Abstract
Myocardial infarction (MI) occurs due to the decrease in the blood flow into one part of the heart, and it further causes damage to the heart muscle. The 12-channel electrocardiogram (ECG) has been widely used to detect and localize MI pathology in clinical studies. The vectorcardiogram (VCG) is a 3-channel recording system used to measure the heart’s electrical activity in sagittal, transverse, and frontal planes. The VCG signals have advantages over the 12-channel ECG to localize posterior MI pathology. Detection and localization of MI using VCG signals are vital in clinical practice. This paper proposes a multi-channel multi-scale two-stage deep-learning-based approach to detect and localize MI using VCG signals. In the first stage, the multivariate variational mode decomposition (MVMD) decomposes the three-channel-based VCG signal beat into five components along each channel. The multi-channel multi-scale VCG tensor is formulated using the modes of each channel of VCG data, and it is used as the input to the deep convolutional neural network (CNN) to classify MI and normal sinus rhythm (NSR) classes. In the second stage, the multi-class deep CNN is used for the categorization of anterior MI (AMI), anterior-lateral MI (ALMI), anterior-septal MI (ASMI), inferior MI (IMI), inferior-lateral MI (ILMI), inferior-posterior-lateral (IPLMI) classes using MI detected multi-channel multi-scale VCG instances from the first stage. The proposed approach is developed using the VCG data obtained from a public database. The results reveal that the approach has obtained the accuracy, sensitivity, and specificity values of 99.58%, 99.18%, and 99.87%, respectively, for MI detection. Moreover, for MI localization, we have obtained the overall accuracy value of 99.86% in the second stage for our proposed network. The proposed approach has demonstrated superior classification performance compared to the existing VCG signal-based MI detection and localization techniques.
Highlights
The localization stage consists of the classification of anterior MI (AMI), inferior MI (IMI), anterior-lateral MI (ALMI), anterior-septal MI (ASMI), inferior-lateral MI (ILMI), and IPLMI beats using
The results evaluated using the proposed multi-channel multi-scale deep CNN (MMDCNN) for myocardial infarction (MI) detection and localization using
The average values of accuracy, sensitivity, specificity, and kappa scores over five trial-based random validation are more than 99%
Summary
The obstruction in one of the coronary arteries of the heart causes the myocardial infarction (MI) disease [1,2]. The MI is progressed in three phases [3]. These three phases are (a) ischemic phase, (b) acute phase, and (c) myocardial necrosis phase.
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