A Novel Approach for Detection of Myocardial Infarction From ECG Signals of Multiple Electrodes

Abstract

Myocardial infarction (MI) is also called the heart attack, and it results in the death of heart muscle cells due to the lacking in the supply of oxygen and other nutrients. The early and accurate detection of MI using the 12-lead electrocardiogram (ECG) is helpful in the clinical standard for saving the lives of the patients suffering from this pathology. This paper proposes a novel approach for the detection of MI pathology using the multiresolution analysis of 12-lead ECG signals. The approach is based on the use of Fourier-Bessel series expansion-based empirical wavelet transform (FBSE-EWT) for the time-scale decomposition of 12-lead ECG signals. For each lead ECG signal, nine subband signals are evaluated using FBSE-EWT. The statistical features such as the kurtosis, the skewness, and the entropy are evaluated from the subband signals of each ECG lead. The deep neural network such as the deep layer least-square support-vector machine (DL-LSSVM) which is formulated using the hidden layers of sparse auto-encoders and the LSSVM is used for the detection of MI from the feature vector of 12-lead ECG. The experimental results demonstrate that the combination of FBSE-EWT-based entropy features and DL-LSSVM has the mean accuracy, the mean sensitivity, and the mean specificity values of 99.74%, 99.87%, and 99.60%, respectively, for the detection of MI. The accuracy value of the proposed method is improved by more than 3% as compared to the wavelet-based features for the detection of MI.