Automated Seizure Onset Detection and Prediction Using EEG: A Comprehensive Model with Multiscale Analysis and Machine Learning

Hassan Aridy; Rayan Abu-Hanifa; Jabbar Thaqib; Naser Al-athiry

Yazarlar

Hassan Aridy New Technologies Department, Al Iraqia University, Baghdad, Iraq
Rayan Abu-Hanifa New Technologies Department, Al Iraqia University, Baghdad, Iraq
Jabbar Thaqib Electrical and Electronic Engineering Department, Al Iraqia University, Baghdad, Iraq
Naser Al-athiry University of Information Technology and Communications, Iraq

Özet

Accurate and timely detection of seizure onset is crucial for effectively managing epilepsy. This paper presents a novel and fully specified model for automated seizure onset detection and prediction based on electroencephalography (EEG) measurements. The model is evaluated using two widely recognized EEG databases, namely Freiburg (intracranial EEG) and CHB-MIT (scalp EEG), to assess its performance against state-of-the-art models. The proposed model incorporates four key components to enhance its effectiveness: (1) multiscale principal component analysis for EEG de-noising to improve signal quality, (2) EEG signal decomposition using empirical mode decomposition, discrete wavelet transform, or wavelet packet decomposition to capture relevant signal characteristics, (3) statistical measures for extracting informative features, and (4) machine learning algorithms for accurate classification. Experimental results demonstrate the superior performance of our model in comparison to existing approaches. It achieves an impressive overall accuracy of 100% in distinguishing between ictal and inter-ictal EEG for both the Freiburg and CHB-MIT databases. Furthermore, the model exhibits high accuracy in seizure onset prediction, successfully discriminating between inter-ictal, pre-ictal, and ictal EEG states with an accuracy of 99.78% and distinguishing between inter-ictal and pre-ictal EEG states with an accuracy of 99.72%. Importantly, the proposed model's versatility extends beyond seizure detection and prediction. It can be applied to other classification tasks involving bio-signals, such as electromyography (EMG) and electrocardiography (ECG). Thus, our model holds promise as a comprehensive tool for various biomedical signal analysis applications.

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