Gray-level co-occurrence matrix of Fourier synchrosqueezed transform for epileptic seizure detection
Epilepsy is a brain disorder that many persons of different ages in the world suffer from it. According to the world health organization, epilepsy is characterized by repetitive seizures and more electrical discharge in a group of brain neurons results in sudden physical actions. The aim of this paper is to introduce a new method to classify epileptic phases based on Fourier synchro-squeezed transform (FSST) of electroencephalogram (EEG) signals. FSST is a time-frequency (TF) analysis and provides sharper TF estimates than the conventional short-time Fourier transform (STFT). Absolute of FSST of EEG signal is computed and segmented into five non-overlapping frequency sub-bands as delta (d), theta (u), alpha (a), beta (b), and gamma (g). Each sub-band is considered as a gray-scale image and then we propose to obtain the gray-level co-occurrence matrix (GLCM) of each sub-band as features. We concatenate the features of different sub-bands to obtain the final feature vector. After selecting informative features by infinite latent feature selection (ILFS) method, the support vector machine (SVM) and K-nearest neighbor (KNN) classifiers are used separately to classify EEG signals. We use the EEG signals from Bonn University database and different combinations of its sets are considered. Simulation results show that the proposed method efficiently classifies the EEG signals and can be used to determine the phase of epilepsy.
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Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).