Why the study?
Comparison of unidirectional and bidirectional phases was needed to determine the optimum phase and best recurrent network model performance for classifying five classes of cardiac disorders using 15-lead ECG signals.
Does a unidirectional GRU model improve the classification of cardiac disorders from 15-lead ECG signals compared to other recurrent network models?
Does a unidirectional GRU model improve the classification of cardiac disorders from 15-lead ECG signals compared to other recurrent network models?
Unidirectional GRU models demonstrate high accuracy and performance for classifying cardiac disorders using 15-lead ECG signals.
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May aid ECG-based cardiac disorder classification research; leaves open prospective clinical validation before practice adoption.
The deep learning approach of supervised recurrent network classifiers model, i.e., recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent units (GRUs) are used in this study. The unidirectional and bidirectional for each cardiac disorder (CDs) class is also compared. Comparing both phases is needed to figure out the optimum phase and the best model performance for ECG using the Physionet dataset to classify five classes of CDs with 15 leads ECG signals. The result shows that the bidirectional RNNs method produces better results than the unidirectional method. In contrast to RNNs, the unidirectional LSTM and GRU outperformed the bidirectional phase. The best recurrent network classifier performance is unidirectional GRU with average accuracy, sensitivity, specificity, precision, and F1-score of 98.50%, 95.54%, 98.42%, 89.93% 92.31%, respectively. Overall, deep learning is a promising improved method for ECG classification.
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Darmawahyuni et al. (2021) studied this question.
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