Epileptic Seizure Detection Using Integrated Decomposed Features from EEG Signal

Abstract

The root cause of the seizure is a sudden abnormal excessive electrical discharge in the brain and it is an Epileptic Seizure (ES) when such abnormal electrical activity arises particularly for epilepsy. Recognizing ES is crucial for effective treatment as it often repeats and can lead to serious outcomes. Since epilepsy is a neurological issue, detecting ES by analyzing brain signals is the preferred method, with Electroencephalogram (EEG) being the most reliable approach for this purpose. Different Machine Learning (ML) and Deep Learning (DL) methods are extensively used in ES detection from EEG signals. Existing methods first extract features from EEG signals using different methods and then classify ES using appropriate ML/DL methods. This study investigates ML-based ES detection where feature extraction from decomposed EEG signals using various methods and integrating the extracted features to classify ES are the main attractions. Empirical Mode Decomposition (EMD) is employed to systematically break down EEG signals into Intrinsic Mode Functions (IMFs), with the earlier IMFs containing more information than the later ones. From the initial six IMFs, three distinct features named Fluctuation index (F), Variance (V) and Ellipse Area (EA) of the second-order difference plot are extracted. Neural Network (NN), the well-known ML method, is employed in this study for ES classification from extracted F, V and EA features individually and integrating these (i.e., F + V + EA). The experimental analysis is conducted on the benchmark CHB-MIT dataset and the integrated feature set shows promising performance over individual feature sets. The proposed NN-based ES detection with integrated decomposed features outperforms prominent existing methods, showing an accuracy of 99.80%