West Syndrone Seizure Detection Algorithm based on Fusion Network of EEG and EMG

West syndrome is an epileptic encephalopathy that will impact the cognitive development of children. To effectively diagnose West syndrome, effective utilization of electroencephalogram (EEG) and electromyography (EMG) signals is crucial. Previous studies have conducted limited investigations on the integration and comparison of EEG and EMG systems. Hence, this research aims to construct and evaluate the effectiveness of a fusion network composed of EEG and EMG by constructing and analyzing its performance. The algorithm is evaluated using EEG data from 12 patients with West syndrome collected by the Children’s Hospital Affiliated with Zhejiang University School of Medicine. With the 5-fold cross-validation method, the algorithm achieves an average accuracy, precision, sensitivity and F1 score of 93.45%, 92.42%, 94.44% and 93.30%, respectively, indicating a high level of detection performance.