Cluster-based phase space density feature in multichannel scalp EEG for seizure prediction by deep learning

• Published in: Biomedical signal processing and control Vol. 86; p. 105276
• Main Authors: Feizbakhsh, Bardia, Omranpour, Hesam
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2023
• Subjects: 2D phase space; Classification; Clustering; Density; Feature extraction; Multichannel EEG signals; Seizure; Multichannel EEG signals; 2D phase space; Classification; Feature extraction; Seizure; Clustering; Density
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2023.105276

•Developed a feature extraction method for EEG signals classification.•Multiclass classification of the brain states before the onset of seizure.•The CHB-MIT scalp EEG dataset is used for the evaluation, which contains 23 subjects.•Compared with nine previous works, the proposed method has superior performance. The electroencephalogram (EEG) is one of the most common methods for studying epileptic seizures. Seizure detection has been a well-known subject in neuroscience. According to previous research projects, it is more effective to extract features from the EEG to detect seizures rather than analyzing the raw EEG signals. Therefore, a new feature extraction method is suggested in this work. In this paper, new features are proposed to classify different brain states in EEG records of patients with epilepsy disorder. In previous works, some of the proposed features were based on the ellipses’ data density in phase space which is the motivation of this paper. The innovation here is to use clusters with no specific shapes instead of ellipses to extract improved features. A clustering method is performed on EEG signals in phase space. The densities of data in each cluster are considered as features, and these features are given to a classifier as inputs to classify different brain signals. The investigation is performed on the CHB-MIT scalp EEG dataset. In the binary classification scenario, the achieved results include a sensitivity of 94.94%, a specificity of 94.94%, and a FPR of 0.051 per hour. On the other hand, for the multiclass classification, the specificity is 92.33% and the FPR is 0.077 per hour. These performance metrics highlight the effectiveness of the proposed feature extraction method in generating robust features for classifying brain states. The achieved results demonstrate its superiority over the methods used in previous studies. By extracting meaningful and discriminative features from the EEG signals, the classification model can accurately differentiate between different brain states. This suggests that the proposed method holds promise for improving the accuracy and reliability of brain state classification. The proposed technique can assist neuroscientists in easily distinguishing different states of the brain before the seizure onset.