Open AccessCross subject myocardial infarction detection from vectorcardiogram signals using binary harry hawks feature selection and ensemble classifiers
Author(s) -
M Krishna Chaitanya,
Lakhan Dev Sharma
Publication year - 2024
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2024.3367597
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Myocardial infarction (MI), widely referred to as a heart attack, is a leading reason for deaths worldwide. It is frequently caused by coronary artery occlusion, resulting in inadequate oxygen and blood supply, which damages the myocardial structure and function. Therefore, innovative diagnostic methods are required for reliable and timely identification of MI. The typical 12-lead electrocardiogram (ECG) technology causes patient discomfort and makes cardiac monitoring challenging. The frontal, sagittal, and transverse planes (3 orthogonal planes) are where vectorcardiogram (VCG) renders an edge over 12-lead ECG. This study, proposes a method for detecting MI utilising VCG signals of four seconds. Circulant singular spectrum analysis (CSSA) and four stage savitzky-golay (SG) filter were used in the filtering stage for the removal of power-line interference and base-line wander. The signal was time-invariantly decomposed using the CSSA, then features were extracted. The binary harry hawks-based feature selection method is employed on the extracted features to choose the optimal feature subspace which was followed by supervised machine learning based classification. The 10-fold cross validation, an even more practical leave-one-out (LOO) cross validation approach, and inter dataset cross validation (IDCV) were used to evaluate the reliability of the suggested method. Voting-based ensemble classification was used in LOO, IDCV validation, which improves the accuracy of this method. The proposed technique achieved an accuracy of 99.97%, 91.03%, and 99.41% for 10-fold, LOO cross validation, and IDCV, out-performing the state-of-the-art methods in the cross validation scenarios. The proposed technique results in an accurate detection of MI. Successful accomplishment of the LOO cross validation demonstrates the applicability and dependability of the suggested technique in the health care applications.