EEG-Based Detection of Focal Seizure Area Using FBSE-EWT Rhythm and SAE-SVM Network

• Published in: IEEE sensors journal 2020-10, Vol.20 (19), p.11421-11428
• Main Authors: Siddharth, T., Gajbhiye, Pranjali, Tripathy, Rajesh Kumar, Pachori, Ram Bilas
• Format: Article
• Language: English
• Subjects: accuracy; Brain; Channels; Classification; Discrete wavelet transforms; electroencephalogram; Electroencephalography; Epilepsy; Feature extraction; Filter banks; Focal seizures; Fourier series; Fourier-Bessel transformations; Rhythm; rhythm specific FBSE-EWT; SAE-SVM; Seizures; Sensors; Series expansion; Support vector machines; Wavelet transforms
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2020.2995749

The neurological pathology which occurs due to the disturbance of the nerve cell activity and causing recurrent seizures is called epilepsy. In medical practice, the localization of the epileptogenic area or region in the brain is the primary task for the effectiveness of the epilepsy surgery. The epileptogenic region is identified based on the presence of focal electroencephalogram (EEG) signals during recording. Therefore, the classification of focal (FL) and non-focal (NFL) classes of EEG channels is the prerequisite to identify the epileptogenic regions in the brain. In this paper, a hybrid approach based on the combination of the band or rhythm specific Fourier-Bessel series expansion domain empirical wavelet transform (FBSE-EWT) filter bank and sparse autoencoder (SAE) based support vector machine (SAE-SVM) network is proposed for the categorization of FL and NFL types of EEG channels. The rhythms such as \delta , \theta , \alpha , \beta , and \gamma are obtained from the EEG signal of each channel using FBSE-EWT filter bank. The SAE-SVM network classifies the FL and NFL categories of EEG channels directly from the rhythms. The results demonstrate that the proposed hybrid approach has 100% accuracy for the classification of FL and NFL types using the \delta -rhythms of EEG signals for both channels. The approach extracts learnable features in the SAE stage, and these features have higher performance as compared to the existing features for the categorization of FL and NFL types of EEG channels.