Research on stack-based LSTM adaptive ECG mapping method

Sudden cardiovascular diseases impose a significant burden on individuals’ health and life, with high mortality and disability rates. Most portable electrocardiogram collection devices collect ECG signals in vector ECG format, which differs from standard ECG signals, making it difficult for doctors to diagnose diseases. To address this issue, we designed a human-engineered “palm” rapid ECG collection system based on flexible sensing materials. Additionally, we implemented an individualized adaptive ECG mapping algorithm using a stack LSTM network to map non-standard ECG signals collected by the portable ECG collection front-end to standard signals. To evaluate the performance of our approach, we conducted a comparative analysis experiment on ECG data collected from 30 participants. Our results show that the correlation between the “palm” rapid ECG graph obtained using our proposed mapping algorithm and the standard 12-lead ECG graph was 97.45 %, with a RMSE of 0.09 mV. These findings indicate that our approach has significant implications for optimizing signal analysis of wearable ECG collection devices.