---
title: "EEG-ECG Conditional Spatial Classification of Interictal Epileptiform Discharges with SHAP Interpr"
id: "plos-one-11-conditional-spatial-classification-of-expert-confirmed-interictal-epileptiform"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-11-conditional-spatial-classification-of-expert-confirmed-interictal-epileptiform"
content_type: "clinical_feed_article"
specialty: "Neurology"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358782"
published_at: "2026-09-22T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# EEG-ECG Conditional Spatial Classification of Interictal Epileptiform Discharges with SHAP Interpr
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-11-conditional-spatial-classification-of-expert-confirmed-interictal-epileptiform
- **Specialty:** [Neurology](https://medichelpline.com/clinical-feed/neurology.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358782)
- **Published At:** 2026-09-22T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- The study analyzed 2,514 expert-confirmed four-second **interictal epileptiform discharge (IED)** epochs labeled into five scalp-distribution categories: generalized, frontal, temporal, occipital, and centro-parietal. - The analysis framed spatial labeling as a conditional downstream classification problem that begins after an epoch is already confirmed to contain an IED, separating this task from IED detection. - Three channel configurations were compared under identical preprocessing and training partitions: 19-channel scalp **EEG**; 21-channel EEG plus two **ECG** channels; and a full 29-channel input including referential and EMG channels. - Each included channel produced 26 handcrafted time-, frequency-, and nonlinear features; identical stratified epoch-level partitions and training-only SMOTE were used across models. - Multiple machine-learning classifiers were evaluated. Linear discriminant analysis achieved the highest EEG-only test accuracy (88.89%). - Adding ECG channels increased descriptive test accuracy for all eight directly comparable classifiers; CatBoost reached 93.25% with EEG+ECG and 94.44% with the full 29-channel input. These increases were descriptive and not subjected to formal paired significance testing. - **SHAP** analysis was applied to fitted models to identify the channel-feature variables driving model predictions. In the CatBoost EEG+ECG model, ECG channels RA and LA accounted for 15.79% and 15.12% of normalized global attribution, respectively. - The beta-band power feature family was the single largest contributor in SHAP attributions, accounting for 18.76% of normalized global importance in the CatBoost EEG+ECG model. - The study intentionally refrains from claiming physiological localization, unseen-patient generalization, or clinical ECG biomarkers; SHAP attributions are interpreted as model-specific predictive contributions only. - Data used were publicly available; no new data collection or study funding was reported.
## Clinical Analysis & Structured Key Points
Conditional spatial classification of expert-confirmed interictal epileptiform discharge epochs: An EEG-ECG ablation and SHAP analysis | PLOS One Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click here . Article Authors Metrics Comments Media Coverage Reader Comments Figures Figures Abstract Interictal epileptiform discharges (IEDs) are diagnostically important EEG abnormalities, but after an epoch has already been confirmed as containing IED, a separate methodological question remains whether its scalp distribution can be assigned among predefined categories, and do routinely co-recorded auxiliary channels alter that classification. This conditional five-class tasks were evaluated using 2,514 expert-confirmed four-second IED epochs labelled as generalized, frontal, temporal, occipital, or centro-parietal. Identical stratified epoch-level partitions, training-only SMOTE, 26 handcrafted features per included channel, and multiple machine-learning classifiers were used across a staged channel ablation comparing 19-channel scalp EEG, 21-channel EEG with two ECG channels, and the complete 29-channel input containing scalp EEG, referential, ECG, and EMG channels. Linear discriminant analysis achieved the best EEG-only test accuracy (88.89%), whereas CatBoost achieved 93.25% on EEG with ECG signals and 94.44% with the whole channel. All eight directly comparable classifiers showed numerically higher test accuracy after ECG was added, although these differences were descriptive and were not subjected to formal paired significance testing. SHAP analysis identified the key channel-feature variables influencing the fitted models, complementing performance ablation. In the EEG with ECG on CatBoost model, ECG channels RA and LA contributed 15.79% and 15.12% of normalized global attribution, respectively, while beta-band power was the most important feature family, accounting for 18.76%. The study therefore provides a controlled analysis of downstream spatial categorization and auxiliary-channel dependence within expert-confirmed IED epochs. The results do not establish physiological localization, unseen-patient generalization, or a clinically validated ECG biomarker. Citation: Plabon AM, Mukit A, Neyamul M, Jehady OF, Zuba FT, Mina MF, et al. (2026) Conditional spatial classification of expert-confirmed interictal epileptiform discharge epochs: An EEG-ECG ablation and SHAP analysis. PLoS One 21(9): e0358782. https://doi.org/10.1371/journal.pone.0358782 Editor: Teppei Matsubara, Athinoula A Martinos Center for Biomedical Imaging, UNITED STATES OF AMERICA Received: November 27, 2025; Accepted: September 4, 2026; Published: September 22, 2026 Copyright: © 2026 Plabon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: No new data were collected. Publicly available data. Data link: https://doi.org/10.6084/m9.figshare.28069568.v2 . Funding: The author(s) received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. Introduction Epilepsy is a neurological disorder characterized by recurrent unprovoked seizures arising from abnormal neuronal activity [ 1 ]. Electroencephalography (EEG) is central to clinical assessment because it records cerebral electrical activity and can reveal interictal epileptiform discharges (IEDs) between seizures [ 2 , 3 ]. Beyond the presence of an IED, its morphology and scalp distribution contribute to electroclinical description and annotation [ 4 ]. The scalp-distribution label, however, should not be equated with source localization, the seizure-onset zone, or the epileptogenic zone. In the source dataset, expert-confirmed IEDs were organized into five predefined scalp-distribution categories: generalized, frontal, temporal, occipital, and centro-parietal [ 5 ]. Once an epoch has already been identified as containing an IED, assigning such a distribution label becomes a distinct downstream classification problem. Studying this conditional task is methodologically useful because it isolates discrimination among expert-defined spatial categories from the separate problem of deciding whether an IED is present at all. It can therefore provide a controlled benchmark for downstream annotation and for examining what recorded channel groups contribute to the separation of these labels. Throughout this article, “spatial classification” refers only to these predefined scalp-distribution categories and not to source reconstruction or localization of the seizure-onset zone. IED detection and conditional spatial classification address different questions [ 6 , 7 ]. Detection evaluates whether unselected EEG contains an IED in the presence of non-IED background, artifacts, and IED-like transients [ 8 ]. The present analysis begins only after expert confirmation of IED presence and asks how the confirmed epoch is categorized among five scalp-distribution labels. This restricted design intentionally holds the detection decision constant so that channel-set and model effects can be examined within the IED-positive subset. Machine learning provides a practical analytical framework for this task because each epoch is represented by hundreds of channel-wise time-domain, frequency-domain, and nonlinear descriptors, and the mapping from these variables to five labels may be multivariate and model-dependent. Evaluating several classifier families under identical data partitions allows the study to test whether the conditional categories are separable from the handcrafted representation and whether observed channel-set effects are consistent across different modelling assumptions. The purpose is not to replace expert IED detection, but to evaluate a high-dimensional downstream categorization problem after expert annotation. The source recordings include scalp EEG together with referential, electrocardiogram (ECG), and electromyogram (EMG) channels [ 5 ]. ECG was evaluated because it is a simultaneously recorded auxiliary modality that can be added without changing the IED labels or scalp EEG channels. The EEG-versus-EEG with ECG comparison therefore asks a focused ablation question whether does the fitted classifier obtain additional discriminatory information when the two ECG channels are available. This test does not assume that any gain is autonomic or physiologically localizing; rather, it helps determine whether the model depends on information outside scalp EEG and highlights the need to consider about patient, state, or recording-specific structure when interpreting multimodal models. The subsequent full-input comparison jointly adds four referential and four EMG channels and consequently cannot separate their individual effects. SHapley Additive exPlanations (SHAP) were incorporated to answer a complementary question to the channel ablation [ 9 ]. Ablation quantifies whether predictive performance changes when a channel group is added, whereas SHAP identifies which channel-feature variables a fitted model uses to produce its outputs. This combination makes SHAP central to the interpretability aim of the study: it can show whether an apparent performance gain is accompanied by substantial model dependence on the added modality and which features account for that dependence. Because SHAP importance is conditional on the dataset, feature representation, model, and target [ 10 ], the resulting attributions are interpreted only as model-specific predictive contributions, not as physiological biomarkers, causal mechanisms, or evidence of clinical localization [ 11 ]. This caution is especially important for ECG and EMG variables, which may reflect patient identity, sleep-wake state, recording conditions, movement, contamination, or other dataset-specific structure [ 12 – 14 ]. Accordingly, this study aimed to: (1) evaluate whether expert-confirmed IED epochs can be separated among five predefined scalp-distribution categories using a common handcrafted-feature representation; (2) compare multiple machine-learning classifiers under identical epoch-level partitions; (3) quantify the descriptive performance change when two ECG channels are added to 19-channel scalp EEG; (4) compare EEG with ECG using the complete 29-channel input; and (5) utilize SHAP to characterize model dependence on specific channels and features, while keeping physiological and clinical claims explicitly outside the scope of the analysis. Methodology The study used a conditional spatial-classification pipeline restricted to expert-confirmed IED epochs in five predefined classes: generalized, frontal, temporal, occipital, and centro-parietal. The same preprocessing, channel-wise feature extraction, data partitions, imbalance handling, classifier evaluation, and SHAP framework were applied across channel configurations so that the ablation compared channel availability rather than different modelling pipelines. Proposed methodology For the proposed work, the same expert-confirmed IED epochs followed the workflow shown in Fig 1 . Twenty-six handcrafted features were extracted independently from every included channel. Three primary feature matrices were created: (1) 19 scalp EEG channels (19 x 26 = 494 variables); (2) 19 scalp EEG channels plus the two ECG channels, RA and LA (21 x 26 = 546 variables); and (3) the full 29-channel input comprising 19 scalp EEG, four referential channels, two ECG channels, and four EMG channels (29 x 26 = 754 variables). Each classifier received a flat vector of channel-feature variables. Electrode coordinates, adjacency matrices, inter-electrode distances, topographic maps, and source-reconstruction outputs were not used; spatial identity was represented only through channel-specific feature names. Download: PNG larger image TIFF original image Fig 1. Study workflow for preprocessing, channel specific feature extraction, conditional five-class spatial classification, channel set ablation and SHAP analysis. https://doi.org/10.1371/journal.pone.0358782.g001 Dataset There are 29 channels on the recordings: 19 scalp EEG channels positioned using the international 10–20 system illustrated in Fig 2 , four scalp referential channels (T1, T2, A1, and A2), 2 channels for ECG (RA and LA), and 4 channels for deltoid EMG (bilateral) depicted in Fig 3 . Table 1 lists the channel groups and the location where they were recorded. The ECG RA and ECG LA are referred to as lead24 and lead25 in the EEG with ECG SHAP outputs, while this was the original feature naming convention. Download: PNG larger image TIFF original image Table 1. Recording of various signal types including the position of electrodes. https://doi.org/10.1371/journal.pone.0358782.t001 Download: PNG larger image TIFF original image Fig 2. EEG electrode position and the predefined five spatial categories of IED. The “Fp” refers to frontopolar, “F” to frontal, “C” to central, “P” to parietal, “O” to occipital, and “T” to temporal locations. https://doi.org/10.1371/journal.pone.0358782.g002 Download: PNG larger image TIFF original image Fig 3. For an electrode arrangement in the ECG and EMG. The right and left arm ECGs are called RA and LA. Bilateral Deltoid EMG channels D1-D4 are indicated. https://doi.org/10.1371/journal.pone.0358782.g003 The open dataset was obtained from the Epilepsy Center of Peking Union Medical College Hospital [ 5 ] and contains recordings from 84 participants. Fifty-two recordings contained at least one expert-confirmed IED, whereas 32 recordings were reported as normal EEG. Each participant contributed 20 minutes of recording, giving 28 hours in total. The source dataset contains 22,933 four-second epochs not labelled as IEDs and 2,516 four-second epochs labelled as IEDs. During quality control, two IED epochs were discarded, leaving 2,514 expert-confirmed IED epochs for the present analysis. Non-IED epochs were not included as a target class because the study was designed specifically to evaluate conditional categorization after IED presence had already been established. The recordings and epoch labels were provided in MATLAB and NumPy formats. Table 2 summarizes the consciousness state of the original 2,516 epochs labelled as IEDs. Download: PNG larger image TIFF original image Table 2. Consciousness state across IED epochs at 4 sec. https://doi.org/10.1371/journal.pone.0358782.t002 EEG signal preprocessing The four-second epochs were linearly detrended, notch filtered at 50 Hz and 100 Hz, and band-pass filtered between 0.5 and 40 Hz using a Butterworth filter [ 15 ]. Each channel configuration underwent the same applicable preprocessing before feature-matrix construction. The composite criterion of robust log-variance outliers and low interchannel correlation was used to identify noisy or defective EEG channels. Detected bad EEG channels were interpolated from neighboring channels [ 13 ]. Independent component analysis was only used on the EEG channels to remove ocular artifacts while retaining the ECG and EMG signals [ 14 , 16 ]. All scalp EEG channels were then re-referenced to a common average. Two epochs of wake state were rejected due to the peak-to-peak amplitude being above a strong median absolute deviation (MAD) threshold. The final dataset therefore contained 2,514 IED epochs: 762 wake epochs (30.31%) and 1,752 sleep epochs (69.69%). Feature extraction For each retained four-second epoch, the same database of 26 handcrafted descriptors was computed independently for every included channel, yielding 494 variables for EEG only, 546 for EEG with ECG, and 754 for the full 29-channel input (scalp EEG, referential, ECG, and EMG). The features were grouped into time-domain, frequency-domain, and nonlinear categories. The same descriptor set was intentionally used across EEG and ECG channels to keep the representation fixed during channel-set ablation: if modality-specific feature engineering were introduced only for ECG, any performance change would reflect both the added channels and a changed feature representation. This design choice does not imply that EEG and ECG have equivalent physiology or that every descriptor has the same physiological interpretation across modalities; it provides a controlled, modality-agnostic statistical representation for the comparative analysis. Time-domain features. The variables used in time domain are mean, median, standard deviation, skewness, kurtosis, peak-to-peak amplitude, waveform length, slope sign changes, Hjorth mobility and Hjorth complexity. Frequency-domain features. Welch power spectral density estimates were used to compute frequency domain variables. Absolute band power was calculated for each band: delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz) and beta (13–30 Hz) as well as the ratio of each band power, peak frequency and median frequency. Nonlinear features. Nonlinear variables were approximate entropy, sample entropy, permutation entropy, spectral entropy, correlation dimension, detrended fluctuation analysis and the Hurst exponent [ 17 ]. Data splitting The 2,514 IED-labelled epochs were partitioned using a stratified 80:10:10 epoch-level split which contains 2,011 training epochs, 251 validation epochs, and 252 held-out test epochs. Class proportions were maintained, and identical epoch assignments were used across all channel configurations and classifiers. The partition was not patient-disjoint, so epochs from the same participant could occur in more than one subset. Consequently, the evaluation measures internal held-out-epoch performance and cannot establish generalization to unseen patients. A patient-disjoint or leave-one-patient-out design would be required for that stronger claim. Table 3 summarizes the distribution of the five IED categories in the retained dataset. Download: PNG larger image TIFF original image Table 3. Distribution of IEDs across five anatomical regions in the dataset. https://doi.org/10.1371/journal.pone.0358782.t003 Addressing class imbalance with SMOTE analysis Synthetic Minority Over-sampling Technique (SMOTE) was applied only to the training subset to reduce imbalance among the five IED categories [ 18 ]. Validation and test epochs were not resampled. Applying SMOTE after the split prevented synthetic information from entering the validation or test sets. For a minority-class feature vector , a synthetic vector was generated by interpolation between and a randomly selected nearest neighbour x_nn from the same class: (1) where: is the original minority-class sample; is a randomly selected nearest neighbour from the same class; Lambda is sampled from a uniform distribution on [0,1]; and is the synthetic sample on the line segment joining and . Machine learning classifier evaluation Machine learning was used here as a comparative framework for mapping the high-dimensional handcrafted feature vectors to five conditional spatial labels. The evaluated classifiers were XGBoost, LightGBM, CatBoost, multinomial logistic regression, linear discriminant analysis (LDA), decision tree, extra trees, random forest, and support vector machine (SVM). Including linear, tree-based, ensemble, and boosting approaches allowed the analysis to assess whether channel-set patterns were specific to one model family. Random-forest results were available for the EEG-only and EEG with ECG analyses, while eight classifiers had results for all three primary channel configurations. The same procedure and partitions were used for each ablation. Hyperparameters were selected using the validation set, and final performance was reported on the held-out test set. The performance was summarized in terms of overall accuracy, class-specific precision, recall and F1 score along with macro-averaged F1 score [ 16 ]. Macro-F1 was highlighted due to the fact that it gives equal importance to each spatial category irrespective of the class frequencies. Channel-set ablation design The channel-set ablation compared feature matrices derived from identical IED epochs and an unchanged feature-extraction pipeline. The EEG-only matrix contained 26 features from each of 19 scalp EEG channels. The EEG with ECG matrix added both ECG RA and ECG LA channels. The full matrix contained all 29 recorded channels: 19 scalp EEG, four referential, two ECG, and four EMG channels. Labels, split assignments, training-only SMOTE, classifier families, and evaluation procedures were held constant. Thus, the EEG-versus-EEG with ECG comparison describes the performance change associated with making ECG-derived variables available to the model. The EEG with ECG-versus-full comparison reflects the joint addition of four referential and four EMG channels and cannot isolate either group independently. Performance differences between channel configurations are reported descriptively. Formal paired hypothesis tests and confidence intervals were not calculated because the epoch-level prediction outputs needed for paired error analysis were not retained for every configuration. The reported percentage-point differences therefore must not be interpreted as statistically significant effects, and the study does not claim that ECG provides a statistically validated improvement. Inferential testing using retained paired predictions or repeated patient-disjoint resampling is required to establish reproducible performance differences [ 19 ]. An additional exploratory EEG with EMG configuration, comprising the 19 scalp EEG channels and four bilateral deltoid EMG channels, was evaluated as a supplementary analysis. The same preprocessing, feature-extraction, data-partitioning, SMOTE based training, classifier-evaluation, and SHAP-analysis procedures were applied. Detailed results are pr
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