Inception CNN-Transformer for Robust PPG-to-ECG Reconstruction

In recent years, wearable healthcare devices and robots have become increasingly crucial in the face of population aging and the rising prevalence of chronic diseases, including cardiovascular disease, which remains a leading cause of mortality worldwide. These conditions amplified the demand for advanced, continuous monitoring of cardiovascular health. While electrocardiogram (ECG) signals offer comprehensive diagnostic insights, their reliance on multiple electrodes often limits practicality. In contrast, photoplethysmogram (PPG) signals are more convenient to acquire but lack the rich detail of ECG. In this paper, we propose a novel PPG-to-ECG reconstruction method that combines an Inception CNN for multiscale feature extraction with a Transformer architecture for capturing global dependencies. Our proposed method achieves robust ECG signal reconstruction even under high-noise conditions by effectively preserving local morphological details and leveraging long-range contextual information. We validate the proposed approach on diverse datasets spanning everyday life and intensive care unit (ICU) settings, demonstrating high accuracy and generalizability. Experimental results indicate an RMSE of 0.26, corresponding to a 10% improvement over state-of-the-art methods. These findings highlight the feasibility of reliable, real-time ECG reconstruction from PPG signals alone, paving the way for scalable and accessible healthcare monitoring solutions in clinical and wearable contexts.