ECG Adaptive Synthesis With Multigranular Fuzzy EASNN: Enhancing Wearable Cardiac Monitoring

Wearable electrocardiographic (ECG) devices have transcended the limitations of long-term Holter monitoring. They provide a solution for early screening of abnormal cardiac conditions. However, in most wearable devices, less than 50% of the collected ECG signals are complete and usable as 12- lead ECG segments, potentially leading to missed detections of paroxysmal cardiac conditions. To address these issues, this study developed an ECG Adaptive Synthesis Neural Network (EASNN) designed to enhance the proportion of usable signals in dynamic 12-lead ECG recordings. Building on the proposed composite 18-lead ECG system, the EASNN incorporates lead masking and temporal-lead embedding. It uses additional leads to provide supplementary cardiac status information, which enhances the robustness of the dynamic 12-lead ECG synthesis. The designed multigranular fuzzy cross-attention module achieves multigranular cross-channel embedding for ECG synthesis, helping to effectively capture the structural correlations between the ECG leads. Inspired by relational memory, we incorporated lead attention deep memory within the encoder to capture critical information on the inter-lead relations, thus deepening the model's understanding of the relationships between the leads. Experimental results demonstrated that the EASNN outperformed state-of-the-art time series forecasting algorithms. It achieved up to 97.87% consistency in expert-annotated experiments, indicating that the EASNN-reconstructed ECG closely matched the recorded ECG signals, thus providing valuable support for ECG diagnosis. Notably, EASNN is the first deep neural network specifically designed for the reconstruction of low-quality ECG signals, offering significant application value in dynamic cardiac monitoring