Key result
A novel eigendomain-based deep representation learning approach achieved 100% accuracy for detecting myocardial infarction from 12-lead ECG trace images.
Why the study?
Automated detection of MI from 12-lead ECG trace images using AI is important for accurate diagnosis.
Does an eigendomain-based deep representation learning approach improve the detection of myocardial infarction from 12-lead ECG trace images compared to existing models?
Does an eigendomain-based deep representation learning approach improve the detection of myocardial infarction from 12-lead ECG trace images compared to existing models?
A novel eigendomain-based deep representation learning approach demonstrated high accuracy (up to 100%) in detecting myocardial infarction from 12-lead ECG trace images.
May aid automated MI detection from ECG; leaves open prospective validation before clinical adoption.
Myocardial infarction (MI) is a life-debilitating emergency in which there is a lack of blood flow in the heart muscle, resulting in permanent damage to the myocardium and sudden cardiac death. The 12-lead electrocardiogram (ECG) is a standardized diagnostic test conducted in hospitals to detect and localize MI-based heart disease. To diagnose MI, the cardiologist visualizes the alternations in the patterns of the 12-lead-based ECG trace image. The automated detection of MI from the 12-lead-based ECG trace image using artificial intelligence (AI)-based approaches is important in the clinical study for the accurate diagnosis of MI disease. This article proposes a novel eigendomain-based deep representation learning (DRL) approach to automatically detect MI using 12-lead ECG trace images. The singular value decomposition (SVD) and eigendomain grouping are used to evaluate five modes or components from the 12-lead ECG trace image. The EfficientNetV2B2-based transfer learning model extracts feature maps from the 12-lead ECG trace image and all five modes. The global average pooling (GAP), batch normalization (BN), dropout, and soft-max layers are used for each feature map to obtain the probability scores. The concatenated probability scores of all the feature maps, followed by the dense layer and output layer, are used to detect MI. A public database containing the 12-lead ECG trace images is used to evaluate the performance of the proposed approach. The results show that for the MI class, the proposed approach has achieved the accuracy value of 100%. Similarly, for normal versus MI versus other cardiac-arrhythmia-based disease classification schemes, the proposed approach has obtained the overall accuracy, F1-score, specificity, and sensitivity values of 99.03%, 99.01%, 99.49%, and 98.96%, respectively using fivefold cross-validation (CV). The suggested approach has demonstrated higher overall accuracy than 24 existing transfer-learning-based models to detect MI using the 12-lead ECG trace images.
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Bhaskarpandit et al. (2023) studied Myocardial Infarction. Eigendomain-based deep representation learning (DRL) approach vs. 24 existing transfer-learning-based models was evaluated on Accuracy of MI detection. A novel eigendomain-based deep representation learning approach achieved 100% accuracy for detecting myocardial infarction from 12-lead ECG trace images.
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