An efficient ECG arrhythmia classification method based on Manta ray foraging optimization

The Electrocardiogram (ECG) arrhythmia classification has become an interesting research area for researchers and developers as it plays a vital role in early prevention and diagnosis of cardiovascular diseases. In ECG signal classification, the feature extraction and selection processes are critical steps. Thus, in this paper, different ECG signal descriptors based on one-dimensional local binary pattern (LBP), wavelet, higher-order statistical (HOS), and morphological information are introduced for feature extraction. For feature selection and classification processes, a new hybrid ECG arrhythmia classification approach called MRFO-SVM that combines a metaheuristic algorithm termed Manta ray foraging optimization (MRFO) with support vector machine (SVM) is proposed to automatically determine the relevance features of LBP, HOS, wavelet and magnitude values. In MRFO-SVM approach, the MRFO is utilized to optimize the parameters of SVM and to select the significant features subset that provides the best classification performance, meanwhile SVM is used for classification purposes. The proposed MRFO-SVM approach is trained on the MIT-BIH Arrhythmia database containing four abnormal and one normal heartbeats. The experimental results of ECG arrhythmia classification using the proposed MRFO-SVM revealed with evidence its superiority with overall classification accuracy of 98.26% over seven well-known metaheuristic algorithms.