0.988), accuracy of 0.791 (95% CI 0.640–0.943), sensitivity of 0.683 (95% CI 0.389–0.977), specificity of 0.838 (95% CI 0.692–0.984), positive predictive value of 0.700 (95% CI 0.439–0.961), and negative predictive value of 0.855 (95% CI 0.724–0.985) were achieved. Of 31 external validation set, 18 (58.1%) were responsive and 13 (41.9%) were refractory to ASM treatment. For external validation set, an AUROC was 0.718 (95% CI 0.682–0.735) at the window level, and 0.798 (95% CI 0.747–0.829) at the patient level. For a comprehensive view of the performance metrics, refer to Table 2. Confusion matrix during fivefold could be found in Supplementary Fig. S2.Fig. 2 Comparative analysis of prediction performance across different machine learning models and feature selection methods. The figure shows the area under the ROC curve (AUROC) for each model and feature selection method combination. Red and blue colors represent filter-based and wrapper-based feature selection methods, respectively. The analysis demonstrates the performance variability of models when using different feature selection techniques. Notably, the Light Gradient Boosting (LGB) model with mutual information filter-based feature selection achieved the highest AUROC of 0.774. This comparison highlights the significance of feature selection methods on model performance, guiding the selection of the most effective approach. AUROC average area under the receiver operating characteristic curve, MI mutual information, ANOVA analysis of variance, RFE recursive feature elimination, RF random forest, XGB extreme gradient boosting, LGB light gradient boosting, SVM suppor vector machine, KNN k-nearest neighbour's, LR logistic regression.

Table 2 Detailed prediction performances at window and patient level.

Dataset	Level	AUROC	Accuracy	Sensitivity	Specificity	PPV	NPV	
Develop	Window	0.774 [0.643–0.904]	0.757 [0.659–0.855]	0.667 [0.457–0.876]	0.807 [0.687–0.926]	0.681 [0.522–0.840]	0.818 [0.726–0.910]	
Patient	0.821 [0.654–0.988]	0.791 [0.640–0.943]	0.683 [0.389–0.977]	0.838 [0.692–0.984]	0.700 [0.439–0.961]	0.855 [0.724–0.985]	
External validation	Window	0.718 [0.682–0.735]	0.670 [0.644–0.675]	0.699 [0.633–0.744]	0.810 [0.763–0.837]	0.837 [0.802–0.853]	0.663 [0.613–0.692]	
Patient	0.788 [0.747–0.829]	0.735 [0.705–0.766]	0.632 [0.613–0.651]	0.697 [0.662–0.732]	0.737 [0.716–0.759]	0.585 [0.570–0.600]	
Values are presented with [95% confidence interval].

AUROC average area under the receiver operating characteristic curve, PPV positive predictive value, NPV negative predictive value.

Functional network analysis using EEG coherence values

For functional network analysis, only the top 20 features (i.e. coherence values between EEG channel pairs) consistently selected across the folds (≥ three times out of five folds) were used. These channel pairs are as follows: Cz-C3, F3-C3, F4-Cz, Fz-C3, Fz-C4, Fz-Cz, P3-C3, P3-Fz, Pz-C3, Pz-O2, Pz-P7, F3-Ca, Pf-Fp1, P4-Fz, Pz-Cz, Pz-Fz, Pz-O1, Pz-P3, P4-F4, and Pz-F4. The SHAP index and importance of the selected channel pairs observed across the five folds are illustrated in Supplementary Figs. S3 and S4, respectively. Supplementary Table S2 shows how many times each channel pair was selected during the folds. Coherence values of selected channel pairs at the window and patient levels are presented in Supplementary Tables S3 and S4.

Figure 3 presents a direct comparison of the top 20 selected channel pairs between the responsive and refractory groups at both window and patient levels. In particular, interchannel coherence displayed larger values in the refractory group (blue lines) than in the responsive group (red lines). Coherences with larger values in the responsive group were primarily observed in the hemisphere ipsilateral to the epileptic focus, which is represented as red edges in Fig. 3. Conversely, coherences with larger values in the refractory group were distributed across the contralateral hemisphere (depicted as blue edges in Fig. 3) as well as in the ipsilateral hemisphere. Notably, only one channel pair (Pz-P7) was selected from among the channel pairs that involved the temporal area.Fig. 3 Visualization of interchannel coherence value. Coherence values were demonstrated among selected channels at (A) window level and (B) patient level. Red edges indicate channel pairs with larger coherence values in the responsive group, and blue colors indicate channel pairs with larger coherence values in the refractory group. Note that the epileptic focus was placed in the left temporal area in this analysis. Visualizations were created using Python 3.9.12 with the Matplotlib 3.7.0 library. (https://matplotlib.org/3.7.0/).

Graph theory measurements based on EEG coherence values

Table 3 presents a comparison of graph theory measurements based on EEG coherence values between the responsive and refractory groups. At the window level, the modularity, closeness centrality, clustering coefficient, betweenness centrality, and degree coefficient were significantly higher in the refractory group. Similarly, at the patient level, the modularity, eigenvector centrality, clustering coefficient, betweenness centrality, and degree coefficient were significantly higher in the refractory group.Table 3 Graph measurement comparisons for patients with resting-state lengths exceeding 10 min at both window and patient levels.

Level	Graph measure	p-value	Responsive group (mean ± s.d.)	Refractory group (mean ± s.d.)	
Window	Small worldness	0.636	1.042 ± 0.042	1.040 ± 0.041	
Modularity	 < 0.001	0.136 ± 0.114	0.170 ± 0.096	
Eigenvector centrality	0.102	0.221 ± 0.043	0.226 ± 0.047	
Closeness centrality	 < 0.001	0.260 ± 0.066	0.297 ± 0.032	
Clustering coefficient	 < 0.001	0.323 ± 0.183	0.389 ± 0.181	
Betweenness centrality	 < 0.001	0.062 ± 0.034	0.084 ± 0.025	
Degree coefficient	 < 0.001	0.171 ± 0.087	0.200 ± 0.088	
Patient	Small worldness	0.951	1.048 ± 0.048	1.047 ± 0.044	
Modularity	0.003	0.108 ± 0.104	0.208 ± 0.032	
Eigenvector centrality	0.032	0.216 ± 0.046	0.244 ± 0.024	
Closeness centrality	0.188	0.264 ± 0.066	0.295 ± 0.034	
Clustering coefficient	0.024	0.294 ± 0.195	0.442 ± 0.115	
Betweenness centrality	0.008	0.053 ± 0.037	0.086 ± 0.020	
Degree coefficient	0.022	0.156 ± 0.093	0.228 ± 0.052	
s.d. standard deviation.

Discussion

In this study, we developed an ML model to predict medical refractoriness using the initial EEGs of patients with TLE who were on monotherapy. The best prediction performance was achieved by the coherence of the gamma frequency band by applying a mutual information filter-based feature selection method utilizing LGB. The refractory group exhibited higher coherence values in the hemisphere contralateral to the epileptic focus than in the responsive group. In the graph analysis, the refractory group exhibited higher graph measurement values than the responsive group.

Among the various features analyzed in our study, coherence within the gamma frequency band demonstrated the most substantial predictive performance. Coherence, a measure of synchrony between EEG signals from different brain regions, offers valuable insights into brain functional connectivity. Disruptions in normal brain connectivity are the hallmark features of epilepsy. Our results align with this understanding, suggesting that higher coherence values in the gamma band may reflect altered or intensified neural communication, which is a characteristic feature of refractory epilepsy23,24.

Notably, the refractory group exhibited higher coherence values than the responsive group, predominantly in the hemisphere contralateral to the epileptic focus. This observation may indicate compensatory or maladaptive network reorganization in refractory patients. The increased synchrony in the contralateral hemisphere may reflect the brain’s attempt to counterbalance the disruption caused by epileptic activity in the affected hemisphere. However, this compensatory mechanism may contribute to the propagation of ictal discharges throughout the entire brain network. Consequently, the persistence or aggravation of seizures due to network alteration can lead to medical refractoriness.

Furthermore, the superior predictive performance of coherence over that of Pearson’s correlation underscores the importance of considering frequency-specific brain connectivity measures in the study and management of epilepsy. Due to its sensitivity to frequency-specific synchronization relevant to epilepsy, coherence in the gamma frequency band has emerged as a more precise tool for predicting medical refractoriness25–27.

Horstmann et al. identified higher clustering coefficients and average path lengths in patients with temporal or neocortical extratemporal epilepsy than in controls28. This distinction was particularly notable in the delta band. Van Diessen et al. studied various graph theory metrics (degree centrality, path length, clustering coefficient, betweenness centrality, closeness centrality, and eigenvector centrality) between children with focal epilepsy and controls29. Although none of the graph theory measurements showed significant differences between the two groups, an RF-based model utilizing these variables successfully distinguished children with focal epilepsy, achieving an AUROC of 0.89. Regarding the prediction of refractory epilepsy, Lee et al. observed a higher mean clustering coefficient within the hippocampal network in patients with refractory TLE than in those with responsive TLE30. Consistent with these findings, we observed altered graph theory parameters in the refractory group within our study population. Specifically, the modularity, eigenvector centrality, clustering coefficient, betweenness centrality, and degree coefficient were higher in the refractory group than in the responsive group.

This study has few limitations. (1) Small sample size. This study has a relatively small sample size of 48 patients. The limited sample size could be attributed to two factors. First, the EEG recordings were restricted to a single EEG system, which constrained the number of available subjects. Second, the study focused exclusively on patients with TLE, which contributed to a limited sample size. Therefore, future research may benefit from a multicenter approach and the application of transfer-learning techniques to overcome machine- and site-specific disparities. (2) Timing of EEG acquisition. EEG data were collected after the administration of the first ASM rather than before ASM initiation. This choice was predominantly influenced by the practical difficulty of conducting an EEG immediately after a seizure due to the extended waiting times in the institutions participating in this study. (3) Asymmetry of left and right hemisphere was not considered in adjustment of EEG signals from participants whose epileptic focus was in the right hemisphere. Functional differentiation of language and visuospatial domain exists in cerebral hemispheres, therefore EEG signals from left and right hemispheres are exactly symmetric. However, in this study, we were able to perform graph analyses in regards to epileptic focus because we flipped EEG signals from participants with right epileptic focus.

In this study, we developed an ML model to predict medical refractoriness in patients with TLE using EEG coherence features. By limiting the study subjects to patients with unilateral TLE, we were able to interpret the functional connectivity analysis results with respect to the epileptic focus. After initial diagnosis of TLE and initiation of single ASM, this ML model could help identify refractory TLE in referral hospitals, where most patients with refractory epilepsy are treated.

Methods

Patients and data collection

This is a retrospective observational study using routine clinical data. Adult patients (≥ 18 years) were enrolled at 2 tertiary referral centers for epilepsy, Seoul National University Hospital and Kangbuk Samsung Hospital between 2014 and 2021. Inclusion criteria were as follows: (1) TLE (temporal lobe epilepsy) diagnosis based on seizure semiology, EEG, and magnetic resonance imaging; (2) monotherapy (1 ASM) during the first EEG recording; (3) unilateral epileptic focus. For external validation, 31 TLE patients meeting the same inclusion criteria were enrolled from 2022 to 2023. Demographic and clinical characteristics, including baseline and final seizure frequencies, were obtained through a retrospective review of medical records. A total of 48 patients with TLE were selected and divided into two groups according to the final outcome, regardless of the final ASM regimen: the responsive group (no seizures during the last 1 year of follow-up) and the refractory group (one or more seizures in the last 1 year of follow-up) (Fig. 4).Fig. 4 Study flow diagram. TLE temporal lobe epilepsy, ASM antiseizure medication, EEG electroencephalography.

Statistical analysis

We used the mean (standard deviation) or frequency (proportion) for statistical analyses. Normality tests were performed using the Shapiro–Wilk test. The chi-square test was used to compare the distributions of sex, seizure type, epileptic focus, and interictal epileptic discharge on the first EEG between the groups. Fisher’s exact test was used to compare the distributions of the etiology of epilepsy, history of febrile convulsion, history of central nervous system infection, and ASM at the first EEG between the groups. Mann–Whitney U test was used to analyze the differences in age at epilepsy onset, age at EEG study, follow-up duration, and seizure frequency at the time of EEG study between the groups. p-value under the threshold of 0.05 was considered statistically significant.

EEG recording

Interictal EEG data were recorded using the NicoletOne® EEG system (Natus, San Carlo, CA, USA), in accordance with the international 10–20 electrode placement protocol, with a sampling frequency of 250 Hz, a hardware high-pass filter of 0.1 Hz, and a hardware low-pass filter of 500 Hz. To ensure optimal signal quality, the impedance of all electrodes was meticulously maintained below 10 kΩ. This study leveraged datasets from two separate organizations to foster a comprehensive analysis. To guarantee uniformity across datasets, only 19 channels (electrodes: Fp1, F7, T7, P7, F3, C3, P3, O1, Fp2, F8, T8, P8, F4, C4, P4, O2, Fz, Cz, and Pz) universally present in both organizations were incorporated into the analysis. EEG data without any stimulus recorded with eyes closed were utilized for this study.

Preprocessing

Based on the results of a previous study, a minimum data length of 2 min was deemed necessary to analyze significant epileptic seizure signals effectively31. Adhering to this guideline, several data windows were created from the individual patient data, each spanning 120 s with a 50% overlap. The increased dataset size helps mitigate overfitting that originates from small datasets, as the model is less likely to learn from the idiosyncrasies of a small dataset and more from generalizable patterns. Subsequently, the data were referenced from the average of the following EEG channels: F3, Fz, F4, C3, Cz, C4, P3, Pz, P4, O1, and O2.

To facilitate a nuanced analysis accounting for the initial site of a patient's epileptic seizures, a methodical strategy was employed to position the electrodes. For individuals with an epileptic focus on the left side, the existing EEG electrode placements were retained. Conversely, for those with an epileptic focus on the right side, the electrode positions were symmetrically adjusted. With this adjustment, the epileptic focus was positioned in the left hemisphere for each individual.

Prior to analysis, the signals underwent bandpass filtering across various frequency bands: delta (0.5–3 Hz), theta (3–8 Hz), alpha (8–12 Hz), low-beta (12–20 Hz), high-beta (20–30 Hz), and gamma (30–50 Hz), to segregate and highlight the relevant signal components for a more robust analysis.

Feature extraction

In the feature extraction phase, four time-domain features (Hjorth parameters, statistical measures, energy metrics, and zero-crossing rate) and two connectivity-based features (Pearson’s correlation and coherence) were used for the analysis, owing to their proven significance in EEG analyses. In addition, connectivity analysis was conducted using Pearson’s correlation and coherence analysis. To avoid duplication and to preserve analytical precision, connectivity values related to duplicated and symmetrically redundant information were omitted from the dataset.Hjorth parameters Hjorth parameters have been used to detect and diagnose seizures, as well as predict seizure recurrence after ASM withdrawal. This set encompasses three components: activity, which indicates the signal power; mobility, representing the mean frequency; and complexity, reflecting changes in frequency32,33.

Statistical measures Statistical parameters have been employed as features to differentiate patients with epilepsy from healthy controls and predict the response to levetiracetam20,34. Six prevalent statistical indicators were used as features: skewness, kurtosis, mean, median, minimum, and maximum values.

Energy metrics Energy metrics serve as markers for assessing brain activity35. Therefore, the linear and nonlinear energies of the EEG signals were included to offer insights into the energy patterns present within the signal36.

Zero-crossing rate This parameter indicates the rate at which a signal transitions from positive to zero to negative or vice versa. It has been a prominent tool in numerous studies for distinguishing seizures from normal EEG signals. For this study, both the zero-crossing rate and its first derivative were incorporated into the analysis37,38.

Interchannel Pearson’s correlation coefficient Pearson’s correlation is a pivotal feature in brain analysis. It computes the linear relationship between two EEG channels and provides a measurement of both the strength and direction of the association between signal sets. This facilitates the identification of intricate patterns and potential anomalies within EEG signals39–41.

Interchannel coherence Coherence is a spectral-domain measure that offers insights into the synchrony between EEG channels in specific frequency bands. By evaluating the cross-spectral and auto-spectral densities, spectral-domain coherence facilitates the understanding of connectivity patterns and potential neural network alliances within EEG data42,43.

Feature selection

Robust feature selection techniques were utilized to improve the performance of the ML model and reduce the risk of overfitting. Two principal methods were employed: filter-based and wrapper-based feature selection. It is critical to highlight that the feature selection process was confined exclusively to the training set. During our fivefold cross-validation procedure, we meticulously maintained a clear separation between the training and validation datasets. Feature selection was conducted exclusively using the training data. Subsequently, the performance metrics were evaluated solely based on the validation data for each fold.Filter-based feature selection is a technique that selects relevant features based on statistical properties. Three commonly used filter-based strategies (chi-square, ANOVA F-value, and mutual information) were employed44. Each of these methods was applied to assess the significance and contribution of individual features within our dataset.

Wrapper-based method uses a search algorithm to evaluate different subsets of features and selects the optimal subset that achieves the best performance for a given ML model. Recursive feature elimination (RFE) was utilized as our wrapper method, systematically reducing the feature set to identify the most predictive features45.

Evaluation

Six robust classifiers, random forest (RF)46, extreme gradient boosting (XGB)47, and light gradient boosting (LGB), support vector machine (SVM), K-nearest neighbors (KNN), logistic regression (LR) were employed in this study48. The optimal feature selection method was determined based on the average area under the receiver operating characteristic curve (AUROC) ascertained during a fivefold cross-validation process. A comprehensive assessment of the model’s performance was facilitated through the analysis of various metrics, including the AUROC, accuracy, F1 score, sensitivity, and specificity. Moreover, both positive and negative predictive values were meticulously scrutinized to gauge the proficiency of the model in accurately delineating the respective classes.

A fivefold cross-validation was implemented at the patient level, rather than at the individual window level. By implementing cross-validation at the patient level, all data pertaining to a single patient, including their respective windows, are grouped together. This ensures that the model is tested on completely unseen patients, providing a more reliable and accurate assessment of its ability to generalize and its true predictive power. After identifying the superior model and feature selection method at the window level, an evaluation at the patient level was conducted using a soft voting mechanism (Supplementary Fig. S5), which is a critical method for aggregating probabilistic predictions across each individual patient’s window, thereby ensuring more nuanced, reliable, and comprehensive insights into the model’s predictive capabilities.

Feature interpretation and graph measurement

The selected channel pairs may vary during the fivefold cross-validation process, highlighting the importance of focusing on channel pairs that are consistently chosen in at least three of the fivefold. The average feature importance and Shapley additive explanation (SHAP) values49 for the chosen edges were systematically analyzed to understand their respective contributions to model predictions. Furthermore, a statistical comparative analysis was conducted between the responsive and refractory groups. A two-tailed paired t-test was employed to analyze each feature, both at the individual window levels and at the patient level (average window basis), with a significance threshold set at 0.05.

Given the prominence of coherence as a principal feature, the visualization results are depicted graphically. Each channel is represented as a node, and the coherence value is illustrated as an edge between the nodes. Graph visualization and analysis were performed using the NetworkX50 and nilearn51 Python libraries. To compare graph measurements, edges were connected in each window only if the coherence values were higher than 0.5. At the patient level, a single graph per patient was generated by averaging the values across all windows and subsequently connecting or disconnecting the edges based on a threshold of 0.5. Given the sensitivity of averaging to outliers, especially in cases with a limited number of windows, the analyses were restricted to patients with resting-state lengths exceeding 10 min, ensuring a minimum of 10 windows.

Supplementary Information

Supplementary Information.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-71583-0.

Acknowledgements

This study was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (Grant Number: RS-2023-00265638).

Author contributions

S.H., Y.S., S.B.L., S.K.L., Y.G.K., and K.I.P. conceived and designed the study; S.H., J.S.S., and H.S. collected the data; S.H. and Y.S. conducted the analyses; S.H., Y.S., Y.G.K., and K.I.P. interpreted the data; S.H. and Y.S. wrote the manuscript; S.H., Y.S., J.S.S., H.S., S.B.L., K.C., K.Y.J., S.K.L., Y.G.K. and K.I.P. edited and approved the manuscript.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Ethical approval

This study was approved by the Institutional Review Board of Seoul National University Hospital (reference number H-2308-010-1455). The study was performed in accordance with the Declaration of Helsinki. Due to the retrospective nature of the study, the Institutional Review Board of Seoul National University Hospital waived the need of obtaining informed consent.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Sungeun Hwang, Youmin Shin, Young-Gon Kim and Kyung-Il Park.
==== Refs
References

1. Fisher RS Instruction manual for the ILAE 2017 operational classification of seizure types Epilepsia 2017 58 531 542 10.1111/epi.13671 28276064
Fisher, R. S. et al. Instruction manual for the ILAE 2017 operational classification of seizure types. Epilepsia 58, 531–542. 10.1111/epi.13671 (2017).28276064 10.1111/epi.13671
2. Chen Z Brodie MJ Liew D Kwan P Treatment outcomes in patients with newly diagnosed epilepsy treated with established and new antiepileptic drugs: A 30-year longitudinal cohort study JAMA Neurol. 2018 75 279 286 10.1001/jamaneurol.2017.3949 29279892
Chen, Z., Brodie, M. J., Liew, D. & Kwan, P. Treatment outcomes in patients with newly diagnosed epilepsy treated with established and new antiepileptic drugs: A 30-year longitudinal cohort study. JAMA Neurol. 75, 279–286. 10.1001/jamaneurol.2017.3949 (2018).29279892 10.1001/jamaneurol.2017.3949
3. Kwan P Definition of drug resistant epilepsy: Consensus proposal by the ad hoc Task Force of the ILAE Commission on therapeutic strategies Epilepsia 2010 51 1069 1077 10.1111/j.1528-1167.2009.02397.x 19889013
Kwan, P. et al. Definition of drug resistant epilepsy: Consensus proposal by the ad hoc Task Force of the ILAE Commission on therapeutic strategies. Epilepsia 51, 1069–1077. 10.1111/j.1528-1167.2009.02397.x (2010).19889013 10.1111/j.1528-1167.2009.02397.x
4. Brodie MJ Sills GJ Combining antiepileptic drugs—Rational polytherapy? Seizure 2011 20 369 375 10.1016/j.seizure.2011.01.004 21306922
Brodie, M. J. & Sills, G. J. Combining antiepileptic drugs—Rational polytherapy? Seizure 20, 369–375. 10.1016/j.seizure.2011.01.004 (2011).21306922 10.1016/j.seizure.2011.01.004
5. Simasathien T Improved outcomes with earlier surgery for intractable frontal lobe epilepsy Ann. Neurol. 2013 73 646 654 10.1002/ana.23862 23494550
Simasathien, T. et al. Improved outcomes with earlier surgery for intractable frontal lobe epilepsy. Ann. Neurol. 73, 646–654. 10.1002/ana.23862 (2013).23494550 10.1002/ana.23862
6. Kwan P Brodie MJ Epilepsy after the first drug fails: Substitution or add-on? Seizure 2000 9 464 468 10.1053/seiz.2000.0442 11034869
Kwan, P. & Brodie, M. J. Epilepsy after the first drug fails: Substitution or add-on? Seizure 9, 464–468. 10.1053/seiz.2000.0442 (2000).11034869 10.1053/seiz.2000.0442
7. Yao L Prediction of antiepileptic drug treatment outcomes of patients with newly diagnosed epilepsy by machine learning Epilepsy Behav. 2019 96 92 97 10.1016/j.yebeh.2019.04.006 31121513
Yao, L. et al. Prediction of antiepileptic drug treatment outcomes of patients with newly diagnosed epilepsy by machine learning. Epilepsy Behav. 96, 92–97. 10.1016/j.yebeh.2019.04.006 (2019).31121513 10.1016/j.yebeh.2019.04.006
8. An S Predicting drug-resistant epilepsy—A machine learning approach based on administrative claims data Epilepsy Behav. 2018 89 118 125 10.1016/j.yebeh.2018.10.013 30412924
An, S. et al. Predicting drug-resistant epilepsy—A machine learning approach based on administrative claims data. Epilepsy Behav. 89, 118–125. 10.1016/j.yebeh.2018.10.013 (2018).30412924 10.1016/j.yebeh.2018.10.013
9. Elger CE Hoppe C Diagnostic challenges in epilepsy: Seizure under-reporting and seizure detection Lancet Neurol. 2018 17 279 288 10.1016/s1474-4422(18)30038-3 29452687
Elger, C. E. & Hoppe, C. Diagnostic challenges in epilepsy: Seizure under-reporting and seizure detection. Lancet Neurol. 17, 279–288. 10.1016/s1474-4422(18)30038-3 (2018).29452687 10.1016/s1474-4422(18)30038-3
10. Kwan P Brodie MJ Early identification of refractory epilepsy N. Engl. J. Med. 2000 342 314 319 10.1056/nejm200002033420503 10660394
Kwan, P. & Brodie, M. J. Early identification of refractory epilepsy. N. Engl. J. Med. 342, 314–319. 10.1056/nejm200002033420503 (2000).10660394 10.1056/nejm200002033420503
11. Xue-Ping W Hai-Jiao W Li-Na Z Xu D Ling L Risk factors for drug-resistant epilepsy: A systematic review and meta-analysis Medicine 2019 98 e16402 10.1097/md.0000000000016402 31348240
Xue-Ping, W., Hai-Jiao, W., Li-Na, Z., Xu, D. & Ling, L. Risk factors for drug-resistant epilepsy: A systematic review and meta-analysis. Medicine 98, e16402. 10.1097/md.0000000000016402 (2019).31348240 10.1097/md.0000000000016402
12. Aaberg KM Short-term seizure outcomes in childhood epilepsy Pediatrics 2018 141 16 10.1542/peds.2017-4016
Aaberg, K. M. et al. Short-term seizure outcomes in childhood epilepsy. Pediatrics 141, 16. 10.1542/peds.2017-4016 (2018).10.1542/peds.2017-4016
13. Berg AT Early development of intractable epilepsy in children: A prospective study Neurology 2001 56 1445 1452 10.1212/wnl.56.11.1445 11402099
Berg, A. T. et al. Early development of intractable epilepsy in children: A prospective study. Neurology 56, 1445–1452. 10.1212/wnl.56.11.1445 (2001).11402099 10.1212/wnl.56.11.1445
14. Ko TS Holmes GL EEG and clinical predictors of medically intractable childhood epilepsy Clin. Neurophysiol. 1999 110 1245 1251 10.1016/s1388-2457(99)00068-1 10423189
Ko, T. S. & Holmes, G. L. EEG and clinical predictors of medically intractable childhood epilepsy. Clin. Neurophysiol. 110, 1245–1251. 10.1016/s1388-2457(99)00068-1 (1999).10423189 10.1016/s1388-2457(99)00068-1
15. Peng G Nourani M Harvey J Dave H Personalized EEG feature selection for low-complexity seizure monitoring Int. J. Neural Syst. 2021 31 2150018 10.1142/s0129065721500180 33752579
Peng, G., Nourani, M., Harvey, J. & Dave, H. Personalized EEG feature selection for low-complexity seizure monitoring. Int. J. Neural Syst. 31, 2150018. 10.1142/s0129065721500180 (2021).33752579 10.1142/s0129065721500180
16. Hakeem H Development and validation of a deep learning model for predicting treatment response in patients with newly diagnosed epilepsy JAMA Neurol. 2022 10.1001/jamaneurol.2022.2514 36036923
Hakeem, H. et al. Development and validation of a deep learning model for predicting treatment response in patients with newly diagnosed epilepsy. JAMA Neurol.10.1001/jamaneurol.2022.2514 (2022).36036923 10.1001/jamaneurol.2022.2514
17. Grigsby J Kramer RE Schneiders JL Gates JR Brewster Smith W Predicting outcome of anterior temporal lobectomy using simulated neural networks Epilepsia 1998 39 61 66 10.1111/j.1528-1157.1998.tb01275.x 9578014
Grigsby, J., Kramer, R. E., Schneiders, J. L., Gates, J. R. & Brewster Smith, W. Predicting outcome of anterior temporal lobectomy using simulated neural networks. Epilepsia 39, 61–66. 10.1111/j.1528-1157.1998.tb01275.x (1998).9578014 10.1111/j.1528-1157.1998.tb01275.x
18. Armañanzas R Machine learning approach for the outcome prediction of temporal lobe epilepsy surgery PLoS ONE 2013 8 e62819 10.1371/journal.pone.0062819 23646148
Armañanzas, R. et al. Machine learning approach for the outcome prediction of temporal lobe epilepsy surgery. PLoS ONE 8, e62819. 10.1371/journal.pone.0062819 (2013).23646148 10.1371/journal.pone.0062819
19. Zhang JH Personalized prediction model for seizure-free epilepsy with levetiracetam therapy: A retrospective data analysis using support vector machine Br. J. Clin. Pharmacol. 2018 84 2615 2624 10.1111/bcp.13720 30043454
Zhang, J. H. et al. Personalized prediction model for seizure-free epilepsy with levetiracetam therapy: A retrospective data analysis using support vector machine. Br. J. Clin. Pharmacol. 84, 2615–2624. 10.1111/bcp.13720 (2018).30043454 10.1111/bcp.13720
20. Croce P Machine learning for predicting levetiracetam treatment response in temporal lobe epilepsy Clin. Neurophysiol. 2021 132 3035 3042 10.1016/j.clinph.2021.08.024 34717224
Croce, P. et al. Machine learning for predicting levetiracetam treatment response in temporal lobe epilepsy. Clin. Neurophysiol. 132, 3035–3042. 10.1016/j.clinph.2021.08.024 (2021).34717224 10.1016/j.clinph.2021.08.024
21. Lin LC Early prediction of medication refractoriness in children with idiopathic epilepsy based on scalp EEG analysis Int. J. Neural Syst. 2014 24 1450023 10.1142/s0129065714500233 25164248
Lin, L. C. et al. Early prediction of medication refractoriness in children with idiopathic epilepsy based on scalp EEG analysis. Int. J. Neural Syst. 24, 1450023. 10.1142/s0129065714500233 (2014).25164248 10.1142/s0129065714500233
22. Wang B An integrative prediction algorithm of drug-refractory epilepsy based on combined clinical-EEG functional connectivity features J. Neurol. 2022 269 1501 1514 10.1007/s00415-021-10718-z 34308506
Wang, B. et al. An integrative prediction algorithm of drug-refractory epilepsy based on combined clinical-EEG functional connectivity features. J. Neurol. 269, 1501–1514. 10.1007/s00415-021-10718-z (2022).34308506 10.1007/s00415-021-10718-z
23. Matos J Diagnosis of epilepsy with functional connectivity in EEG after a suspected first seizure Bioengineering 2022 9 690 10.3390/bioengineering9110690 36421091
Matos, J. et al. Diagnosis of epilepsy with functional connectivity in EEG after a suspected first seizure. Bioengineering 9, 690. 10.3390/bioengineering9110690 (2022).36421091 10.3390/bioengineering9110690
24. Jiruska P High-frequency network activity, global increase in neuronal activity, and synchrony expansion precede epileptic seizures in vitro J. Neurosci. 2010 30 5690 5701 10.1523/jneurosci.0535-10.2010 20410121
Jiruska, P. et al. High-frequency network activity, global increase in neuronal activity, and synchrony expansion precede epileptic seizures in vitro. J. Neurosci. 30, 5690–5701. 10.1523/jneurosci.0535-10.2010 (2010).20410121 10.1523/jneurosci.0535-10.2010
25. Engel J Jr Bragin A Staba R Mody I High-frequency oscillations: What is normal and what is not? Epilepsia 2009 50 598 604 10.1111/j.1528-1167.2008.01917.x 19055491
Engel, J. Jr., Bragin, A., Staba, R. & Mody, I. High-frequency oscillations: What is normal and what is not? Epilepsia 50, 598–604. 10.1111/j.1528-1167.2008.01917.x (2009).19055491 10.1111/j.1528-1167.2008.01917.x
26. Fisher RS Webber WR Lesser RP Arroyo S Uematsu S High-frequency EEG activity at the start of seizures J. Clin. Neurophysiol. 1992 9 441 448 10.1097/00004691-199207010-00012 1517412
Fisher, R. S., Webber, W. R., Lesser, R. P., Arroyo, S. & Uematsu, S. High-frequency EEG activity at the start of seizures. J. Clin. Neurophysiol. 9, 441–448. 10.1097/00004691-199207010-00012 (1992).1517412 10.1097/00004691-199207010-00012
27. Pereda E Quiroga RQ Bhattacharya J Nonlinear multivariate analysis of neurophysiological signals Prog. Neurobiol. 2005 77 1 37 10.1016/j.pneurobio.2005.10.003 16289760
Pereda, E., Quiroga, R. Q. & Bhattacharya, J. Nonlinear multivariate analysis of neurophysiological signals. Prog. Neurobiol. 77, 1–37. 10.1016/j.pneurobio.2005.10.003 (2005).16289760 10.1016/j.pneurobio.2005.10.003
28. Horstmann MT State dependent properties of epileptic brain networks: Comparative graph-theoretical analyses of simultaneously recorded EEG and MEG Clin. Neurophysiol. 2010 121 172 185 10.1016/j.clinph.2009.10.013 20045375
Horstmann, M. T. et al. State dependent properties of epileptic brain networks: Comparative graph-theoretical analyses of simultaneously recorded EEG and MEG. Clin. Neurophysiol. 121, 172–185. 10.1016/j.clinph.2009.10.013 (2010).20045375 10.1016/j.clinph.2009.10.013
29. van Diessen E Otte WM Braun KP Stam CJ Jansen FE Improved diagnosis in children with partial epilepsy using a multivariable prediction model based on EEG network characteristics PLoS ONE 2013 8 e59764 10.1371/journal.pone.0059764 23565166
van Diessen, E., Otte, W. M., Braun, K. P., Stam, C. J. & Jansen, F. E. Improved diagnosis in children with partial epilepsy using a multivariable prediction model based on EEG network characteristics. PLoS ONE 8, e59764. 10.1371/journal.pone.0059764 (2013).23565166 10.1371/journal.pone.0059764
30. Lee HJ Park KM Intrinsic hippocampal and thalamic networks in temporal lobe epilepsy with hippocampal sclerosis according to drug response Seizure 2020 76 32 38 10.1016/j.seizure.2020.01.010 31986443
Lee, H. J. & Park, K. M. Intrinsic hippocampal and thalamic networks in temporal lobe epilepsy with hippocampal sclerosis according to drug response. Seizure 76, 32–38. 10.1016/j.seizure.2020.01.010 (2020).31986443 10.1016/j.seizure.2020.01.010
31. Shin Y Using spectral and temporal filters with EEG signal to predict the temporal lobe epilepsy outcome after antiseizure medication via machine learning Sci. Rep. 2023 13 22532 10.1038/s41598-023-49255-2 38110465
Shin, Y. et al. Using spectral and temporal filters with EEG signal to predict the temporal lobe epilepsy outcome after antiseizure medication via machine learning. Sci. Rep. 13, 22532. 10.1038/s41598-023-49255-2 (2023).38110465 10.1038/s41598-023-49255-2
32. Päivinen N Epileptic seizure detection: A nonlinear viewpoint Comput. Methods Progr. Biomed. 2005 79 151 159 10.1016/j.cmpb.2005.04.006
Päivinen, N. et al. Epileptic seizure detection: A nonlinear viewpoint. Comput. Methods Progr. Biomed. 79, 151–159. 10.1016/j.cmpb.2005.04.006 (2005).10.1016/j.cmpb.2005.04.006
33. Tanveer, M., Pachori, R. B. & Angami, N. V. Classification of seizure and seizure-free EEG signals using Hjorth parameters. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI) 2180–2185 (2018).
34. Gemein LAW Machine-learning-based diagnostics of EEG pathology Neuroimage 2020 220 117021 10.1016/j.neuroimage.2020.117021 32534126
Gemein, L. A. W. et al. Machine-learning-based diagnostics of EEG pathology. Neuroimage 220, 117021. 10.1016/j.neuroimage.2020.117021 (2020).32534126 10.1016/j.neuroimage.2020.117021
35. Lanzone J The effect of Perampanel on EEG spectral power and connectivity in patients with focal epilepsy Clin. Neurophysiol. 2021 132 2176 2183 10.1016/j.clinph.2021.05.026 34284253
Lanzone, J. et al. The effect of Perampanel on EEG spectral power and connectivity in patients with focal epilepsy. Clin. Neurophysiol. 132, 2176–2183. 10.1016/j.clinph.2021.05.026 (2021).34284253 10.1016/j.clinph.2021.05.026
36. Ricci L Measuring the effects of first antiepileptic medication in temporal lobe epilepsy: Predictive value of quantitative-EEG analysis Clin. Neurophysiol. 2021 132 25 35 10.1016/j.clinph.2020.10.020 33248432
Ricci, L. et al. Measuring the effects of first antiepileptic medication in temporal lobe epilepsy: Predictive value of quantitative-EEG analysis. Clin. Neurophysiol. 132, 25–35. 10.1016/j.clinph.2020.10.020 (2021).33248432 10.1016/j.clinph.2020.10.020
37. Pyrzowski J Zero-crossing patterns reveal subtle epileptiform discharges in the scalp EEG Sci. Rep. 2021 11 4128 10.1038/s41598-021-83337-3 33602954
Pyrzowski, J. et al. Zero-crossing patterns reveal subtle epileptiform discharges in the scalp EEG. Sci. Rep. 11, 4128. 10.1038/s41598-021-83337-3 (2021).33602954 10.1038/s41598-021-83337-3
38. ShahidiZandi A Tafreshi R Javidan M Dumont GA Predicting temporal lobe epileptic seizures based on zero-crossing interval analysis in scalp EEG Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2010 2010 5537 5540 10.1109/iembs.2010.5626764 21096472
ShahidiZandi, A., Tafreshi, R., Javidan, M. & Dumont, G. A. Predicting temporal lobe epileptic seizures based on zero-crossing interval analysis in scalp EEG. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2010, 5537–5540. 10.1109/iembs.2010.5626764 (2010).21096472 10.1109/iembs.2010.5626764
39. Morgan VL Magnetic resonance imaging connectivity for the prediction of seizure outcome in temporal lobe epilepsy Epilepsia 2017 58 1251 1260 10.1111/epi.13762 28448683
Morgan, V. L. et al. Magnetic resonance imaging connectivity for the prediction of seizure outcome in temporal lobe epilepsy. Epilepsia 58, 1251–1260. 10.1111/epi.13762 (2017).28448683 10.1111/epi.13762
40. Antony AR Functional connectivity estimated from intracranial EEG predicts surgical outcome in intractable temporal lobe epilepsy PLoS ONE 2013 8 e77916 10.1371/journal.pone.0077916 24205027
Antony, A. R. et al. Functional connectivity estimated from intracranial EEG predicts surgical outcome in intractable temporal lobe epilepsy. PLoS ONE 8, e77916. 10.1371/journal.pone.0077916 (2013).24205027 10.1371/journal.pone.0077916
41. Peng G Nourani M Dave H Harvey J SEEG-based epileptic seizure network modeling and analysis for pre-surgery evaluation Comput. Biol. Med. 2023 167 107692 10.1016/j.compbiomed.2023.107692 37976827
Peng, G., Nourani, M., Dave, H. & Harvey, J. SEEG-based epileptic seizure network modeling and analysis for pre-surgery evaluation. Comput. Biol. Med. 167, 107692. 10.1016/j.compbiomed.2023.107692 (2023).37976827 10.1016/j.compbiomed.2023.107692
42. van Mierlo P Functional brain connectivity from EEG in epilepsy: Seizure prediction and epileptogenic focus localization Prog. Neurobiol. 2014 121 19 35 10.1016/j.pneurobio.2014.06.004 25014528
van Mierlo, P. et al. Functional brain connectivity from EEG in epilepsy: Seizure prediction and epileptogenic focus localization. Prog. Neurobiol. 121, 19–35. 10.1016/j.pneurobio.2014.06.004 (2014).25014528 10.1016/j.pneurobio.2014.06.004
43. Zaveri HP Localization-related epilepsy exhibits significant connectivity away from the seizure-onset area Neuroreport 2009 20 891 895 10.1097/WNR.0b013e32832c78e0 19424095
Zaveri, H. P. et al. Localization-related epilepsy exhibits significant connectivity away from the seizure-onset area. Neuroreport 20, 891–895. 10.1097/WNR.0b013e32832c78e0 (2009).19424095 10.1097/WNR.0b013e32832c78e0
44. Chandrashekar G Sahin F A survey on feature selection methods Comput. Electr. Eng. 2014 40 16 28 10.1016/j.compeleceng.2013.11.024
Chandrashekar, G. & Sahin, F. A survey on feature selection methods. Comput. Electr. Eng. 40, 16–28. 10.1016/j.compeleceng.2013.11.024 (2014).10.1016/j.compeleceng.2013.11.024
45. Guyon I Weston J Barnhill S Vapnik V Gene selection for cancer classification using support vector machines Mach. Learn. 2002 46 389 422 10.1023/A:1012487302797
Guyon, I., Weston, J., Barnhill, S. & Vapnik, V. Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389–422. 10.1023/A:1012487302797 (2002).10.1023/A:1012487302797
46. Breiman L Random Forests Mach. Learn. 2001 45 5 32 10.1023/A:1010933404324
Breiman, L. Random Forests. Mach. Learn. 45, 5–32. 10.1023/A:1010933404324 (2001).10.1023/A:1010933404324
47. Chen, T. & Guestrin, C. XGBoost: A scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016).
48. Ke, G. et al. Neural Information Processing Systems.
49. Lundberg, S. M. & Lee, S.-I. Neural Information Processing Systems.
50. Hagberg A Swart P Chult D Exploring Network Structure, Dynamics, and Function Using NetworkX 2008 Los Alamos National Lab
Hagberg, A., Swart, P. & Chult, D. Exploring Network Structure, Dynamics, and Function Using NetworkX (Los Alamos National Lab, 2008).
51. Abraham A Machine learning for neuroimaging with scikit-learn Front. Neuroinform. 2014 8 14 10.3389/fninf.2014.00014 24600388
Abraham, A. et al. Machine learning for neuroimaging with scikit-learn. Front. Neuroinform. 8, 14. 10.3389/fninf.2014.00014 (2014).24600388 10.3389/fninf.2014.00014
