The role of beat-by-beat cardiac features in machine learning classification of ischemic heart disease (IHD) in magnetocardiogram (MCG)

• Published in: Biomedical physics & engineering express Vol. 10; no. 4; pp. 45007 - 45017
• Main Authors: Senthilnathan, S, Shenbaga Devi, S, Sasikala, M, Satheesh, Santhosh, Selvaraj, Raja J
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.07.2024
• Subjects: Adult; Aged; Algorithms; beat-by-beat cardiac features; Case-Control Studies; Electrocardiography - methods; Female; Heart - physiopathology; Heart Rate - physiology; Humans; ischemic heart disease; Machine Learning; machine learning classifiers; magnetocardiography; Magnetocardiography - methods; Male; Middle Aged; myocardial infarction; Myocardial Ischemia - diagnosis; Myocardial Ischemia - physiopathology; Reproducibility of Results; Signal Processing, Computer-Assisted; ischemic heart disease; machine learning classifiers; beat-by-beat cardiac features; magnetocardiography; myocardial infarction
• ISSN: 2057-1976; 2057-1976
• DOI: 10.1088/2057-1976/ad40b1

Cardiac electrical changes associated with ischemic heart disease (IHD) are subtle and could be detected even in rest condition in magnetocardiography (MCG) which measures weak cardiac magnetic fields. Cardiac features that are derived from MCG recorded from multiple locations on the chest of subjects and some conventional time domain indices are widely used in Machine learning (ML) classifiers to objectively distinguish IHD and control subjects. Most of the earlier studies have employed features that are derived from signal-averaged cardiac beats and have ignored inter-beat information. The present study demonstrates the utility of beat-by-beat features to be useful in classifying IHD subjects (n = 23) and healthy controls (n = 75) in 37-channel MCG data taken under rest condition of subjects. The study reveals the importance of three features (out of eight measured features) namely, the field map angle (FMA) computed from magnetic field map, beat-by-beat variations of alpha angle in the ST-T region and T wave magnitude variations in yielding a better classification accuracy (92.7 %) against that achieved by conventional features (81 %). Further, beat-by-beat features are also found to augment the accuracy in classifying myocardial infarction (MI) Versus control subjects in two public ECG databases (92 % from 88 % and 94 % from 77 %). These demonstrations summarily suggest the importance of beat-by-beat features in clinical diagnosis of ischemia.