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<p>Seizure detection from scalp EEG recordings requires features capable of capturing the rapidly evolving patterns of cerebral activity. We present a strictly patient-independent approach that investigates 1-second EEG windows using nested cross-validation to prevent data leakage within the paediatric CHB-MIT dataset. In this approach, we combine features extracted from temporal, spectral, wavelet, and spatial (CSP) domains. Feature selection, spatial filtering, and hyperparameter optimization are performed only within training folds to avoid leakage. A compact feature set of just 15 features enables traditional classifiers to achieve remarkable performance: a K-Nearest Neighbours classifier achieves 90.6% accuracy, whereas a Random Forest achieves 90.5% accuracy with an AUC of 0.927. The proposed system is interpretable, highly computationally efficient, and suitable for near real-time clinical monitoring.</p>

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Seizure detection using EEG on the CHB-MIT dataset via multi-domain feature engineering and classical machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月3日

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  • Srishti Ghosh,
  • Vandita Singh,
  • Omkar S Powar

摘要

Seizure detection from scalp EEG recordings requires features capable of capturing the rapidly evolving patterns of cerebral activity. We present a strictly patient-independent approach that investigates 1-second EEG windows using nested cross-validation to prevent data leakage within the paediatric CHB-MIT dataset. In this approach, we combine features extracted from temporal, spectral, wavelet, and spatial (CSP) domains. Feature selection, spatial filtering, and hyperparameter optimization are performed only within training folds to avoid leakage. A compact feature set of just 15 features enables traditional classifiers to achieve remarkable performance: a K-Nearest Neighbours classifier achieves 90.6% accuracy, whereas a Random Forest achieves 90.5% accuracy with an AUC of 0.927. The proposed system is interpretable, highly computationally efficient, and suitable for near real-time clinical monitoring.