New Hybrid Deep Learning Approach Using BiGRU-BiLSTM and Multilayered Dilated CNN to Detect Arrhythmia

Abstract

Deep learning methods have shown early progress in analyzing complicated ECG signals, especially in heartbeat classification and arrhythmia detection. There is still a great deal of space for further research on this area before reaching a definite decision. This study introduced a novel hybrid framework based on a bidirectional recurrent neural network (BiRNN) with a multilayered dilated convolution neural network (CNN) for arrhythmia classification. Initially, the raw ECG signals are filtered using Chebyshev Type II method and the Daubechies wavelet method is used to solve fractal problems and signal discontinuities. Then, a synthetic signal is generated using a generative adversarial network (GAN) to handle imbalanced signal classes. The proposed Bidirectional RNN with Dilated CNN (BRDC) architecture takes advantage of multilayered dilated CNN and bidirectional RNN units (bidirectional gated recurrent Units, BiGRU, bidirectional long short-term memory, BiLSTM) to generate fusion features and then, fusion features are classified in the fully connected layer. The PhysioNet 2017 challenge (MIT-BIH) dataset is used to train and validate the proposed approach. By combining fusion features with dilated CNN, the proposed approach outperforms the existing model for arrhythmia detection with 99.90 % accuracy, 98.41 % F1-score, 97.96 % precision, and 99.90 % recall. Overall, our hybrid BRDC model provides a cost-effective ECG signal reduction and high-performance automated recognition technique to identify arrhythmia. In the future, an automatic and cloud-based system with more arrhythmia data variance to test the model’s robustness will be given the highest priority.