Enhanced 12-Lead ECG Reconstruction from Single-Lead Data Using WaveNet

The Electrocardiogram (ECG) is a fundamental tool in clinical practice for diagnosing a variety of heart conditions. Traditional ECG systems require a complete set of 12 leads collected in a clinical environment, which can be time-consuming and costly. Recent advancements in wearable technology, such as smartwatches, allow for the collection of ECG signals in a more convenient manner, but typically only provide a single lead. This paper presents a novel approach to reconstructing the full 12-lead ECG from a single lead using WaveNet. The WaveNet model offers flexibility in handling signal segments of varying durations, while exceling in capturing complex data dependencies. Our models achieve superior performance in terms of signal reconstruction quality, demonstrated by a significant improvement in Pearson correlation coefficients, RMSE and SSIM. This work paves the way for more accessible and cost-effective ECG diagnostics, potentially revolutionizing cardiac care with wearable devices.