Conditional spatial classification of expert-confirmed interictal epileptiform discharge epochs: An EEG-ECG ablation and SHAP analysis
- 1Department of Biomedical Engineering, Jashore University of Science and Technology, Jashore, Bangladesh
- 2Department of Biomedical Engineering, Khulna University of Engineering and Technology, Khulna, Bangladesh
- 3Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, United States of America
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.
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- A Reproducible Cortical Model of Focal Epileptiform Activity with a Multi-Biomarker Analysis Pipeline
- Early Increases in Hippocampal Synchrony Predict Chronic Epileptiform Burden in a Temporal Lobe Epilepsy Model
- Localising the Seizure Onset Zone from Routine Interictal and Ictal Scalp EEG Via a Factorised Spatiotemporal Ensemble Deep Learning Framework
BibTeX
@article{plabon2026conditional,
title = {{Conditional spatial classification of expert-confirmed interictal epileptiform discharge epochs: An EEG-ECG ablation and SHAP analysis}},
author = {Al Mukshit Plabon and Abdul Mukit and Md. Neyamul and Omar Faruk Jehady and Fatima Tuz Zuba and Md. Faisal Mina and Torikul Islam},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0358782},
url = {https://doi.org/10.1371/journal.pone.0358782},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Conditional spatial classification of expert-confirmed interictal epileptiform discharge epochs: An EEG-ECG ablation and SHAP analysis
AU - Al Mukshit Plabon
AU - Abdul Mukit
AU - Md. Neyamul
AU - Omar Faruk Jehady
AU - Fatima Tuz Zuba
AU - Md. Faisal Mina
AU - Torikul Islam
PY - 2026
DA - 2026/09/22
JO - PLOS One
DO - 10.1371/journal.pone.0358782
UR - https://doi.org/10.1371/journal.pone.0358782
AB - 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.
SN - 1932-6203
ER -