A novel deep residual-dense network combined with a bidirectional recurrent neural network achieved 97.72% accuracy, 93.09% sensitivity, and 98.71% specificity for atrial fibrillation detection.
Does a deep residual-dense network based on bidirectional RNN accurately detect atrial fibrillation from ECG signals?
A novel deep residual-dense network combined with bidirectional RNN achieves high accuracy (97.72%) in detecting atrial fibrillation from single-lead ECG signals, demonstrating potential for portable mobile medical applications.
Atrial fibrillation easily leads to stroke, cerebral infarction and other complications, which will seriously harm the life and health of patients. Traditional deep learning methods have weak anti-interference and generalization ability. Therefore, we propose a new-fashioned deep residual-dense network via bidirectional recurrent neural network (RNN) model for atrial fibrillation detection. The combination of one-dimensional dense residual network and bidirectional RNN for atrial fibrillation detection simplifies the tedious feature extraction steps, and constructs the end-to-end neural network to achieve atrial fibrillation detection through data feature learning. Meanwhile, the attention mechanism is utilized to fuse the different features and extract the high-value information. The accuracy of the experimental results is 97.72%, the sensitivity and specificity are 93.09% and 98.71%, respectively compared with other methods.
Laghari et al. (Wed,) conducted a other in Atrial fibrillation (n=6,877). Deep residual-dense network based on bidirectional recurrent neural network vs. Other deep learning methods (CNN-LSTM, OTE) was evaluated on Atrial fibrillation detection accuracy. A novel deep residual-dense network combined with a bidirectional recurrent neural network achieved 97.72% accuracy, 93.09% sensitivity, and 98.71% specificity for atrial fibrillation detection.