Automatic seizure detection based on Gray Level Co-occurrence Matrix of STFT imaged-EEG. (January 2023) - Shayeste, Haniye
Abstract: Epilepsy is a complicated neurological disorder, that features recurrent seizures. During a seizure, the repetitive action potentials lead to a high-frequency burst, and a hyper synchronization happens among the activities of a population of cortical neurons concurrently. In this study, we propose a repetition-based seizure detection method, using Gray Level Co-occurrence Matrix (GLCM) and Electroencephalogram (EEG) signals. In this method, imaged-EEGs are made from the Short Time Fourier Transform (STFT) coefficients of two sequential epochs. The GLCMs of these images are calculated and one feature named d i a g G L C M is extracted from each co-occurrence matrix. This feature demonstrates the repetition of the STFT coefficient of a frequency band in two sequential epochs, which are used for a classification task between ictal (during a seizure) and inter-ictal (between seizures) time segments. Using one frequency band's STFT repetition, a patient-specific seizure detection algorithm is developed with an accuracy of 99.56%, a sensitivity of 99.52%, and specificity of 99.62%. This accurate classification shows the efficiency of the extracted feature which can be a biomarker for the ictal repetitive action potentials. Highlights: Converting EEG features in successive epochs into imaged-EEGs. Gray Level Co-occurrence Matrix for imaged-EEGs processing. STFT coefficients repetition in sequential epochs as a biomarker of epileptic seizures. 99.56% accuracy of seizure detection using CHB-MIT dataset.