2026-10-11T10:58:25Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33966726 2026-09-22T17:23:35Z category_146 category_7 category_12 category_15 category_16 category_21 category_272 category_45 category_734 portal_5 item_type_3 month_year_09_2026

<p>Test macro-F1 across channel configurations.</p> Al Mukshit Plabon (25091103) Abdul Mukit (22930321) Md. Neyamul (25091106) Omar Faruk Jehady (25091109) Fatima Tuz Zuba (25091112) Md. Faisal Mina (25091115) Torikul Islam (22969426) Space Science Medicine Cell Biology Neuroscience Physiology Biotechnology Environmental Sciences not elsewhere classified Sociology Biological Sciences not elsewhere classified study therefore provides normalized global attribution important feature family feature variables influencing evaluated using 2 establish physiological localization downstream spatial categorization shap analysis identified complementing performance ablation la contributed 15 identical stratified epoch two ecg channels ecg channels ra confirmed ied epochs conditional spatial classification channel scalp eeg emg channels scalp distribution controlled analysis containing ied conditional five ecg ablation whole channel key channel used across test accuracy routinely co patient generalization multiple machine level partitions learning classifiers fitted models confirmed four complete 29 class tasks catboost model band power 76 %. <div><p>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.</p></div> 2026-09-22T17:23:23Z Dataset Dataset 10.1371/journal.pone.0358782.t006 https://figshare.com/articles/dataset/_p_Test_macro-F1_across_channel_configurations_p_/33966726 CC BY 4.0