Congestive Heart Failure Detection From ECG Signals Using Deep Residual Neural Network

The early and accurate detection of congestive heart failure (CHF) using an electrocardiogram (ECG) is of great significance for improving the survival rate of patients. Existing approaches show limited detection accuracy as they fail to capture the temporal ECG dynamics. Also, these methods lack model transparency and are often difficult to interpret. This article proposes a novel end-to-end diagnostic attention-based deep residual recurrent neural network (DA-DRRNet) that effectively captures the temporal dynamics and extracts high-level attentive representations for accurate CHF detection. Specifically, we first employ a recurrent neural network (RNN) layer to encode the temporal dynamics from the raw ECG beats. Then, multilayered RNNs with residual connections are incorporated to extract high-level feature representations hierarchically. The residual connections allow gradients in deep RNN to propagate effectively, thereby improving the network's representation ability. Finally, an attention module identifies the hidden vectors corresponding to the diagnostically prominent ECG characteristics to form an attentive representation for improved CHF detection. Using ECG signals from the three publicly available datasets (BIDMC-CHF, PTBDB, and MIT-BIH NSRDB), the proposed method achieves an impressive accuracy of 98.57 $\%$ and nearly 100 $\%$ for beat-level and 24-h record-level diagnosis, respectively. Notably, the analysis of learned attention weights demonstrates that the proposed model focuses on the clinically relevant ECG features that characterize CHF. This model transparency and improved detection results advance research in this field and provide a reliable and transparent diagnostic system for CHF analysis.