Automatic epileptic seizure detection in EEG signals using sparse common spatial pattern and adaptive short-time Fourier transform-based synchrosqueezing transform

Epilepsy can now be diagnosed more accurately and quickly due to computer-aided seizure detection utilizing Electroencephalography (EEG) recordings. In this work, a novel method for automated seizure identification from the EEG signal is proposed utilizing the sparse common spatial pattern (sCSP) and the adaptive short-time Fourier transform-based synchrosqueezing transform (adaptive FSST). The sCSP is utilized to select optimal channels and discriminate seizure states. In order to enhance the time-frequency representation of multi -component EEG signals and reduce noise and interferences, the selected channels are imported into adaptive FSST. The frequency spectrum of the adaptive FSST is separated into distinct segments concentrated on a particular frequency to improve seizure detection by adapting the EEG signal decomposition to the rhythmic component of seizures. Thus, a simple linear classifier is all that is needed to identify seizures thanks to this collection of algorithms since the extracted features from each segment's instantaneous amplitude and frequency and logvariance of each selected channel could be highly discriminative. Using linear classifiers for these discriminative features avoids overfitting and training complexity inherent in most non-linear classifiers and deep learning approaches. The proposed approach is compared to previous high-performance algorithms using the CHB-MIT epileptic EEG database. This method outperformed most of the best findings in the literature regarding mean performance, with mean sensitivity, specificity, and accuracy of 98.44%, 99.19%, and 98.81%, respectively. Thus, the proposed method's outstanding performance demonstrates that it could significantly decrease EEG interpretation load on specialists and be a powerful tool for an epilepsy diagnosis.