Evaluating Time-Series Deep Learning Models for Accurate and Efficient Reconstruction of Clinical 12-Lead ECG Signals

With the transformation of healthcare systems, the aging population, and the impact of the COVID-19 pandemic, interest in the healthcare industry has significantly increased. In particular, demand has grown for personal electrocardiogram (ECG) monitoring using wearable devices, such as smartwatches. However, these devices usually only record single-lead ECG signals, which are not as accurate for diagnosing cardiac conditions as the standard 12-lead ECGs used in clinical settings. Deep learning models have been actively explored to reconstruct standard 12-lead ECG signals from several leads to overcome this limitation. Nevertheless, comprehensive comparative studies on time-series-based deep learning models capable of generating clinically equivalent ECG signals are lacking. In this study, we designed and evaluated three recurrent neural network (RNN) architectures LSTM, Bi-LSTM, and GRU to reconstruct 12-lead ECG signals from a limited subset of leads and compared their reconstruction performance. Each model consists of three RNN layers, batch normalization, and dropout layers. Experimental results on the PTB-XL dataset show that the GRU model achieves the best reconstruction performance, with a PCC of 0.911, MAE of 0.033 and RMSE of 0.065, outperforming Bi-LSTM (PCC: 0.893, MAE: 0.035, RMSE: 0.072) and LSTM (PCC: 0.819, MAE: 0.039, RMSE: 0.088). This study systematically compares the performance of RNN-based models for ECG signal reconstruction and identifies GRU as the most effective architecture for reconstructing standard 12-lead ECGs from single-lead inputs. Due to its relatively lightweight structure, the proposed model shows strong potential for real-time implementation on wearable devices and has valuable implications for mobile healthcare and remote diagnostic systems.