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

39294238
72249
10.1038/s41598-024-72249-7
Article
Machine learning algorithm for predicting seizure control after temporal lobe resection using peri-ictal electroencephalography
Sheikh Shehryar R. sheikhs@ccf.org

12
McKee Zachary A. 3
Ghosn Samer 4
Jeong Ki-Soo 45
Kattan Michael 6
Burgess Richard C. 38
Jehi Lara 378
Saab Carl Y. 458
1 https://ror.org/03xjacd83 grid.239578.2 0000 0001 0675 4725 Department of Neurosurgery, Cleveland Clinic, Cleveland, OH USA
2 https://ror.org/03xjacd83 grid.239578.2 0000 0001 0675 4725 Department of Molecular Medicine, Cleveland Clinic, Cleveland, OH USA
3 https://ror.org/03xjacd83 grid.239578.2 0000 0001 0675 4725 Epilepsy Center, Cleveland Clinic, Cleveland, OH USA
4 https://ror.org/03xjacd83 grid.239578.2 0000 0001 0675 4725 Department of Biomedical Engineering, Cleveland Clinic, Cleveland, OH USA
5 https://ror.org/05gq02987 grid.40263.33 0000 0004 1936 9094 Department of Biomedical Engineering, Brown University, Providence, RI USA
6 https://ror.org/03xjacd83 grid.239578.2 0000 0001 0675 4725 Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH USA
7 https://ror.org/03xjacd83 grid.239578.2 0000 0001 0675 4725 Center for Computational Life Sciences, Cleveland Clinic, Cleveland, OH USA
8 grid.67105.35 0000 0001 2164 3847 School of Medicine, Case Western Reserve University, Cleveland, OH USA
18 9 2024
18 9 2024
2024
14 2177123 7 2024
5 9 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/.
Brain resection is curative for a subset of patients with drug resistant epilepsy but up to half will fail to achieve sustained seizure freedom in the long term. There is a critical need for accurate prediction tools to identify patients likely to have recurrent postoperative seizures. Results from preclinical models and intracranial EEG in humans suggest that the window of time immediately before and after a seizure (“peri-ictal”) represents a unique brain state with implications for clinical outcome prediction. Using a dataset of 294 patients who underwent temporal lobe resection for seizures, we show that machine learning classifiers can make accurate predictions of postoperative seizure outcome using 5 min of peri-ictal scalp EEG data that is part of universal presurgical evaluation (AUC 0.98, out-of-group testing accuracy > 90%). This is the first approach to seizure outcome prediction that employs a routine non-invasive preoperative study (scalp EEG) with accuracy range likely to translate into a clinical tool. Decision curve analysis (DCA) shows that compared to the prevalent clinical-variable based nomogram, use of the EEG-augmented approach could decrease the rate of unsuccessful brain resections by 20%.

Keywords

Drug resistant epilepsy
Machine learning
Decision curve analysis
Surgical outcome prediction
Temporal lobe resection
Subject terms

Predictive markers
Epilepsy
Machine learning
issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

Drug Resistant Epilepsy (DRE) afflicts over 20 million patients globally1. Uncontrolled seizures in DRE result in devastating consequences for quality of life2,3, increased healthcare costs4, and increased mortality5. Surgical brain resection can stop seizures permanently for many patients6–9 but only about half achieve sustained seizure freedom10,11. Every year, hundreds will assume the risks of brain resection (including visual field cuts, naming deficits, infections) while continuing to have debilitating seizures12. The task of identifying DRE patients who are likely to experience recurrent seizures after resection is vital but challenging; there is thus a critical need for accurate evidence-based prediction tools13.

Seizure outcome prediction tools for epilepsy surgery are available but accuracy is modest. In 2015, the Cleveland Clinic Epilepsy Center published a model that provides individualized risk prediction of seizure freedom after brain resection based on historical cohorts14. The current iteration of the nomogram utilizes simplified clinical variables such as baseline seizure burden, presence of MRI abnormalities, and presence of interictal discharges on EEG to offer a personalized seizure-freedom prediction for a specific patient15. The nomogram is publicly available at https://riskcalc.org/ and is accessed several thousand times annually from around the world as an important adjunct in presurgical decision making and patient counseling. While the nomogram is the most widely used and validated epilepsy surgery outcomes prediction tool to our knowledge, the performance of the nomogram in terms of predictive accuracy remains modest (C-index 0.65, where 0.5 represents a model that is as predictive as a coin flip, and 1 represents a perfectly discriminatory model). The predictive power of the nomogram is limited in large part because the patient-specific granularity in terms of physiological data that is key to robust outcome prediction is lost when the patient is described in simplified categories (e.g. instead of incorporating the full brain MRI for a specific patient, the model is only able to incorporate the binary value of ‘normal’ or ‘abnormal’).

Several efforts have attempted to incorporate more granular “raw” data into predictive frameworks, with most focusing on adding different imaging data as inputs (primarily preoperative structural MRI and brain PET)16–22 (Table 1). Although these studies added valuable insights to our understanding of epilepsy, their translation to clinical practice has been challenging for two main reasons. First, most were developed using modalities that are not part of the routine presurgical evaluation- and thus not easily translated into prediction model inputs for widespread utilization- such as diffusion tensor imaging19, functional MRI23, and intracranial EEG20. Second, these studies have had an accuracy ceiling in the 70–80% range. No prior approaches have reached the elusive > 90% accuracy range wherein they would represent a promising investment of resources for development into a surgical outcomes prediction tool and none have been developed to a stage where they can be incorporated into the presurgical decision-making workflow.Table 1 Summary of prior prediction models for post-operative seizure outcome based on different features for model building.

Model	Prediction offered	N	Features used for model building	Performance metrics	
Jehi14,16	2 and 5 year seizure freedom probability, all epilepsy surgery (frontal/temporal/posterior quadrant)	N = 864 (development cohort), N = 604 (validation cohort)	6 clinical features (initial iteration in 2015)

Clinical features + simplified EEG features i.e. presence of non-localizable seizures on scalp EEG (yes/no), presence of bilateral interictal epileptiform discharges (updated iteration 2021)

	C-index 0.59

C-index 0.65

	
Gleichgerrcht19	Probability of seizure freedom after TLE surgery, either resection or laser interstitial ablation, > 1 year	168	Connectomes built using DTI (Note: DTI is not part routine standard of care in epilepsy surgery evaluation)	AUC 0.88	
Sherman16	Probability of seizure freedom after temporal lobe surgery, median 34 months	435	Clinical variables plus structural MRI data	C-index 0.664 (left sided surgery), 0.703 (right sided surgery)	
Li20	Probability of seizure freedom 1 year after temporal or extra-temporal resection in patients who had undergone implanted EEG evaluation	91	Multiple features from invasive EEG, most importantly ‘neural fragility’	AUC = 0.88	
Sinclair17	Probably of seizure freedom after temporal lobe surgery, > 2 years	82	Preoperative structural MRI and FDG-PET MRI (Note: FDG-PET MRI is not part routine standard of care in epilepsy surgery evaluation)	AUC 0.62	
Varatharajah25	Probability of seizure freedom after temporal lobectomy	N = 41 (Mayo Clinic), N = 23 (Cleveland Clinic)	60 s of visually inspected “normal” scalp EEG, interictal	AUC 0.78	
Thomas21	Probability of seizure freedom after a brain resection in patients who had undergone intracranial EEG	83	Rate of epileptic spikes with preceding gamma activity in wakefulness based on invasive EEG recordings	AUC 0.76	
Miron22	Probability of seizure freedom after one year after temporal or extra-temporal resection	47	EEG features from foramen ovale EEG and peg electrodes	AUC = 0.74	

In 2013, we analyzed data from patients who had undergone intracranial EEG evaluation with subsequent surgery (n = 23) and showed that preoperative functional connectivity could be used to predict postoperative seizure outcome; this formed the basis for using electrophysiological data to build post-operative seizure outcome prediction models24. Unlike intracranial EEG, however, scalp EEG is a non-invasive, inexpensive, universal component of the preoperative evaluation of every epilepsy surgery patient around the world; a model built using scalp EEG will thus be directly implementable. We investigated the use of inter-ictal scalp EEG as part of a seizure outcome prediction strategy25. When a Naïve Bayes classifier was built to predict postoperative seizure outcome based on power spectral density features from a “normal” pre-operative scalp EEG, we found that the model was able to perform with a meaningful accuracy (area under the ROC curve or ‘AUC’ of 0.78 on data from one center, and 0.76 on a validation dataset from another center). These results provided an important proof of concept that scalp EEG features could potentially play an important role in robust post-operative seizure outcome prediction, but to progress towards a clinical prediction framework we needed to find the right scalp EEG features captured at the optimal clinical moment for predicting outcome.

Based on pre-clinical studies, we hypothesized that the window of time immediately before and after a seizure (“peri-ictal” epoch) was likely to be uniquely informative for clinical outcome prediction. In a rat model with spontaneous recurrent seizures and measuring intracranial local field potentials, seizures were reliably preceded by an increase in theta synchrony between the hippocampus and prefrontal cortex in the 2 min before a seizure with prompt dissipation postictally26. Similarly, electrode recordings from the CA3 region of the hippocampus in a pilocarpine rat model of epilepsy showed that beginning minutes before a seizure, interneurons displayed progressive synchrony with oscillations in the theta, gamma, and finally ictal spiking frequencies27. These pre-clinical studies support a model of ictogenesis wherein electrophysiological brain activity in the minutes immediately before and after the ictal event is quantitatively and qualitatively distinct. Such a model is supported by observations from human patients who have undergone intracranial electrode studies as part of pre-surgical evaluation28–32. Based on these findings, we suspected that the differences in functional connectivity that would discriminate between patients with different postoperative outcomes would be most pronounced in the peri-ictal epoch.

We share the results of machine learning model-building experiments in a large sample (n = 294 surgical patients) to demonstrate that by using 5 min of peri-ictal scalp EEG data, it is possible to build a predictive framework of post-operative seizure outcome with accuracies significantly higher than those obtained from earlier approaches (> 90%) and within a range that is likely to be translatable into a clinically useful tool, all while using a data modality that is already part of standard presurgical evaluation globally (Fig. 1).Fig. 1 Schematic representation of development of AI-based epilepsy surgery outcome prediction models using peri-ictal scalp EEG. (1) Patients were diagnosed with drug resistant epilepsy of the temporal lobe after failing adequate trials of 2 anti-epileptic medications, (2) patients were admitted to the Cleveland Clinic Epilepsy Monitoring Unit for an inpatient video-EEG study in which multiple seizures were recorded, (3) one seizure was chosen at random for each patient, 2 min of immediately pre-ictal and 3 min of immediately post-ictal data were captured for each patient, (4) a machine-learning based, previously validated artifact detector was applied to the raw EEG data time-series, only artifact-free seconds of data were utilized for subsequent analyses, (5) the data was converted from a time-series to the frequency domain using spectral decomposition yielding electrode-wise power spectral density, (6) 75% of the dataset was used for model building using an Auto-ML pipeline which recommended models based on fourfold cross validation, 25% was used as a hold-out test set so that performance metrics based on unseen data could be reported.

Methods

Study population

Retrospective data were captured from patients who had undergone temporal lobe resection for drug resistant temporal lobe epilepsy at the Cleveland Clinic (Cleveland, OH) from 2011 to 2021. To be included in the study, patients had to have undergone a preoperative scalp EEG evaluation during an inpatient stay in the Cleveland Clinic Epilepsy Monitoring Unit (EMU) with a recorded seizure during that time. Post-surgical seizure outcome at the last follow-up was used as the basis of outcome classification. Patients who were seizure free postoperatively (equivalent to Engel Classification Class IA-D or International League Against Epilepsy ILAE Classes 1 and 2) were considered ‘surgical success’ cases while all others (Engel II-IV or ILAE 3–6) were considered ‘surgical failure’ cases33. To ascertain differences in the baseline characteristics of patients in the surgical success and surgical failure groups, we applied inferential tests; two-sided t tests were applied for comparison of means, Fisher Exact tests were applied for comparison of proportions when only two categories were present, Chi square tests were applied when more than 2 categories were present.

The study was conducted under an outcomes registry protocol of the Cleveland Clinic Foundation Institutional Review Board and all methods were reviewed and approved by the Cleveland Clinic Foundation Institutional Review Board (IRB reference number 16-1539). The informed consent requirement was waived, which was approved by the Cleveland Clinic Foundation Institutional Review Board.

EEG data acquisition

EEG studies at the Cleveland Clinic EMU were recorded using the Nihon Kohden JE-921A, JE-120A, and JE-208A headboxes (Nihon Kohden Corporation) using an extended 10–20 electrode placement scheme at a sampling rate of 200 Hz. The standard electrode placement at our center consists of 23 electrodes: Fz, Cz, Pz, Fp1, Fp3, C3, P3, O1, F7, T7, TP9, FT9, P7, Fp2, F4, C4, P4, O2, F8, T8, P8, FT10, TP10 (Supplementary Fig. 1). For certain analyses, we separately considered ‘temporal’ and ‘extra-temporal’ electrodes. For those analyses, the ‘temporal’ electrodes were TP10, FT10, P8, T9, P7, T8, F8, TP9, T7, F7. The standard reference electrodes for our headboxes are C3 and C4. No re-referencing was performed.

During inpatient EMU stays, it is typical for patients to have multiple seizures over several days of observation so a standardized strategy is required in order to decide which seizure should be selected for each patient. It is theoretically possible that seizures captured later in the course of a patient’s inpatient monitoring stay may have more or less value to an outcome prediction model compared to those captured earlier in the stay. Additionally, some patients have multiple seizure types as classified by scalp EEG. In a prior analysis of 386 temporal lobe epilepsy patients from multiple surgical centers (including Cleveland Clinic)15, we found that while the vast majority (72%) had a single type of ictal EEG pattern on preoperative evaluation, a minority had multiple ictal patterns (the presence of multiple ictal patterns was not an independent predictor of postoperative surgical outcome). We employed a randomization strategy in order to address these sources of data variability at the individual patient level. For each patient across the cohort, we selected a single seizure at random and captured the EEG file in European Data Format (EDF) for subsequent analysis. As a post-hoc analysis, we reviewed 50 seizures from each outcome group and found that randomization had been efficacious (i.e. no statistically significant difference between proportion of different seizure types between outcome groups, see Supplementary Table 1).

At our center, EEG files are annotated with ‘on’ and ‘off’ labels around the time of a seizure as part of the standard clinical workflow of patients undergoing epilepsy surgery evaluation. The ‘on’ label marks the time of seizure onset (i.e. time at which a trained EEG technologist can see the beginning of an organizing seizure activity on an EEG) while the ‘off’ label marks the post-ictal time point where no more organized seizure activity is ascertainable. The use of technician annotated data is reasonable as it has previously been shown in large prospective studies that interrater agreement for detecting seizures between trained EEG technologists and clinical neurophysiologists is almost perfect (i.e. > 95% in the context of epilepsy patients)34. We captured 2 min of data before the ‘on’ label (i.e. 2 min of pre-ictal data) and 3 min of data after the ‘off’ label (post-ictal data).

EEG preprocessing

Pre-processing of EEG data including removal of additional electrode channels and harmonization of electrode labels was performed using the MNE library in Python. Subsequent pre-processing (including automated artifact annotation, generation of power spectra, feature extraction) was performed in MATLAB (MathWorks).

Artifact detection and annotation was performed using an automated pipeline which we have previously reported35. Briefly, we use a trained and validated SVM classifier which samples the raw EEG time series data and annotates each second as either artifact free or artifactual. Subsequent analyses can then be performed exclusively on artifact-free data. When applied to the preictal data (2 min of raw data per patient), the artifact detector identified on average 77 s of artifact free data per patient in the surgical success group (95% CI 73–80) and 74 s in the surgical failure group (95% CI 71–79). When applied to the postictal data (3 min of raw data per patient), the artifact detector identified on average 88 s of artifact free data per patient for the surgical success group (95% CI 81–94) and 82 s for the surgical failure group (95% CI 76–87). A highpass filter (1 Hz) and notch filer (60 Hz) were applied (filtfilt function in MATLAB).

EEG features for machine learning

We utilized artifact free EEG data to generate power spectral density information for each patient from both the preictal and postictal epochs. Specifically, a periodogram was gathered from artifact-free segments for each channel and these were then averaged to produce the power spectral density across all channels in each frequency bin for a given patient using the first 40 frequency bins (1-40 Hz) for model building. PSD was generated using the periodogram function in MATLAB. This resulted in a [23 channel × 40 frequency bin] matrix containing preictal data, and another [23 channel × 40 frequency bin] matrix containing postictal data. A Z-score normalization was applied on a per electrode and per frequency basis for machine learning applications.

Clinical features

We included the nine clinical features from our previously published nomograms. These are: preoperative monthly seizure frequency, occurrence of generalized convulsion at any time before surgery (yes/no), cause of seizures (mesial temporal sclerosis/malformation of cortical development/stroke/tumor/other), years of epilepsy duration at time of surgery, gender (male/female), MRI findings (normal/abnormal), EEG seizure localization (always localizable/ sometimes not localizable), interictal epileptiform discharges (> 80% unilateral/ bilateral/ no interictal epileptiform discharges). In addition, we also included side of surgery (left/right), age at time of surgery in years, and follow-up period (at which seizure outcome was assigned) in years. In total, 12 clinical features were included.

Final data structure for ML applications

The preictal and postictal EEG data (which were in the form of two separate [23 × 40] arrays) were flattened and then horizontally concatenated into a single one-dimensional array [1 × 1840]. The 12 clinical variables were then horizontally concatenated to this array to make an “EEG plus clinical variables” array for each patient [1 × 1852]. The final data matrix for all 294 patients was thus created [294 patients × 1852 features].

Machine learning

ML was conducted in a Python environment. Consistent with recent trends in ML applications, we implemented an AutoML workflow to model building and used the Oracle Data Science platform36,37. Briefly, the AutoML utility is an automated method that fits multiple candidate classifier models to a dataset and uses a grid-search strategy to select an optimal set of hyperparameters and feature subsets based on a cross-validation strategy. The result of the AutoML implementation is a model that can be explored, validated, and optimized by the investigator based on the use case.

We implemented a two-fold approach to model validation. In the AutoML pipeline, we implemented a stratified k-fold cross validation (k = 4) strategy. In the context of this cross-validation strategy, accuracy was calculated as the quotient of out-of-fold predicted labels that matched the true label divided by the total number of samples. We also calculated area under the receiver operating curve (AUC-ROC), precision and recall. Secondly, we implemented an ‘out of group’ testing strategy wherein we built the model using a stratified training set containing 75% of the total dataset, and retained 25% of the dataset as an “out of group” testing set. This strategy is commonly used as a means of detecting overfitting of machine learning models; significant discrepancies between cross-validation based estimated accuracy and out-of-group testing based accuracy raise concern for an overfitted model. Train/test splitting was automated using the train_test_split functionality of the sklearn package. All of optimized machine learning classifiers reported in the results section can be replicated using publicly available Python packages. The necessary packages and optimized hyperparameters are detailed in Supplementary Table 2.

Decision curve analysis (DCA)

We implemented DCA as described by Vickers et al.38. DCA is an innovative approach supplanting statistical model parameters (e.g., AUC) with individual preference and procedure outcomes to quantify the clinical usefulness of a model. This is accomplished by calculating a clinical “net benefit” (NB) for one or more prediction models in comparison to default strategies of treating all or no patients, or treatment based on other tests:NetBenefitpt=TPN-FPN∗pt1-pt

where TP = true positives, FP = false positive, N = total number of patients, pt = probability threshold.

In effect, the Net Benefit is a single number that incorporates true positive rate while penalizing for the harms of false positives. The probability threshold is the probability at which a clinical decision maker would be indifferent between two possible actions. In DCA, the Net Benefit provided by a clinical prediction model is plotted over a range of clinically relevant threshold probabilities; curves that lie higher on the plot represent more clinically useful prediction strategies.To plot a DCA, the range of pt should be clinically defined a priori as the range within which a guidance on risk could be helpful: below the minimum pt, a temporal lobe resection is typically not recommended (success chance is too low); above the max pt, temporal lobe resection is usually offered; in-between is the gray area where a model could inform the decision. If a model has the highest net benefit across the entire pre-defined range of pts, it should be used when clinically feasible.

DCA also allows for estimation of the reduction in unnecessary interventions (i.e. likely unsuccessful surgeries that could have been avoided by using a given prediction model):Reductioninlikelyunsuccessful(i.e.avoidable)surgeries=NBofpredictionmodel-NBof′treatall′strategypt1-pt

DCA was implemented in Python using the dcurves 1.1.0 package.

Results

Sample characteristics

Our study sample consisted of young to middle-aged individuals with equal proportion of males and females (Table 2). Most patients had an abnormal preoperative brain MRI, consistent with the majority of drug resistant temporal lobe epilepsy cases that undergo surgery at epilepsy centers. Inferential testing did not reveal baseline differences between patients who went on to become ‘surgical success’ or ‘surgical failure’ cases. Nonetheless, surgical failure patients tended to be slightly younger with a higher tendency to have a normal preoperative brain MRI and non-localizable seizures on preoperative scalp EEG.Table 2 Patient characteristics.

Variable	Combined	Surgical success	Surgical failure	Adjusted p value	
N	294	170	124		
Age at surgery (years)	37.3 (15.3)	39.5 (15.5)	34.3 (14.7)	0.06	
Female sex (%)	50	49.4	50.8	1	
Abnormal MRI (%)	77.9	81.2	73.4	1	
Duration of epilepsy (years)	17.5 (13.5)	18.5 (14.5)	16 (11.8)	1	
Monthly seizure frequency	15.4 (46.3)	15.2 (53.7)	15.8 (33.6)	1	
Has had a GTC before surgery (%)	81.3	77.6	87.1	0.4	
Left sided surgery (%)	55.1	54.1	56.5	1	
Length of randomly selected seizure from preop EEG (minutes)	1.56 (1.43)	1.5 (1.48)	1.64 (1.34)	1	
Follow-up duration when outcome status defined (years)	3.42 (1.96)	3.42 (1.82)	3.42 (2.14)	1	
Causes of seizures (%)	MCD†	64	61.8	66.9	1	
MTS†	28.2	29.4	26.6	1	
Cryptogenic	7.5	7.6	7.3	1	
Presence of non-localizable seizures on preoperative EEG (%)	19.4	14.1	26.2	0.1	
Interictal discharges (%)	Bilateral epileptiform discharges	18.7	14.7	24.2	0.6	
Ipsilateral (atleast 80%)	72.8	78.2	65.3	
None	8.5	7.1	10.5	
Adjusted p values represent the result of a Bonferroni correction for 14 individual comparisons. For continuous variables, means are provided with standard deviations within brackets. †indicates that MCD and MTS were known etiologies of seizures either alone or in combination.

Visual comparison of spectral data

As an initial screening test, we investigated whether there were statistically significant differences between surgical success and surgical failure cases which we plotted as heatmaps on the basis of electrode-frequency pairs (Supplementary Fig. 2A) and electrode-frequency band pairs (Supplementary Fig. 2B). In the pre-ictal epoch, differences between the two outcome groups were most pronounced in the theta band and were distributed across electrode channels. In the post-ictal epoch, statistically significant differences were less apparent overall but nonetheless were discernable primarily in the delta and theta bands.

We plotted power spectral density across frequencies for both surgical success and surgical failure cases to ascertain visually apparent differences in between the two outcome groups on the basis of power spectra (Fig. 2). When average power spectra for all electrode channels were considered together, it was clear that the surgical failure cases tended to have higher power in the lower frequency bands (delta and theta) compared to surgical success cases, though this was only statistically significant in the theta band in the preictal epoch. When electrode channels were classified into temporal and extra-temporal electrodes, the same trend was recapitulated.Fig. 2 Plots of power spectral density (µV2/Hz) against Frequency (Hz). Surgical failure (red lines, n = 124) and surgical success (blue lines, n = 174) are plotted separately. (A–C) show results from preictal EEG data while (D–F) show results from postictal EEG data. (A,D) show average power spectral densities from all electrodes combined, (B,E) show averages from all temporal electrodes combined, (C,F) show averages from all extra-temporal electrodes combined. Shaded areas represent standard error of the mean. Inset tables show p values (adjusted for multiple comparisons for each table) of band-wise power spectral density for the averaged channels.

Figure 2 shows the differences in the absolute power spectral density for different outcome groups across frequencies. We also evaluated whether there were differences between surgical success and surgical failure groups on the basis of the proportional representation of each frequency band. To ascertain the proportional representation of a given frequency band, we found the area under the curve of the power spectral density versus frequency plot and calculated the proportional area for each frequency band. We then found the difference in the proportional representation of each frequency band between surgical success and surgical failure groups (Fig. 3). The surgical failure group had markedly higher proportional representation of the delta band in both preictal and postictal epochs though the trend was more pronounced in the postictal epoch. At the same time, the proportional representation of the gamma band was markedly lower in the surgical failure group than the surgical success group. Once again, this trend was seen in both the preictal and postictal setting but was more pronounced postictally.Fig. 3 Difference in proportional representation of frequency bands in preictal and postictal epochs in different electrode groups. The area under the curve of the power spectral density vs frequency plot (Fig. 2) was calculated and the proportion of the area for each frequency band was determined. The value on the y axis is calculated as [proportional representation of the frequency band in the surgical failure group] – [proportional representation of the frequency band in the surgical success group]. Error bars represent 95% confidence intervals for the difference between two proportions. (A–C) represent results from the preictal epoch, panels 3D-F represent results from the postictal epoch.

Machine learning applications

We built multiple binary classifiers to predict postoperative seizure outcome using scalp EEG derived power spectral density features using a grid-search based AutoML strategy. The best performing models in the AutoML pipeline were optimized and hold-out group testing was performed to evaluate model performance (Table 3, top 4 rows). Multiple classifier types can be fit to this data with excellent meaningful accuracy measures, suggesting that a sufficiently strong signal discriminating between ‘surgical success’ and ‘surgical failure’ patients is inherent in the peri-ictal EEG data such that multiple ML models are able to discriminate meaningfully between patients with different outcomes on the basis of these features. The best performing model is a version of the Light Gradient Booster Machine (LGBM), a model that has previously been extensively described39 and implemented in various EEG research contexts40–42. For a model built exclusively using peri-ictal scalp EEG features, the LGBM classifier had a mean accuracy (based on fourfold cross validation) of 94.5% and out-of-group testing set prediction accuracy of 91.9%. The winning model was based on 34 EEG features which were ranked in importance based on a permutation-based approach (Supplementary Table 3). In the winning model, 70% of features were from the pre-ictal period and 30% were from post-ictal period. 62% of features were from temporal electrodes while 38% were from extra-temporal electrodes. 56% of features were from the beta band, while 12%, 12%, 5%, and 3% were from gamma, theta, delta, and alpha bands respectively.Table 3 Summary of different binary classifier models to predict postoperative seizure outcomes after temporal lobe resection based on peri-ictal scalp EEG derived EEG features and/or clinical variables.

Model features	Candidate model	Accuracy	Precision	Recall	AUC of ROC	
Peri-ictal scalp EEG features only	LGBM, 34 EEG features	91.9	95.1	90.7	0.977	
Peri-ictal scalp EEG features only	RFC (with PCA, 57 features)	81.1	79.6	90.7	0.901	
Peri-ictal scalp EEG features only	CatBoost, 28 EEG features	90.5	89.1	95.3	0.988	
Peri-ictal scalp EEG features AND clinical variables from prevailing nomogram	RFC (with PCA for EEG features, 12 clinical features)	79.7	79.2	88.4	0.873	
Clinical variables from prevailing nomogram	Logistic Regression	62.1	67.4	67.4	0.642	
Accuracy, precision, recall, and AUC of ROC are based on testing of the model on an outcome-stratified hold-out testing set (25% of total data). LGBM: Light Gradient Booster Machine. RFC: Random Forest Classifier. CatBoost: Category Boost.

To quantify the discriminatory ability of the winning EEG-augmented model over a range of discrimination thresholds, we plotted the Receiver Operating Characteristic (ROC) curve (Fig. 4A). The ROC curve analysis shows that both the clinical variables model and the EEG augmented model perform better than chance, and the EEG-augmented model is the most discriminatory across the range of discriminatory thresholds. We also generated normalized confusion matrices to quantify the likelihood that erroneous predictions would be false positives (i.e. the model erroneously predicts seizure freedom) or false negatives (i.e. the model erroneously predicts seizure recurrence). On the hold-out testing set, the EEG augmented model was slightly more likely to erroneously predict seizure freedom (i.e. the model slightly overestimated the therapeutic effect of surgery, Fig. 4B,C).Fig. 4 Performance characteristics of EEG-augmented and non-augmented surgical outcome prediction models. (A) Receiver Operative Characteristic (ROC) curves showing performance of the EEG augmented models (Orange: Light Gradient Boost Machine, Red: CatBoost, Green: Random Forest Classifier) compared with a model build on clinical variables alone (Blue). (B) Normalized Confusion Matrix for EEG augmented outcome prediction model (shown matrix is for Light GBM model). Proportions in horizontal dimension sum to 1. (C) Normalized Confusion Matrix for non-EEG augmented outcome prediction model built on descriptive clinical features alone. Proportions in horizontal dimension sum to 1.

We performed Decision Curve Analysis (DCA) to quantify the clinical usefulness of the EEG-augmented outcome prediction approach in terms of net benefit across a range of probability thresholds (Fig. 5). We were most interested in the probability threshold range of 30–70% as this is the range in which an outcome prediction model is likely to be useful (we estimate that clinicians would be hesitant to change treatment plans based on automated outcome prediction approaches outside of this threshold range: For example, if the clinician is starting with an expectation, based on their clinical judgment, that the patient has less than a 30% chance of becoming seizure-free with surgery, it is unlikely that a model prediction will sway them into deciding to resect. If their baseline estimate is > 70%, they are likely to just proceed without needing a model to inform them further). Over this range of probability thresholds, the use of the EEG-augmented approach consistently increased net benefit compared to use of the clinical variables nomogram or a ‘treat all’ strategy (Fig. 5A). The use of the EEG-augmented approach also maximized the number of unnecessary surgeries that could be avoided (Fig. 5B). Across the relevant range of probability thresholds, the EEG-augmented approach would decrease the number of unnecessary surgeries by approximately 40% compared to a ‘treat all’ strategy and approximately 20% compared to an approach based on the clinical variables nomogram.Fig. 5 Decision curve analysis. (A) Net benefit is plotted against a range of threshold probabilities. Net benefit at a given threshold probability (pt) quantifies the ‘benefits’ (true positives) after accounting for the harms of false positives (weighted by the threshold probability); it is calculated as NB(pt) = TP − (FP × W))/N where TP = true positive, FP = false positive, W = “weighting factor” = (pt)/(1-(pt)), N = total number of patients. Threshold probability is the probability where a clinical decision-maker would be indifferent when choosing between two actions (i.e. to resect or not). Within the range of threshold probabilities in which the model is most likely to be utilized (30–70%, dashed vertical lines in A), the EEG augmented model (orange) clearly outperforms the non-EEG augmented model (orange curve is consistently higher than blue curve). (B) Net reduction in unnecessary surgeries is plotted against threshold probability for both outcome prediction models. Net reduction in avoidable surgeries for a prediction model (i.e. surgeries that the model would have correctly predicted to be unsuccessful) is calculated as (net benefit of using prediction model – net benefit of treat all strategy)/ (pt)/(1-(pt)). Within the range of threshold probabilities in which the model is most likely to be utilized (30–70%, dashed vertical lines in B), the EEG augmented model (orange) clearly outperforms the non-EEG augmented model (orange curve is consistently higher than blue curve).

Discussion

The peri-ictal window is particularly valuable for electrophysiological studies in DRE

The focus on the peri-ictal window (in our case, 2 min pre-ictal and 3 min post-ictal) is grounded in the existing science of seizure generation and propagation. Since atleast the 1990s, we have understood that there is a measurable electrophysiological synchronization event that occurs in the time immediately before a seizure31. Bartolomei et al. have reported compelling results supporting this notion from patients with mesial temporal lobe epilepsy who were undergoing implanted EEG evaluation32. They studied the synchronization of EEG signal between different mesial temporal structures (hippocampus, amygdala, and entorhinal cortex) and showed that in the window of time immediately before a seizure occurred (“before rapid discharge” or “BRD”), there was an observable synchronization event between mesial temporal structures. For our purposes, the key lesson from these early studies (which have since been replicated and augmented by others28–30) is that the periods of time immediately before and after a seizure represent a brain state that is both quantitatively and qualitatively distinct from the brain state hours before or after a seizure (the latter more commonly referred to as the “interictal” period.) Complementarily, we know from earlier work at our center that preoperative functional connectivity (based on implanted EEG electrodes) can be used to discriminate between patients who will go on to be surgical success or surgical failure cases24. By combining the insights from these studies, we hypothesized that focusing on the peri-ictal time period may be useful for post-operative seizure outcome prediction.

The value of peri-ictal EEG becomes particularly clear when the results of our present report are compared with those of earlier studies wherein we attempted to use inter-ictal EEG to predict postoperative seizure outcomes in DRE patients. When using visually reviewed ‘normal’ resting state pre-operative EEG (i.e. inter-ictal EEG taken hours before/after a seizure), our ML classifiers were able to predict post-operative seizure outcome with modest accuracy (AUC 0.78)25. The use of peri-ictal EEG substantially changed the predictive ability of the models (AUCs > 0.95 for multiple models). This comparison suggests that future electrophysiological investigations in DRE, particularly in terms of understanding differential responses to surgical therapies, should focus on the peri-ictal window.

Machine learning classifiers are able to discern differences in power spectral features that are not apparent on visual inspection with quantifiable accuracy

Direct visual comparison of the power spectral density plots of surgical success and surgical failure patients revealed minimal differences between the two groups (Fig. 2). However, multiple different ML classifiers were able to use power spectral features to accurately distinguish these patient groups. What is the marginal utility of using a machine learning classifier to interpret these EEG features rather than relying on the interpretation of a trained clinician? Firstly, physician interpretation of scalp EEG is an indispensable portion of the presurgical evaluation for the purpose of seizure localization but not postsurgical seizure outcome prediction. Our review of the peri-ictal scalp EEG features reveals that there are several dozen features that help discriminate between the two outcome groups with complex interdependencies (e.g. increased delta power in the temporal electrodes, increased theta power across the scalp, decreased proportional representation of the gamma band). A physician seeking to make a prediction of postsurgical outcome on the basis of a single preoperative scalp EEG would have to consider all these features simultaneously in order to make a prediction for any one patient. It is hard to imagine that a human interpreter would be able to make an accurate prediction given this underlying complexity. Secondly, the machine learning classifier offers the benefit of a consistent and quantifiable accuracy for each prediction that is made. The predictive accuracy of electrophysiologists interpreting clinical EEG data is difficult to quantify, highly variable, and likely to change as a function of experience and human factors (e.g. fatigue, bias)43–45. More generally, different expert epileptologists reviewing the same patient case are likely to give highly variable estimates of postoperative seizure outcome with poor correlation with real outcomes (concordance index 0.478)13.

A viable pathway towards a new family of presurgical outcome prediction tools

We present a novel approach to machine-learning enabled postoperative seizure control prediction in the context of DRE that is both highly accurate and makes use of data captured noninvasively and inexpensively during routine presurgical evaluation. Our use of machine learning in this context is complimentary to compelling work that has recently been published elsewhere within the domain of automated EEG analysis. A large, multicenter effort using over 30,000 EEG recordings found that an artificial intelligence model was able to detect EEG abnormalities with performance similar to human experts (AUC 0.89–0.96)46; this effort underscored the immense promise of applying machine learning models to scalp EEG. In this report, we aim to harness machine learning methods beyond EEG interpretation towards a surgical outcome prediction methodology.

The significance of these findings must be considered in the context of the prior efforts in this domain, which we have recently reviewed at length47. Prior approaches have attempted to use structural MRI data in temporal lobe cases (C index ~ 0.7, 435 patients)16 and frontal lobe cases (AUC 0.89, 90 patients)18 to predict seizure outcome. Others have reported on models built using FDG-PET MRI (AUC 0.62, 82 patients)17, and diffusion tenor imaging generated connectomic data (AUC 0.88, 168 patients)19. Some of the strongest predictive frameworks have been produced by using intracranial electrode recordings. Li et al. showed that ‘neural fragility’ derived from invasive EEG allowed for strong model building (n = 91, AUC 0.88)20. In the same vein, Thomas et al. have shown that the rate of epileptic spikes with preceding gamma activity in wakefulness captured on invasive EEG may be important in outcome prediction (n = 83, AUC 0.76)21. Recently, Miron et al. have reported similar model performance when using data from foramen ovale and peg electrodes (n = 47, AUC 0.74).22.

The field’s best efforts to develop automated seizure outcome prediction frameworks have been limited by two major factors. Firstly, the best performing models have been built using data types that are not part of the universal presurgical workflow (such as diffusion tensor imaging, foramen ovale electrodes, intracranial electrodes) which places an almost insurmountable barrier to generalizability. Secondly, models have been limited by an apparent accuracy ceiling which has limited their potential for clinical translation (most models have accuracies in the 70% range, with none surpassing the 90% threshold). The approach that we document in this report (i.e. the use of peri-ictal scalp EEG derived features in a machine-learning enabled predictive framework) overcomes both of these limitations and thus has the potential to achieve clinical translation into a useful presurgical tool.

EEG-augmented approach is likely to reduce avoidable surgeries

We have utilized Decision Curve Analysis (DCA) to evaluate the likely clinical usefulness of our approach. Since it was first described in 200638, DCA has been widely implemented48 and recognized as a meaningful way of quantifying and comparing the clinical usefulness of prediction models49–51, including within the context of neurosurgery52,53. When applied to our approach, DCA shows increased net benefit (i.e. true positives) when using an EEG-augmented model which translates into a 20–40% reduction in avoidable surgeries compared to decision-making strategies without the EEG-augmented model. Given that over 15% of patients who undergo brain resection for seizures experience a neurologic complication (with 4.7% experiencing a major complication, such as hemiparesis, language difficulties, or memory difficulties)54, such a reduction in avoidable brain resections is particularly significant.

Approach designed to optimize scientific rigor and reproducibility

We employ an automated machine-learning enabled artifact annotator when preprocessing the raw EEG data. The use of automation in this context not only allows for the rapid preprocessing of a large amount of data (in our case, ~ 1500 min of raw EEG data that would otherwise have had to undergo manual human inspection), it also ensures that a consistent standard for artifact status is applied to all data in the study thereby increasing reproducibility. We have also sought to improve rigor by conducting two independent and complimentary validation strategies (cross-validation and out-of-group model testing). Out-of-group testing is seen in only a minority of prior approaches as it requires a large enough dataset to justify data splitting.

Enabled by this highly automated pipeline, our report constitutes (to our knowledge) the largest en masse analysis of EEG data from epilepsy patients who have undergone brain resection.

Limitations and future directions

We present the results of a single-institution study. All EEG studies analyzed in this work were captured at a single institution as part of our institutional protocol for presurgical evaluation. While our protocols are similar to those used around the world, it is impossible to quantify how generalizable our results will be in other institutions without performing model building experiments on external data. Thus, the most important next step in the development of this approach will be the incorporation of EEG data from other institutions.

We focused our work on temporal lobe epilepsy cases as these represent the most frequently encountered surgical epilepsy in most academic centers. However, our approach can be easily expanded to extra-temporal epilepsies and this will represent an important future area for research.

Finally, the current work has focused on optimizing the prediction of seizure outcome after resective surgery. However, brain resection is not the only surgical option available for DRE. Non-resective (and thus less invasive) surgical options include vagal nerve stimulation, responsive neuromodulation, anterior nucleus of the thalamus deep brain stimulation (ANT-DBS), and laser interstitial ablation55,56. Though data on these non-resective therapies are still emerging, it is likely that for a carefully selected subset of patients, a non-resective approach may provide comparable benefit to resection57. In the future, prediction tools that are able to accurately identify patients who will fail to achieve seizure freedom after resection will be indispensable in steering patients towards non-resective treatments that are likely to provide comparable or greater benefit without the risk profile of resective surgery. As data on the long term efficacy of these alternative surgical treatments for drug resistant epilepsy emerge, our approach could be harnessed to provide comparative predictions for which of the multiple surgical treatments is most likely to benefit a specific patient on the basis of preoperative EEG and clinical features.

Conclusion

There remains a critical need for accurate seizure outcome prediction models to identify patients that are likely to have seizure recurrence after brain resection. A predictive framework that uses a combination of clinical variables and peri-ictal scalp EEG data may provide high accuracy predictions in this context. This approach is likely to be highly clinically translatable as scalp EEG is universally acquired for all surgical candidates and is non-invasive and inexpensive. Further work is required for external validation of this approach and for the implementation of the approach towards predictions for non-resective surgical epilepsy treatment.

Supplementary Information

Supplementary Information.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-72249-7.

Acknowledgements

As this work entered the final stages of revision, one of the senior co-authors, Dr Michael Kattan, tragically passed away. We dedicate this work to his memory and we recognize the immense impact of his life efforts on the care of patients and the development of young investigators. Mike, we promise to always follow the data.

Author contributions

SS, LJ, CYS conceived of the work. SS and ZM conducted data gathering, SS, SG, KSJ conducted data analysis and figure preparation, MK, RB, LJ, CYS provided critical domain expertise and review, SS prepared the manuscript drafts, all authors critically revised the manuscript, LJ and CYS are equal-contributor senior authors.

Data availability

Data analyzed in this work are from real patients who continue to undergo care at Cleveland Clinic Foundation. The authors are unable to publicly share clinical data (in the absence of an approved data sharing agreement) due to constraints of the Institutional Review Board approved protocol governing this work. Nonetheless, individual qualified investigators are welcome to contact the corresponding authors to discuss formal data-sharing agreements to assure compliance with patient privacy protection commitments; the authors are committed to accommodating reasonable requests in this regard.

Competing interests

The Cleveland Clinic Foundation has filed a provisional patent application for the key finding in this report (i.e. the use of peri-ictal EEG to predict post-operative seizure control in the context of epilepsy surgery), application number 63/671,400. Named inventors are Drs Sheikh, Jehi, and Saab. The remaining authors (McKee, Ghosn, Jeong, Kattan, Burgess) have no relevant disclosures.

Publisher's note

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

These authors contributed equally: Lara Jehi and Carl Y. Saab.
==== Refs
References

1. Kalilani L Sun X Pelgrims B Noack-Rink M Villanueva V The epidemiology of drug-resistant epilepsy: A systematic review and meta-analysis Epilepsia 2018 59 2179 2193 10.1111/epi.14596 30426482
Kalilani, L., Sun, X., Pelgrims, B., Noack-Rink, M. & Villanueva, V. The epidemiology of drug-resistant epilepsy: A systematic review and meta-analysis. Epilepsia 59, 2179–2193 (2018).30426482
2. Boylan L Depression but not seizure frequency predicts quality of life in treatment-resistant epilepsy Neurology 2004 62 258 261 10.1212/01.WNL.0000103282.62353.85 14745064
Boylan, L. et al. Depression but not seizure frequency predicts quality of life in treatment-resistant epilepsy. Neurology 62, 258–261 (2004).14745064
3. Sheikh SR Thompson N Frech F Malhotra M Jehi L Quantifying the burden of generalized tonic-clonic seizures in patients with drug-resistant epilepsy Epilepsia 2020 61 1627 1637 10.1111/epi.16603 32658343
Sheikh, S. R., Thompson, N., Frech, F., Malhotra, M. & Jehi, L. Quantifying the burden of generalized tonic-clonic seizures in patients with drug-resistant epilepsy. Epilepsia 61, 1627–1637. 10.1111/epi.16603 (2020).32658343
4. Begley, C. E. & Jeong, S. In Medication-Resistant Epilepsy: Diagnosis and Treatment (eds Stern, J. M., Sperling, M. & Sankar, R.) 27–33 (Cambridge University Press, 2020).
5. Trinka E Cause-specific mortality among patients with epilepsy: Results from a 30-year cohort study Epilepsia 2013 54 495 501 10.1111/epi.12014 23167828
Trinka, E. et al. Cause-specific mortality among patients with epilepsy: Results from a 30-year cohort study. Epilepsia 54, 495–501 (2013).23167828
6. Wiebe S Blume WT Girvin JP Eliasziw M A randomized, controlled trial of surgery for temporal-lobe epilepsy N. Engl. J. Med. 2001 345 311 318 10.1056/nejm200108023450501 11484687
Wiebe, S., Blume, W. T., Girvin, J. P. & Eliasziw, M. A randomized, controlled trial of surgery for temporal-lobe epilepsy. N. Engl. J. Med. 345, 311–318. 10.1056/nejm200108023450501 (2001).11484687
7. Engel J Jr Early surgical therapy for drug-resistant temporal lobe epilepsy: A randomized trial JAMA 2012 307 922 930 10.1001/jama.2012.220 22396514
Engel, J. Jr. et al. Early surgical therapy for drug-resistant temporal lobe epilepsy: A randomized trial. JAMA 307, 922–930. 10.1001/jama.2012.220 (2012).22396514
8. Dwivedi R Surgery for drug-resistant epilepsy in children N. Engl. J. Med. 2017 377 1639 1647 10.1056/NEJMoa1615335 29069568
Dwivedi, R. et al. Surgery for drug-resistant epilepsy in children. N. Engl. J. Med. 377, 1639–1647. 10.1056/NEJMoa1615335 (2017).29069568
9. Engel J Jr Practice parameter: Temporal lobe and localized neocortical resections for epilepsy: report of the Quality Standards Subcommittee of the American Academy of Neurology, in association with the American Epilepsy Society and the American Association of Neurological Surgeons Neurology 2003 60 538 547 10.1212/01.wnl.0000055086.35806.2d 12601090
Engel, J. Jr. et al. Practice parameter: Temporal lobe and localized neocortical resections for epilepsy: report of the Quality Standards Subcommittee of the American Academy of Neurology, in association with the American Epilepsy Society and the American Association of Neurological Surgeons. Neurology 60, 538–547. 10.1212/01.wnl.0000055086.35806.2d (2003).12601090
10. Téllez-Zenteno JF Dhar R Wiebe S Long-term seizure outcomes following epilepsy surgery: A systematic review and meta-analysis Brain 2005 128 1188 1198 10.1093/brain/awh449 15758038
Téllez-Zenteno, J. F., Dhar, R. & Wiebe, S. Long-term seizure outcomes following epilepsy surgery: A systematic review and meta-analysis. Brain 128, 1188–1198 (2005).15758038
11. Noe K Long-term outcomes after nonlesional extratemporal lobe epilepsy surgery JAMA Neurol. 2013 70 1003 1008 10.1001/jamaneurol.2013.209 23732844
Noe, K. et al. Long-term outcomes after nonlesional extratemporal lobe epilepsy surgery. JAMA Neurol. 70, 1003–1008 (2013).23732844
12. Hader WJ Complications of epilepsy surgery—a systematic review of focal surgical resections and invasive EEG monitoring Epilepsia 2013 54 840 847 10.1111/epi.12161 23551133
Hader, W. J. et al. Complications of epilepsy surgery—a systematic review of focal surgical resections and invasive EEG monitoring. Epilepsia 54, 840–847 (2013).23551133
13. Gracia CG Predicting seizure freedom after epilepsy surgery, a challenge in clinical practice Epilepsy Behav. 2019 95 124 130 10.1016/j.yebeh.2019.03.047 31035104
Gracia, C. G. et al. Predicting seizure freedom after epilepsy surgery, a challenge in clinical practice. Epilepsy Behav. 95, 124–130 (2019).31035104
14. Jehi L Development and validation of nomograms to provide individualised predictions of seizure outcomes after epilepsy surgery: A retrospective analysis Lancet Neurol. 2015 14 283 290 10.1016/S1474-4422(14)70325-4 25638640
Jehi, L. et al. Development and validation of nomograms to provide individualised predictions of seizure outcomes after epilepsy surgery: A retrospective analysis. Lancet Neurol. 14, 283–290 (2015).25638640
15. Fitzgerald Z Improving the prediction of epilepsy surgery outcomes using basic scalp EEG findings Epilepsia 2021 62 2439 2450 10.1111/epi.17024 34338324
Fitzgerald, Z. et al. Improving the prediction of epilepsy surgery outcomes using basic scalp EEG findings. Epilepsia 62, 2439–2450 (2021).34338324
16. Morita-Sherman M Incorporation of quantitative MRI in a model to predict temporal lobe epilepsy surgery outcome Brain Commun. 2021 3 fcab164 10.1093/braincomms/fcab164 34396113
Morita-Sherman, M. et al. Incorporation of quantitative MRI in a model to predict temporal lobe epilepsy surgery outcome. Brain Commun. 3, fcab164 (2021).34396113
17. Sinclair B Machine learning approaches for imaging-based prognostication of the outcome of surgery for mesial temporal lobe epilepsy Epilepsia 2022 63 1081 1092 10.1111/epi.17217 35266138
Sinclair, B. et al. Machine learning approaches for imaging-based prognostication of the outcome of surgery for mesial temporal lobe epilepsy. Epilepsia 63, 1081–1092 (2022).35266138
18. Whiting AC Automated analysis of cortical volume loss predicts seizure outcomes after frontal lobectomy Epilepsia 2021 62 1074 1084 10.1111/epi.16877 33756031
Whiting, A. C. et al. Automated analysis of cortical volume loss predicts seizure outcomes after frontal lobectomy. Epilepsia 62, 1074–1084 (2021).33756031
19. Gleichgerrcht E Temporal lobe epilepsy surgical outcomes can be inferred based on structural connectome hubs: A machine learning study Ann. Neurol. 2020 88 970 983 10.1002/ana.25888 32827235
Gleichgerrcht, E. et al. Temporal lobe epilepsy surgical outcomes can be inferred based on structural connectome hubs: A machine learning study. Ann. Neurol. 88, 970–983. 10.1002/ana.25888 (2020).32827235
20. Li A Neural fragility as an EEG marker of the seizure onset zone Nat. Neurosci. 2021 24 1465 1474 10.1038/s41593-021-00901-w 34354282
Li, A. et al. Neural fragility as an EEG marker of the seizure onset zone. Nat. Neurosci. 24, 1465–1474 (2021).34354282
21. Thomas J A subpopulation of spikes predicts successful epilepsy surgery outcome Ann. Neurol. 2023 93 522 535 10.1002/ana.26548 36373178
Thomas, J. et al. A subpopulation of spikes predicts successful epilepsy surgery outcome. Ann. Neurol. 93, 522–535 (2023).36373178
22. Miron G Müller PM Holtkamp M Meisel C Prediction of epilepsy surgery outcome using foramen ovale EEG—A machine learning approach Epilepsy Res. 2023 191 107111 10.1016/j.eplepsyres.2023.107111 36857943
Miron, G., Müller, P. M., Holtkamp, M. & Meisel, C. Prediction of epilepsy surgery outcome using foramen ovale EEG—A machine learning approach. Epilepsy Res. 191, 107111 (2023).36857943
23. An D Electroencephalography/functional magnetic resonance imaging responses help predict surgical outcome in focal epilepsy Epilepsia 2013 54 2184 2194 10.1111/epi.12434 24304438
An, D. et al. Electroencephalography/functional magnetic resonance imaging responses help predict surgical outcome in focal epilepsy. Epilepsia 54, 2184–2194 (2013).24304438
24. 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 (2013).24205027
25. Varatharajah Y Quantitative analysis of visually reviewed normal scalp EEG predicts seizure freedom following anterior temporal lobectomy Epilepsia 2022 63 1630 1642 10.1111/epi.17257 35416285
Varatharajah, Y. et al. Quantitative analysis of visually reviewed normal scalp EEG predicts seizure freedom following anterior temporal lobectomy. Epilepsia 63, 1630–1642 (2022).35416285
26. Broggini ACS Esteves IM Romcy-Pereira RN Leite JP Leao RN Pre-ictal increase in theta synchrony between the hippocampus and prefrontal cortex in a rat model of temporal lobe epilepsy Exp. Neurol. 2016 279 232 242 10.1016/j.expneurol.2016.03.007 26953232
Broggini, A. C. S., Esteves, I. M., Romcy-Pereira, R. N., Leite, J. P. & Leao, R. N. Pre-ictal increase in theta synchrony between the hippocampus and prefrontal cortex in a rat model of temporal lobe epilepsy. Exp. Neurol. 279, 232–242 (2016).26953232
27. Grasse DW Karunakaran S Moxon KA Neuronal synchrony and the transition to spontaneous seizures Exp. Neurol. 2013 248 72 84 10.1016/j.expneurol.2013.05.004 23707218
Grasse, D. W., Karunakaran, S. & Moxon, K. A. Neuronal synchrony and the transition to spontaneous seizures. Exp. Neurol. 248, 72–84 (2013).23707218
28. Guye M The role of corticothalamic coupling in human temporal lobe epilepsy Brain 2006 129 1917 1928 10.1093/brain/awl151 16760199
Guye, M. et al. The role of corticothalamic coupling in human temporal lobe epilepsy. Brain 129, 1917–1928 (2006).16760199
29. Ponten S Bartolomei F Stam C Small-world networks and epilepsy: Graph theoretical analysis of intracerebrally recorded mesial temporal lobe seizures Clin. Neurophysiol. 2007 118 918 927 10.1016/j.clinph.2006.12.002 17314065
Ponten, S., Bartolomei, F. & Stam, C. Small-world networks and epilepsy: Graph theoretical analysis of intracerebrally recorded mesial temporal lobe seizures. Clin. Neurophysiol. 118, 918–927 (2007).17314065
30. Bartolomei, F. & Wendling, F. Synchrony in neural networks underlying seizure generation in human partial epilepsies. Coordinated activity in the brain: measurements and relevance to brain function and behavior, 137–147 (2009).
31. Duckrow RB Spencer SS Regional coherence and the transfer of ictal activity during seizure onset in the medial temporal lobe Electroencephalogr. Clin. Neurophysiol. 1992 82 415 422 10.1016/0013-4694(92)90046-K 1375548
Duckrow, R. B. & Spencer, S. S. Regional coherence and the transfer of ictal activity during seizure onset in the medial temporal lobe. Electroencephalogr. Clin. Neurophysiol. 82, 415–422. 10.1016/0013-4694(92)90046-K (1992).1375548
32. Bartolomei F Pre-ictal synchronicity in limbic networks of mesial temporal lobe epilepsy Epilepsy Res. 2004 61 89 104 10.1016/j.eplepsyres.2004.06.006 15451011
Bartolomei, F. et al. Pre-ictal synchronicity in limbic networks of mesial temporal lobe epilepsy. Epilepsy Res. 61, 89–104 (2004).15451011
33. Wieser H Commission on Neurosurgery of the International League Against Epilepsy (ILAE). ILAE Commission Report. Proposal for a new classification of outcome with respect to epileptic seizures following epilepsy surgery Epilepsia 2001 42 282 286 10.1046/j.1528-1157.2001.4220282.x 11240604
Wieser, H. et al. Commission on Neurosurgery of the International League Against Epilepsy (ILAE). ILAE Commission Report. Proposal for a new classification of outcome with respect to epileptic seizures following epilepsy surgery. Epilepsia 42, 282–286 (2001).11240604
34. Beuchat I Prospective evaluation of interrater agreement between EEG technologists and neurophysiologists Sci. Rep. 2021 11 13406 10.1038/s41598-021-92827-3 34183718
Beuchat, I. et al. Prospective evaluation of interrater agreement between EEG technologists and neurophysiologists. Sci. Rep. 11, 13406 (2021).34183718
35. Levitt J Automated detection of electroencephalography artifacts in human, rodent and canine subjects using machine learning J. Neurosci. Methods 2018 307 53 59 10.1016/j.jneumeth.2018.06.014 29944891
Levitt, J. et al. Automated detection of electroencephalography artifacts in human, rodent and canine subjects using machine learning. J. Neurosci. Methods 307, 53–59 (2018).29944891
36. Karmaker SK Automl to date and beyond: Challenges and opportunities ACM Comput. Surv. 2021 54 1 36 10.1145/3470918
Karmaker, S. K. et al. Automl to date and beyond: Challenges and opportunities. ACM Comput. Surv. 54, 1–36 (2021).
37. Yakovlev A Oracle automl: A fast and predictive automl pipeline Proc. VLDB Endow. 2020 13 3166 3180 10.14778/3415478.3415542
Yakovlev, A. et al. Oracle automl: A fast and predictive automl pipeline. Proc. VLDB Endow. 13, 3166–3180 (2020).
38. Vickers AJ Elkin EB Decision curve analysis: A novel method for evaluating prediction models Med. Decis. Mak. 2006 26 565 574 10.1177/0272989x06295361
Vickers, A. J. & Elkin, E. B. Decision curve analysis: A novel method for evaluating prediction models. Med. Decis. Mak. 26, 565–574. 10.1177/0272989x06295361 (2006).
39. Ke, G. et al. Lightgbm: A highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst. 30 (2017).
40. Zeng H A lightGBM-based EEG analysis method for driver mental states classification Comput. Intell. Neurosci. 2019 2019 1 11 10.1155/2019/3761203
Zeng, H. et al. A lightGBM-based EEG analysis method for driver mental states classification. Comput. Intell. Neurosci. 2019, 1–11 (2019).
41. Abenna S Nahid M Bajit A Motor imagery based brain-computer interface: Improving the EEG classification using Delta rhythm and LightGBM algorithm Biomed. Signal Process. Control 2022 71 103102 10.1016/j.bspc.2021.103102
Abenna, S., Nahid, M. & Bajit, A. Motor imagery based brain-computer interface: Improving the EEG classification using Delta rhythm and LightGBM algorithm. Biomed. Signal Process. Control 71, 103102 (2022).
42. Pan H The LightGBM-based classification algorithm for Chinese characters speech imagery BCI system Cogn. Neurodyn. 2023 17 373 384 10.1007/s11571-022-09819-w 37007202
Pan, H. et al. The LightGBM-based classification algorithm for Chinese characters speech imagery BCI system. Cogn. Neurodyn. 17, 373–384 (2023).37007202
43. Noe K Most experts agree … but what about other EEG readers? Epilepsy Curr. 2020 20 78 79 10.1177/1535759720901511 32313500
Noe, K. Most experts agree … but what about other EEG readers?. Epilepsy Curr. 20, 78–79. 10.1177/1535759720901511 (2020).32313500
44. Benbadis SR Interrater reliability of EEG-video monitoring Neurology 2009 73 843 846 10.1212/WNL.0b013e3181b78425 19752450
Benbadis, S. R. et al. Interrater reliability of EEG-video monitoring. Neurology 73, 843–846 (2009).19752450
45. Grant AC EEG interpretation reliability and interpreter confidence: A large single-center study Epilepsy Behav. 2014 32 102 107 10.1016/j.yebeh.2014.01.011 24531133
Grant, A. C. et al. EEG interpretation reliability and interpreter confidence: A large single-center study. Epilepsy Behav. 32, 102–107 (2014).24531133
46. Tveit J Automated interpretation of clinical electroencephalograms using artificial intelligence JAMA Neurol. 2023 80 805 812 10.1001/jamaneurol.2023.1645 37338864
Tveit, J. et al. Automated interpretation of clinical electroencephalograms using artificial intelligence. JAMA Neurol. 80, 805–812. 10.1001/jamaneurol.2023.1645 (2023).37338864
47. Sheikh S Jehi L Predictive models of epilepsy outcomes Curr. Opin. Neurol. 2024 37 115 120 10.1097/wco.0000000000001241 38224138
Sheikh, S. & Jehi, L. Predictive models of epilepsy outcomes. Curr. Opin. Neurol. 37, 115–120. 10.1097/wco.0000000000001241 (2024).38224138
48. Capogrosso P Vickers AJ A systematic review of the literature demonstrates some errors in the use of decision curve analysis but generally correct interpretation of findings Med. Decis. Mak. 2019 39 493 498 10.1177/0272989x19832881
Capogrosso, P. & Vickers, A. J. A systematic review of the literature demonstrates some errors in the use of decision curve analysis but generally correct interpretation of findings. Med. Decis. Mak. 39, 493–498. 10.1177/0272989x19832881 (2019).
49. Localio AR Goodman S Beyond the usual prediction accuracy metrics: Reporting results for clinical decision making Ann. Intern. Med. 2012 157 294 295 10.7326/0003-4819-157-4-201208210-00014 22910942
Localio, A. R. & Goodman, S. Beyond the usual prediction accuracy metrics: Reporting results for clinical decision making. Ann. Intern. Med. 157, 294–295. 10.7326/0003-4819-157-4-201208210-00014 (2012).22910942
50. Fitzgerald M Saville BR Lewis RJ Decision curve analysis JAMA 2015 313 409 410 10.1001/jama.2015.37 25626037
Fitzgerald, M., Saville, B. R. & Lewis, R. J. Decision curve analysis. JAMA 313, 409–410. 10.1001/jama.2015.37 (2015).25626037
51. Vickers AJ Van Calster B Steyerberg EW Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests BMJ 2016 352 i6 10.1136/bmj.i6 26810254
Vickers, A. J., Van Calster, B. & Steyerberg, E. W. Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests. BMJ 352, i6. 10.1136/bmj.i6 (2016).26810254
52. Mijderwijk HJ Nieboer D Is my clinical prediction model clinically useful? A primer on decision curve analysis Acta Neurochir. Suppl. 2022 134 115 118 10.1007/978-3-030-85292-4_15 34862535
Mijderwijk, H. J. & Nieboer, D. Is my clinical prediction model clinically useful? A primer on decision curve analysis. Acta Neurochir. Suppl. 134, 115–118. 10.1007/978-3-030-85292-4_15 (2022).34862535
53. Vickers AJ Holland F Decision curve analysis to evaluate the clinical benefit of prediction models Spine J. 2021 21 1643 1648 10.1016/j.spinee.2021.02.024 33676020
Vickers, A. J. & Holland, F. Decision curve analysis to evaluate the clinical benefit of prediction models. Spine J. 21, 1643–1648. 10.1016/j.spinee.2021.02.024 (2021).33676020
54. Hader WJ Complications of epilepsy surgery—A systematic review of focal surgical resections and invasive EEG monitoring Epilepsia 2013 54 840 847 10.1111/epi.12161 23551133
Hader, W. J. et al. Complications of epilepsy surgery—A systematic review of focal surgical resections and invasive EEG monitoring. Epilepsia 54, 840–847. 10.1111/epi.12161 (2013).23551133
55. Ryvlin P Rheims S Hirsch LJ Sokolov A Jehi L Neuromodulation in epilepsy: State-of-the-art approved therapies Lancet Neurol. 2021 20 1038 1047 10.1016/S1474-4422(21)00300-8 34710360
Ryvlin, P., Rheims, S., Hirsch, L. J., Sokolov, A. & Jehi, L. Neuromodulation in epilepsy: State-of-the-art approved therapies. Lancet Neurol. 20, 1038–1047 (2021).34710360
56. Kang JY Laser interstitial thermal therapy for medically intractable mesial temporal lobe epilepsy Epilepsia 2016 57 325 334 10.1111/epi.13284 26697969
Kang, J. Y. et al. Laser interstitial thermal therapy for medically intractable mesial temporal lobe epilepsy. Epilepsia 57, 325–334 (2016).26697969
57. Nair DR Nine-year prospective efficacy and safety of brain-responsive neurostimulation for focal epilepsy Neurology 2020 95 e1244 e1256 10.1212/WNL.0000000000010154 32690786
Nair, D. R. et al. Nine-year prospective efficacy and safety of brain-responsive neurostimulation for focal epilepsy. Neurology 95, e1244–e1256 (2020).32690786
