Key result
The proposed machine learning technique utilizing vectorcardiogram signals achieved a classification accuracy of 99.97% for 10-fold cross-validation, 91.03% for leave-one-out, and 99.41% for inter-dataset cross-validation.
Why the study?
Innovative diagnostic methods are needed for reliable and timely MI identification, as typical 12-lead ECG causes patient discomfort and makes cardiac monitoring challenging.
Does a machine learning approach using CSSA and BHHO feature selection on VCG signals improve the detection accuracy of myocardial infarction?
Does a machine learning approach using CSSA and BHHO feature selection on VCG signals improve the detection accuracy of myocardial infarction?
A novel machine learning approach using vectorcardiogram signals, CSSA decomposition, and BHHO feature selection achieved >99% accuracy in detecting myocardial infarction in cross-validation scenarios.
Answers are grounded in this paper and cite their sources.
VCG-based MI detection may ease monitoring discomfort; leaves open prospective validation against standard ECG.
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.
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Chaitanya et al. (2024) studied Myocardial Infarction (n=200). Circulant singular spectrum analysis, binary Harry Hawks feature selection, and ensemble classifiers vs. Standard machine learning classifiers without proposed feature selection was evaluated on Classification accuracy for myocardial infarction detection. The proposed machine learning technique utilizing vectorcardiogram signals achieved a classification accuracy of 99.97% for 10-fold cross-validation, 91.03% for leave-one-out, and 99.41% for inter-dataset cross-validation.
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