Synthesis of chest-lead ECG using temporal convolutional networks

Cardiovascular diseases (CVDs) are a leading cause of mortality globally, and therefore timely and accurate diagnosis is crucial to patient safety. The standard 12-lead electrocardiography (ECG) is routinely used to diagnose heart disease. Most wearable monitoring devices provide insufficient ECG information because of the limitations in the number of leads and measurement positions. This study presents a patient-specific chest-lead synthesis method based on temporal convolutional network (TCN) to exploit both intra- and inter-lead correlations of ECG signals. Performance can be further enhanced by using the variational mode decomposition (VMD), which reduces the non-stationary characteristic of ECG signals and helps to improve the synthesis accuracy. Experiments on PTB diagnostic database demonstrate that the proposed method is effective and has good performance in synthesis of chest-lead ECG signals from a single limb lead.