Feasibility and validity of using deep learning to reconstruct 12-lead ECG from three­lead signals.

BACKGROUND: In the field of mobile health, portable dynamic electrocardiogram (ECG) monitoring devices often have a limited number of lead electrodes due to considerations, such as portability and battery life. This situation leads to a contradiction between the demand for standard 12­lead ECG information and the limited number of leads collected by portable devices. METHODS: This study introduces a composite ECG vector reconstruction network architecture based on convolutional neural network (CNN) combined with recurrent neural network by using leads I, II, and V2. This network is designed to reconstruct three­lead ECG signals into 12­lead ECG signals. A 1D CNN abstracts and extracts features from the spatial domain of the ECG signals, and a bidirectional long short-term memory network analyzes the temporal trends in the signals. Then, the ECG signals are inputted into the model in a multilead, single-channel manner. RESULTS: Under inter-patient conditions, the mean reconstructed Root mean squared error (RMSE) for precordial leads V1, V3, V4, V5, and V6 were 28.7, 17.3, 24.2, 36.5, and 25.5 µV, respectively. The mean overall RMSE and reconstructed Correlation coefficient (CC) were 26.44 µV and 0.9562, respectively. CONCLUSION: This paper presents a solution and innovative approach for recovering 12­lead ECG information when only three­lead information is available. After supplementing with comprehensive leads, we can analyze the cardiac health status more comprehensively across 12 dimensions.