Affiliation:
1. School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
Abstract
The potential of microwave Doppler radar in non-contact vital sign detection is significant; however, prevailing radar-based heart rate (HR) and heart rate variability (HRV) monitoring technologies often necessitate data lengths surpassing 10 s, leading to increased detection latency and inaccurate HRV estimates. To address this problem, this paper introduces a novel network integrating a frequency representation module and a residual in residual module for the precise estimation and tracking of HR from concise time series, followed by HRV monitoring. The network adeptly transforms radar signals from the time domain to the frequency domain, yielding high-resolution spectrum representation within specified frequency intervals. This significantly reduces latency and improves HRV estimation accuracy by using data that are only 4 s in length. This study uses simulation data, Frequency-Modulated Continuous-Wave radar-measured data, and Continuous-Wave radar data to validate the model. Experimental results show that despite the shortened data length, the average heart rate measurement accuracy of the algorithm remains above 95% with no loss of estimation accuracy. This study contributes an efficient heart rate variability estimation algorithm to the domain of non-contact vital sign detection, offering significant practical application value.
Funder
Science, Technology and Innovation Commission of Shenzhen Municipality
Science and Technology Planning Project of the Key Laboratory of Advanced IntelliSense Technology, Guangdong Science and Technology Department
Reference42 articles.
1. Alkhodari, M., Jelinek, H.F., Werghi, N., Hadjileontiadis, L.J., and Khandoker, A.H. (2020, January 20–24). Investigating Circadian Heart Rate Variability in Coronary Artery Disease Patients with Various Degrees of Left Ventricle Ejection Fraction. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.
2. Schroeder, E.B. (2003). Determinants of the Longitudinal Change in Heart Rate Variability: The Atherosclerosis Risk in Communities Study, The University of North Carolina at Chapel Hill.
3. Morshed, M.G., Mukit, M.A., Ahmed, K.I.U., Mostafa, R., Parveen, S., and Khandoker, A.H. (2020, January 5–7). Heart rate variability analysis for diagnosis of diabetic peripheral neuropathy. Proceedings of the 2020 IEEE Region 10 Symposium (TENSYMP), Dhaka, Bangladesh.
4. Sivanantham, A., and Devi, S.S. (2014, January 8–10). Cardiac arrhythmia detection using linear and non-linear features of HRV signal. Proceedings of the 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies, Ramanathapuram, India.
5. A United CNN-LSTM Algorithm Combining RR Wave Signals to Detect Arrhythmia in the 5G-Enabled Medical Internet of Things;Zhang;IEEE Internet Things J.,2021
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献