An automated seizure detection approach using local mean decomposition and GA-SVM achieved an average classification accuracy of ≥98.10% across five classification cases on the Bonn EEG dataset.
Does LMD combined with GA-SVM improve the automatic detection of epileptic seizures from EEG signals?
The proposed LMD and GA-SVM based approach achieves high accuracy (≥98.10%) for automated seizure detection using EEG signals.
Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for the treatment of epileptic seizures. To realize this aim, a newly-developed time-frequency analytical algorithm, namely local mean decomposition (LMD), is employed in the presented study. LMD is able to decompose an arbitrary signal into a series of product functions (PFs). Primarily, the raw EEG signal is decomposed into several PFs, and then the temporal statistical and non-linear features of the first five PFs are calculated. The features of each PF are fed into five classifiers, including back propagation neural network (BPNN), K-nearest neighbor (KNN), linear discriminant analysis (LDA), un-optimized support vector machine (SVM) and SVM optimized by genetic algorithm (GA-SVM), for five classification cases, respectively. Confluent features of all PFs and raw EEG are further passed into the high-performance GA-SVM for the same classification tasks. Experimental results on the international public Bonn epilepsy EEG dataset show that the average classification accuracy of the presented approach are equal to or higher than 98.10% in all the five cases, and this indicates the effectiveness of the proposed approach for automated seizure detection.
Zhang et al. (2016) studied Epileptic seizures. Local mean decomposition (LMD) combined with GA-SVM classifier vs. BPNN, KNN, LDA, and un-optimized SVM was evaluated on Average classification accuracy for seizure detection. An automated seizure detection approach using local mean decomposition and GA-SVM achieved an average classification accuracy of ≥98.10% across five classification cases on the Bonn EEG dataset.