Short duration Vectorcardiogram based inferior myocardial infarction detection: class and subject-oriented approach

• Published in: Biomedizinische Technik Vol. 66; no. 5; pp. 489 - 501
• Main Authors: Rahul, Jagdeep, Sharma, Lakhan Dev, Bohat, Vijay Kumar
• Format: Journal Article
• Language: English
• Published: Germany De Gruyter 01.10.2021; Walter de Gruyter GmbH
• Subjects: Automation; Blood circulation; Cardiac muscle; Feature extraction; Heart attacks; machine learning; minimum-redundancy-maximum-relevance; Muscles; Myocardial infarction; Redundancy; Segments; Signal processing; Stationary Wavelet Transform; subject-oriented; Vectorcardiography; Vectorcardiography (VCG); Wavelet transforms; Stationary Wavelet Transform; Vectorcardiography (VCG); minimum-redundancy-maximum-relevance; subject-oriented; machine learning; myocardial infarction
• ISSN: 0013-5585; 1862-278X; 1862-278X
• DOI: 10.1515/bmt-2020-0329

Myocardial infarction (MI) happens when blood stops circulating to an explicit segment of the heart causing harm to the heart muscles. Vectorcardiography (VCG) is a technique of recording direction and magnitude of the signals that are produced by the heart in a 3-lead representation. In this work, we present a technique for detection of MI in the inferior portion of heart using short duration VCG signals. The raw signal was pre-processed using the median and Savitzky–Golay (SG) filter. The Stationary Wavelet Transform (SWT) was used for time-invariant decomposition of the signal followed by feature extraction. The selected features using minimum-redundancy-maximum-relevance (mRMR) based feature selection method were applied to the supervised classification methods. The efficacy of the proposed method was assessed under both class-oriented and a more real-life subject-oriented approach. An accuracy of 99.14 and 89.37% were achieved respectively. Results of the proposed technique are better than existing state-of-art methods and used VCG segment is shorter. Thus, a shorter segment and a high accuracy can be helpful in the automation of timely and reliable detection of MI. The satisfactory performance achieved in the subject-oriented approach shows reliability and applicability of the proposed technique.