Non-Contact Reconstruction of ECG Signals Using FMCW Radar and SS-S2SA Network

The electrocardiogram (ECG) signals P-wave, QRS wave and T-wave all reflect the activity of the heart, and the analysis of ECG signals can provide basic information for the diagnosis and prevention of heart disease. In the work of this paper, frequency-modulated continuous-wave (FMCW) radar and deep learning network are utilized to acquire ECG signals non-contactly, and we propose an improved differential and cross multiply (DACM) algorithm and a multi-neighbor differentiator for extracting cardiac motion acceleration information, as well as a partitioned reconstruction network incorporating an attention mechanism of encoder-decoder to achieve ECG signal reconstruction. The design principle is a combination of signal segmentation and deep learning (Sequence-to-sequence and attention) called SS-S2SA. firstly, a segmentation algorithm is applied to segment the acceleration signal and the ECG signal synchronously, and then the cardiac motion acceleration signal is mapped to the ECG signal using the SS-S2SA network. The method proposed in this paper is demonstrated to reconstruct ECG signals more accurately and finely by training more than 18,000 acceleration signal segments from 10 healthy subjects and evaluating the predictions from 5 subjects. The average correlation coefficient between the predicted signal and the real signal is about 0.92, and the mean absolute error (MAE) of the timing of the P-peak, R-peak, and T-peak are 13.9ms, 8.1 ms, and 11.1 ms, respectively.