Automated detection of myocardial infarction from ECG signal using variational mode decomposition based analysis

Kapfo, Ato; Dandapat, Samarendra; Bora, Prabin Kumar

doi:10.1049/htl.2020.0015

Cited by 5 publications

(1 citation statement)

References 36 publications

(47 reference statements)

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“…They have obtained specificity of 86.29% for the classification of MI. Kapfo et al [17] decomposed the input ECG signal using variational mode decomposition (VMD) and employed support vector machine (SVM) for the classification of MI through the LOO cross validation. They have obtained an accuracy of 99.88%.…”

Section: Introductionmentioning

confidence: 99%

Cross Subject Myocardial Infarction Detection From Vectorcardiogram Signals Using Binary Harry Hawks Feature Selection and Ensemble Classifiers

Chaitanya,

Sharma

2024

IEEE Access

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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 leaveone-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-theart 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.

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Section: Introductionmentioning

confidence: 99%