Key result
The proposed 1D-CNN architecture for 15-lead ECG signals achieved an average accuracy of 99.98%, sensitivity of 99.91%, and specificity of 99.99% in detecting myocardial infarction.
Why the study?
Early detection and diagnosis of myocardial infarction are necessary to prevent death and complications like heart failure or arrhythmias.
Does a 1D-CNN architecture accurately classify myocardial infarction using 15-lead ECG signals?
Does a 1D-CNN architecture accurately classify myocardial infarction using 15-lead ECG signals?
A proposed 1D-CNN architecture demonstrates excellent diagnostic performance for detecting and classifying myocardial infarction using 15-lead ECG signals.
Supports rapid automated MI detection via 15-lead ECG; extends prior CNN models but requires prospective validation before practice change.
Impaired blood flow caused by coronary artery occlusion due to thrombus can cause damage to the heart muscle which is often called Myocardial Infarction (MI). To avoid the complexity of MI diseases such as heart failure or arrhythmias that can cause death, it is necessary to diagnose and detect them early. An electrocardiogram (ECG) signal is a diagnostic medium that can be used to detect acute MI. Diagnostics with the help of data science is very useful in detecting MI in ECG signals. The purpose of study is to propose an automatic classification framework for Myocardial Infarction (MI) with 15 lead ECG signals consisting of 12 standard leads and 3 Frank leads. This research contributes to the improvement of classification performance for 10 MI classes and normal classes. The PTB dataset trained with the proposed 1D-CNN architecture was able to produce average accuracy, sensitivity, specificity, precision and F1-score of 99.98%, 99.91%, 99.99%, 99.91, and 99.91%. From the evaluation results, it can be concluded that the proposed 1D-CNN architecture is able to provide excellent performance in detecting MI attacks.
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Mirza et al. (2022) studied Myocardial Infarction. 1D-CNN architecture for 15-lead ECG signals was evaluated on Classification performance (accuracy, sensitivity, specificity, precision, F1-score). The proposed 1D-CNN architecture for 15-lead ECG signals achieved an average accuracy of 99.98%, sensitivity of 99.91%, and specificity of 99.99% in detecting myocardial infarction.
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