An efficient machine learning based ventricular late potential detection and classification technique for cardiac healthcare

Abstract

SUMMARYCardiovascular diseases (CVD) are a major cause of death worldwide each year, with myocardial infarction (MI) accounting for the highest percentage of deaths. The rate of mortality can be reduced if electrical homogeneity of the cardiac tissue is detected early. The presence of the ventricular late potential (VLP) at the conclusion of the QRS complex and the start of the ST segment of the electrocardiogram (ECG) signal indicates a violation. Existing research use a wavelet‐based decomposition of the ECG signal for VLP detection, which is a multistage process. The use of empirical mode decomposition (EMD) to automatically distinguish normal ECG signal and signal with the presence of VLP is proposed in this work. Furthermore, utilizing machine learning classifiers, this research aims to enable automated diagnosis of cardiac arrhythmia. Intrinsic mode functions (IMF) are amplitude and frequency modulated waves that result from the decomposition of ECG using the EMD. Each IMF's peak amplitudes, spectral entropy, and mean of the ECG in the EMD are retrieved and used as input features for various machine learning techniques (ML). According to simulation findings, the bagging classifier achieves accuracy and specificity of 98.93% and 98.62%, respectively, while the AdaBoost classifier based on machine learning technique achieves sensitivity of 99.84%. The observed performance is considered to be the best when compared to existing approaches.