Improving heart rate variability information consistency in Doppler cardiogram using signal reconstruction system with deep learning for Contact-free heartbeat monitoring

A contact-free continuous heart rate variability (HRV) analysis is required to conduct daily heart monitoring and minimize physical contact during medical remedies owing to COVID-19. This paper suggests a Doppler cardiogram (DCG) signal processing and reconstruction system that enables the standard deviation of normal-to-normal peaks (SDNN) obtained from DCG to be used as an actual HRV index. The heartbeat signals of twelve healthy adults were recorded. Three electrodes and a Doppler radar module were used to record the electro-cardiogram (ECG) and DCG signals, respectively. To optimize the performance of the signal reconstruction system, two signal processing methods were applied to the dataset. These DCG signals were reconstructed into a signal that mimicked the ECG using a variational autoencoder (VAE), to enhance the consistency of the SDNN. The synthetic signal quality was assessed by comparing the SDNN of the synthetic ECG with that of the reference ECG. A total of 1,430 signals were reconstructed to achieve a valid SDNN. A unified analysis of the signal reconstruction results using different signal processing methods was built up to raise the consistency growth. The final result of the signal reconstruction system represented a consistency improvement of 75.5%, compared to the SDNN of the input DCG.