ECG Signal Reconstruction Using FMCW Radar and Convolutional Neural Network

This paper presents a method for radar-based contactless vital sensing and ECG (ElectroCardioGram) signal reconstruction using deep learning. ECG signal is a typical representation of heartbeat signals. However, its measurement usually requires any contact device, which is not suitable due to discomfort of subjects. Radar system is effective for vital sensing because it can measure small displacement of body surface caused by breathing and heartbeat without contact. On the other hand, most of the methods using radar system are limited to evaluating simple indices such as heart rate and heartbeat interval while subjects or devices are stationary. In this paper, we propose a method for body surface displacement signals using FMCW (Frequency-Modulated Continuous Wave) radar and reconstructing ECG signals using CNN (Convolutional Neural Network). The result of experiments on six healthy males shows the ECG signals are successfully reconstructed. Furthermore, we confirmed that the proposed method can reconstruct signal waveforms even in an environment with low SNR (Signal-to-Noise Ratio).