Automatic Epileptic Seizure Detection Using PSO-Based Feature Selection and Multilevel Spectral Analysis for EEG Signals. (28th October 2022)

• Record Type: Journal Article
• Authors: Sun, Qi; Liu, Yuanjian; Li, Shuangde; Wang, Chaodong
• Other Names: Srinivasan Kathiravan Academic Editor.
• Is Part Of: Journal of sensors. Volume 2022(2022)
• Publication Date: 2022-10-28
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2022/6585800
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : Automatic epileptic seizure detection technologies for clinical diagnosis mainly rely on electroencephalogram (EEG) recordings, which are immensely useful tools for epileptic location and identification. Currently, traditional seizure detection methods based only on single-view features have great limitations for the typical dynamic and nonlinear EEG signals. An objective of this paper is to investigate the effect of multiview feature selection and multilevel spectral analysis methods on the identification of the EEG signals for seizure detection. Here, multiview features are extracted from time domain, frequency domain, and information theory to collect adequate information of EEG signals. And a feature selection algorithm based on particle swarm optimization (PSO) is proposed for automatic seizure detection. Moreover, due to the different frequency components of the EEG signals, they are divided into four kinds of brain waves for multilevel spectral analysis. The effect of these four rhythm waves on seizure detection is compared. Three well-known classifiers are employed to classify EEG signals concerning seizure or nonseizure events. The result shows that the average accuracy, specificity, and sensitivity of classification with the CHB-MIT database are 98.14%, 98.64%, and 96.79%, respectively. The application of the PSO-based feature selection method for automatic seizure detection improves accuracy by 5.99% with the SVM classifier. Compared with the state-of-the-art methods, the proposed method has superior competence with high performance for automatic seizure detection. It is further shown that the feature selection method is an indispensable step in seizure detection. With PSO-based feature selection and multilevel spectral analysis, the θ wave in the frequency range of 4-7 Hz shows better performance in the identification of EEG signals and is more suitable for the proposed method. The PSO-based feature selection algorithm for automatic seizure detection can be a useful assistant tool for clinical diagnosis.