Cardiovascular disease (CVD) is responsible for an estimated 17.9 million annual deaths. Myocardial infarction (MI), a prominent symptom of CVD, occurs when reduced blood flow causes heart muscle death. Since permanent damage to the heart muscle begins within 30 minutes of blood flow restriction, MI is extremely dangerous and time sensitive. Electrocardiogram (ECG) is one of the most efficient methods for MI detection; however, it requires expertise to identify characteristic waveform features and it is also prone to interobserver bias. As such, numerous studies have implemented and proposed deep learning algorithms for ECG analysis, which both eliminate these issues and reduce the time it takes to arrive at a classification, in place of manual analysis. However, these studies do not consider the importance of compression algorithms to condense ECG data into smaller, richer sets of data, which could significantly increase classification accuracy and significantly decrease processing time. Therefore, this study investigates 10 compressed formats—orthogonal leads, vectorcardiogram (VCG), three different PCA variations, three different median beat variations, autoencoder, and binary convolutional autoencoder (BCAE)—in comparison to the baseline 12-lead format in MI classification efficacy with the XResNet model.

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