Automatic Recognition of High-Density Epileptic EEG Using Support Vector Machine and Gradient-Boosting Decision Tree

Abstract

Background: Epilepsy (Ep) is a chronic neural disease. The diagnosis of epilepsy depends on detailed seizure history and scalp electroencephalogram (EEG) examinations. The automatic recognition of epileptic EEG is an artificial intelligence application developed from machine learning (ML). Purpose: This study compares the classification effects of two kinds of classifiers by controlling the EEG data source and characteristic values. Method: All EEG data were collected by GSN HydroCel 256 leads and high-density EEG from Xiangya Third Hospital. This study used time-domain features (mean, kurtosis and skewness processed by empirical mode decomposition (EMD) and three IMFs), a frequency-domain feature (power spectrum density, PSD) and a non-linear feature (Shannon entropy). Support vector machine (SVM) and gradient-boosting decision tree (GBDT) classifiers were used to recognize epileptic EEG. Result: The result of the SVM classifier showed an accuracy of 72.00%, precision of 73.98%, and an F1_score of 82.28%. Meanwhile, the result of the GBDT classifier showed a sensitivity of 98.57%, precision of 89.13%, F1_score of 93.40%, and an AUC of 0.9119. Conclusion: The comparison of GBDT and SVM by controlling the variables of the feature values and parameters of a classifier is presented. GBDT obtained the better classification accuracy (90.00%) and F1_score (93.40%).