Key result
The Adaptive Recurrent Neural Network (ARNN) detected myocardial infarction with 99.21% accuracy, 99% sensitivity, and 99.4% specificity, outperforming conventional LSTM-CAE and LSTM-CNN techniques.
Why the study?
A lack of accurate and early detection techniques for myocardial infarction has reduced the efficiency of its diagnosis.
Does an Adaptive Recurrent Neural Network (ARNN) improve the detection accuracy of myocardial infarction from ECG signals compared to conventional LSTM techniques?
Does an Adaptive Recurrent Neural Network (ARNN) improve the detection accuracy of myocardial infarction from ECG signals compared to conventional LSTM techniques?
A deep learning-based Adaptive Recurrent Neural Network demonstrates high accuracy (99.21%) in detecting myocardial infarction from ECG signals, outperforming conventional LSTM techniques.
ARNN shows promise for MI detection; leaves open prospective validation before clinical adoption.
Myocardial infarction (MI) may precipitate severe health damage and lead to irreversible death of the heart muscle, the result of prolonged lack of oxygen if it is not treated in a timely manner. Lack of accurate and early detection techniques for this heart disease has reduced the efficiency of MI diagnosis. In this paper, the design, and implementation of an efficient deep learning algorithm called Adaptive Recurrent neural network (ARNN) is proposed for the MI detection. The main objective of the proposed work is the accurate identification of MI disease using ECG signals. ECG signal denoising has been performed using the Multi-Notch filter, which removes the specified noise frequency range. Discrete wavelet transform (DWT) is utilized for performing the feature extraction that decomposes the ECG signal into varied scales with waveletfiltering bank. After the extraction of specific QRS features, classification of the defected and normal ECG arrhythmic beat has been performed using the deep learning-based ARNN classifier. The MIT-BIH database has been used for testing and training data. The performance of the proposed algorithm is evaluated based on classification accuracy. Results that are attained include the classification accuracy of about 99.21%, 99% of sensitivity and 99.4% of specificity with PPV and NPV of about 99.4 and 99.01 values indicate the enhanced performance of our proposed work compared with the conventional LSTM-CAE and LSTM-CNN techniques.
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Mahendran et al. (2021) studied Myocardial Infarction. Adaptive Recurrent neural network (ARNN) vs. LSTM-CAE and LSTM-CNN techniques was evaluated on classification accuracy. The Adaptive Recurrent Neural Network (ARNN) detected myocardial infarction with 99.21% accuracy, 99% sensitivity, and 99.4% specificity, outperforming conventional LSTM-CAE and LSTM-CNN techniques.
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