Why the study?
While ECG is the gold standard for heart rate monitoring, contactless monitoring using radar systems to measure chest movements is preferred for applications like long-term monitoring.
Does a bidirectional GRU network using Doppler radar improve heartbeat detection accuracy compared to state-of-the-art methods?
Does a bidirectional GRU network using Doppler radar improve heartbeat detection accuracy compared to state-of-the-art methods?
A novel bidirectional GRU network using Doppler radar provides highly accurate, contactless heartbeat detection, outperforming existing methods.
Supports contactless radar monitoring in lab settings; leaves open clinical validation and real-world deployment.
Heart rate is one of the most critical and important vital signs in healthcare. While electrocardiography (ECG) is gold-standard procedure for heart rate monitoring, contactless monitoring is preferred in many applications like long-term monitoring. Radar systems enable contactless sensing by measuring small movements on the chest induced by the heart beat. In this paper, we present a machine learning-based method using a bidirectional gated recurrent unit (bi-GRU) network for accurate heartbeat detection. Band-pass filtered in-phase (I) and quadrature (Q) signals in heart sound and pulse wave frequency ranges were fused. The proposed method achieves a high F1 score of 98.06% for heart beat detection, thus outperforming the state-of-the-art method with an F1 score of 95.62% in the resting scenario. In the tilt-up scenario with the tilt table, F1 score is significantly improved by 10%. Besides, a median inter-beat intervals (IBIs) RMSE of only 22.07 ms in the resting scenario is realized.
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Lu et al. (2023) studied this question.
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