Key result
A novel architecture using wavelet transform and Capsule network for myocardial infarction detection achieved an accuracy of 91.2%, sensitivity of 83%, and specificity of 89.5%.
Why the study?
Early diagnosis of myocardial infarction is crucial, but conventional CNN-based detection requires large amounts of data, presenting a challenge in the medical field.
Does a Capsule network based on ECG scalograms improve myocardial infarction classification compared to traditional CNNs?
Does a Capsule network based on ECG scalograms improve myocardial infarction classification compared to traditional CNNs?
A novel Capsule network architecture using ECG scalograms demonstrates promising accuracy for early detection of myocardial infarction while potentially requiring less training data than traditional CNNs.
May aid automated MI detection on ECG; extends capsule networks in cardiology but requires prospective validation.
Myocardial infarction (MI) is one of the leading causes of mortality throughout the world. Early diagnosis of MI is crucial for effective treatment to avoid patient morality. In this regard, the most commonly used technique for the problem of MI detection is the Convolutional Neural Network (CNN), which has shown good performance, but it still has some limitations. CNN requires a large amount of data, which is a challenge in the medical field. Therefore, the proposed approach uses a novel architecture consisting of wavelet transform and Capsule network, which is the most advanced algorithm to overcome CNN's drawback. Experimental results achieve an accuracy of 91.2%, Sensitivity of 83% and Specificity of 89.5% which demonstrates that CapsNet acquires promising results while using fewer data.
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Boujnouni et al. (2020) studied Myocardial infarction. Capsule network based on scalograms of ECG vs. Convolutional Neural Network (CNN) was evaluated on Classification accuracy. A novel architecture using wavelet transform and Capsule network for myocardial infarction detection achieved an accuracy of 91.2%, sensitivity of 83%, and specificity of 89.5%.
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