RaECG: mmWave Radar-based Electrocardiogram Monitoring Using Chest Vibration and Carotid Pulse

Abstract

Cardiovascular diseases typically require continuous, long-term electrocardiogram (ECG) monitoring to detect intermittent cardiac abnormalities. Currently, ECG monitoring generally relies on wearable devices, which face the issues of limited battery life and electrode shedding, resulting in monitoring discontinuity. In contrast, mmWave radar enables non-contact vital sign monitoring by detecting subtle chest vibration. Based on this, we present a novel mmWave radar-based ECG monitoring system, RaECG, which perceives both heartbeat-induced chest vibration and carotid pulse from the neck and employs spatiotemporal feature fusion to improve ECG reconstruction accuracy. RaECG adopts a respiration-guided interference suppression by enhancing the amplitude of the echo signals caused by chest vibrations and a dynamic time warping (DTW)-based signal evaluation algorithm to accurately capture the signals from heartbeat-induced chest vibration and carotid pulse. Then, a parallel neural network is designed to extract spatiotemporal features, where the Squeeze-and-Excitation-enhanced TCN module dynamically weights features from the chest vibration and carotid pulse, combined with the Global Attention-enhanced BiGRU to model ECG periodicity. The experimental results show the effectiveness of RaECG in ECG reconstruction and achieve high morphology accuracy with a median Pearson correlation coefficient of 0.941 and a median root mean square error of 0.078mV in amplitude.