A hybrid SVM and kernel function-based sparse representation classification for automated epilepsy detection in EEG signals

Automatic epilepsy detection based on electroencephalography (EEG) is crucial for advancing the diagnosis and treatment of epilepsy. In this paper, we propose a novel classification algorithm called SVM-KSRC, which differs from integrated learning approaches. The algorithm establishes a connection between support vector machine (SVM) and kernel sparse representation classification (KSRC) using support vectors. Specifically, we extract two types of features from the pre-processed EEG signals in this study. During the training phase, these features are utilized to train the SVM model and construct the kernel sparse representation dictionary. We differentiate the SVM part of the features of the test data to determine whether SVM or KSRC should be employed for classifying the test data. Our method is evaluated on two publicly available datasets: University of Bonn dataset and Neurology and Sleep Centre-New Delhi dataset. Through 10 times 10-fold cross validation, our method demonstrates superior performance in epilepsy detection when compared to existing machine learning methods. The experimental results demonstrate that SVM-KSRC is more effective compared to SVM and KSRC used separately. It achieves over 99% accuracy in all binary classification tasks and attains 100% accuracy in certain tasks. The source code is publicly available at https://github.com/Walkeraaa/SVM-KSRC.