Seizure Detection Algorithm Based on Multidimensional Covariance Matrix and Binary Harris Hawks Optimization With Cauchy–Gaussian Mutation

This article proposes a novel seizure detection algorithm based on multidimensional covariance matrix and hybrid mutation of binary Harris hawks optimizer. First, raw electroencephalogram (EEG) signals are preprocessed using empirical mode decomposition (EMD) and discrete wavelet transform (DWT) to obtain several subband signals. Second, single-channel single-subband (SCSS) matrices, single-channel multisubband (SCMS) matrices, and multichannel single-subband (MCSS) matrices are constructed and transformed into covariance matrices. After computing the eigenvalues of each covariance matrix, statistical parameters, which include mean, variance, and skewness, are extracted to form a feature set. Then, a binary Harris hawks optimization algorithm with Cauchy–Gaussian mutation (CGBHHO) is proposed to enhance global search capability and local search capability of binary Harris hawks optimization (BHHO) algorithm to accomplish iterative feature selection. Finally, random forest (RF) classifier is employed for automatic seizure detection. The proposed method achieves favorable experimental results with a tenfold cross-validation evaluation on the CHB-MIT database. The results show that the accuracy (ACC), specificity (SPE), sensitivity (SEN), and F1 score (F1) of the method can reach 99.32%, 99.22%, 99.45%, and 99.36%, respectively.