MIMO-Net: Multi-Input Multi-Output Deep Learning Network for Full 12-Lead ECG Reconstruction from a Single Lead

The standard 12-lead ECG is the gold-standard tool for cardiac diagnosis and monitoring; however, its multi-electrode configuration limits its use in prolonged monitoring and wearable devices. Existing portable ECG devices typically record only a single lead, reducing their diagnostic value. To overcome this limitation, this study proposes novel Multi-Input Multi-Output neural networks (MIMO-Nets) that reconstruct a standard 12-lead ECG from a single raw ECG lead without requiring any feature extraction techniques. Four deep learning architectures were investigated: MIMO-Conv-Net, MIMO-U-Net, Conv-Net, and U-Net. Each model was trained and evaluated using all 12 standard ECG leads with different length L as candidate inputs to determine the optimal single-lead acquisition strategy. Results show that Lead II with L = 256 consistently provides the highest reconstruction performance across all architectures, making it the most informative single-lead input. Using Lead II as input, MIMO-U-Net yielded the highest reconstruction accuracy on a processed clinical dataset (ST-Petersburg INCART), with an average correlation of 0.864 at L = 256, closely followed by U-Net (0.862). On raw, unprocessed recordings from the PTB-XL database, both models achieved lower but still competitive correlations (0.740 and 0.732, respectively). These findings demonstrate the feasibility of single-lead-to-12-lead ECG reconstruction and highlight the role of signal preprocessing in reconstruction fidelity, supporting the development of wearable ECG systems for continuous cardiac monitoring.