Epileptic seizure detection from electroencephalogram (EEG) signals using linear graph convolutional network and DenseNet based hybrid framework

Abstract

A clinical condition known as epilepsy occurs when the brain's regular electrical activity is disturbed, resulting in a rapid, aberrant, and excessive discharge of brain neurons. The electroencephalogram (EEG) signal is the measurement of electrical activity received from the nerve cells of the cerebral cortex to make precise diagnoses of disorders, which is made crucial attention for treating epilepsy patients in recent years. The concentration on grid-like data has been a significant drawback of existing deep learning-based automatic epileptic seizure detection algorithms from raw EEG signals; nevertheless, physiological recordings frequently have irregular and unordered structures, making it challenging to think of them as a matrix. In order to take advantage of the implicit information that exists in seizure detection, graph neural networks have received a lot of attention. These networks feature interacting nodes connected by edges whose weights can be either dictated by temporal correlations or anatomical junctions. To address this limitation, a novel hybrid framework is proposed for epileptic seizure detection by using linear graph convolution neural network (LGCN) and DenseNet. When compared to previous deep learning networks, DenseNet achieves the model's higher computational accuracy and memory efficiency by reducing the vanishing gradient problem and enhancing feature propagation in each of its layers. The Stockwell transform (S-transform) is used to preprocess from the raw EEG signal and then group the resulting matrix into time-frequency blocks as inputs for the LGCN to use for feature selection and after the Densenet uses for classification. The proposed hybrid framework outperforms the state-of-the-art in seizure detection tasks, achieving 98% accuracy and 98.60% specificity in extensive experiments on the publicly available CHB-MIT EEG dataset.