Detection of Myocardial Infarction from 12-lead ECG trace images using Eigendomain Deep Representation Learning

• Published in: IEEE transactions on instrumentation and measurement 2023-01, Vol.72, p.1-1
• Main Authors: Bhaskarpandit, Sathvik, Gade, Anurag, Dash, Shaswati, Dash, Dinesh Kumar, Tripathy, Rajesh Kumar, Pachori, Ram Bilas
• Format: Article
• Language: English
• Subjects: 12-lead ECG trace image; Accuracy; Alternations; Arrhythmia; Artificial intelligence; Blood flow; Deep learning; Diseases; Eigendomain analysis; Electrocardiography; Feature extraction; Feature maps; Heart attacks; Heart diseases; Lead; Learning; Medical imaging; Muscles; Myocardial infarction; Myocardium; Performance evaluation; Recording; Representation learning; Representations; Singular value decomposition; Transfer learning
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2023.3241986

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 paper proposes a novel eigendomain-based deep representation learning approach to automatically detect MI using 12-lead ECG trace images. The singular value decomposition (SVD) and eigendomain grouping are employed 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), drop-out, and soft-max layers are used for each feature map to obtain the probability scores. The concatenated probability scores of all 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 5-fold cross-validation (CV). The suggested approach has demonstrated higher overall accuracy than twenty-four existing transfer learning-based models to detect MI using 12-lead ECG trace images.