Reducing lead requirements for wearable ECG: Chest lead reconstruction with 1D-CNN and Bi-LSTM

Wearable ECG devices encounter significant challenges in replicating the diagnostic capabilities of standard 12-lead ECGs, primarily due to the complexity of electrode placement and the need for specialized equipment. This study aims to develop a deep learning model capable of reconstructing complete 12-lead ECG waveforms using a minimal number of chest leads, thereby optimizing lead configurations for wearable ECG systems. Leveraging the PTB-XL ECG dataset, we preprocessed the signals to eliminate noise and trained a model integrating 1D convolutional layers with a Bi-directional Long Short-Term Memory (Bi-LSTM) architecture. Reconstruction performance was assessed using Pearson's correlation coefficient and root mean squared error (RMSE) across various input lead configurations, ranging from single to quintuple inputs. Our preprocessing and network architecture effectively capture both spatial and temporal features. The model achieved its highest reconstruction accuracy for leads located near the input leads, with performance gradually diminishing for more distant leads. Notably, the transitional zone between leads V₃ and V₄ presented reconstruction challenges due to polarity shifts. While increasing the number of input leads enhanced reconstruction accuracy and reduced variability, the improvements plateaued beyond the use of double input leads. Among configurations, double input leads, particularly those with two intervening leads between input pairs, offered an optimal balance between reconstruction accuracy and model complexity. This study highlights that accurate reconstruction of 12-lead ECG is achievable with only two input leads, providing a balance between diagnostic accuracy and reduced electrode requirements. These findings offer valuable insights for designing wearable ECG systems capable of reliable monitoring with fewer electrodes.