Seizure detection algorithm based on improved functional brain network structure feature extraction

Abstract

Epilepsy is one of the most common neurological disorders. Accurate detection of epileptic seizures is essential for treatment. A seizure detection method with the structure of functional brain network, time, and frequency multi-domain features is presented in this paper. Pearson correlation coefficient (PCC) and mutual information (MI) can characterize the correlation between channels of EEG, and be used to construct PCC and MI combined functional brain networks (PMNet). Then the complex network theory is used to calculate the structure features of PMNet to explore whether it reflects seizure or not. After extraction of the multi-domain features, principal component analysis (PCA) is used to eliminate irrelevant feature information before feeding the features into the support vector machine (SVM) classification method. The method is tested on two open datasets (CHB-MIT and SIENA) and verified on the West syndrome (WS) dataset from the Children’s Hospital, Zhejiang University School of Medicine. The results demonstrate that the method based on the structure of functional brain networks and other multi-domain EEG features is competitive with other comparison methods. Furthermore, base on the method with brain network features, this paper has developed a simple automatic seizure detection system in hospital applications, which can reduce the workload of EEG technicians in reading EEG and marking seizures.