Beat-by-Beat ECG Monitoring from Photoplythmography Based on Scattering Wavelet Transform

The electrocardiogram (ECG) is a popular measurement scheme to assess and diagnose cardiovascular diseases. ECG devices use gel material and electrodes that may cause skin irritation and discomfort during long use, which restricts the long-term use of these devices. On the other hand, Photoplethysmography (PPG) is an optical approach used to estimate the skin blood flow using photons. Recently, the relationship between the PPG and ECG has been recognized and there are early stage attempts to reconstruct the ECG signals from PPG signals that can lead to giving up electrodes and skin irritating and uncomfortable materials. However, these recent researches suffer from the sensitivity to the PPG signal quality, shifting, and scaling. Therefore, it is restricted with constrained PPG signals. In this paper, we propose an ECG reconstruction system that is independent of PPG scaling and shifting. The proposed system is based on scattering wavelet transform (SWT) as a feature domain along with the deep learning network. Using SWT helps deep learning networks to learn the non-linear relationship between ECG and PPG even with small datasets. Also, the proposed system is based on beat-by-beat ECG estimation rather than signal-based which leads to the learning of local features rather than global features. Based on the presented simulation results, the proposed system with beat-by-beat SWT features extraction outperforms the other feature domains; Time, DCT, and DWT domains.