Heart Rate Variability: Comparison
Please note this is a comparison between Version 2 by Rita Xu and Version 1 by Xiangyu Han.

Heart Rate Variability (HRV) describes the variation in the time interval between successive heartbeats. It is generally considered to be the result of an interaction between the heart and the brain, which is also called neuro-cardiac function. HRV is controlled by the autonomic nervous system (ANS), including the parasympathetic nervous system (PNS) and sympathetic nervous system (SNS).

  • FMCW radar
  • heartbeat
  • HRV

1. Background

Electrocardiography (ECG) and photoplethysmography (PPG), which reflect the change in body surface electrical potentials and blood volume fluctuations in a superficial body location, respectively, are still the mainstream monitoring methods for HRV [8][1]. However, as contact measurement methods, they both have many limitations in practice. Their lead wires will hinder the user’s physical movement. For electrocardiographs, many electrodes need to be attached to particular locations, which complicates the monitoring process, makes people feel uncomfortable, and puts higher requirements on operators. In addition, they do not apply to certain groups, such as infants, patients with skin burns, and individuals with sleep disorders [9,10,11,12,13][2][3][4][5][6].
In the past 50 years, contactless physiological monitoring has developed rapidly [14][7] and has largely overcome the disadvantages of contact equipment such as electrocardiographs. It can monitor respiration, heartbeat, or other physiological signals without directly contacting the body of the monitored subject and will not make them feel uncomfortable or interfere with their daily routine [15][8], which allows its use for special groups like infants. Monitoring based on millimeter-wave radar is considered a promising noncontact physiological monitoring method that can penetrate non-metallic obstacles, such as clothes or quilts on the surface of the monitored subject, to capture physiological signals of the human body [16][9]. It can be used to probe respiration disorders, such as obstructive sleep apnea (OSA) and sudden infant death syndrome (SIDS), as well as used in medical sleep labs and earthquake or fire search-and-rescue scenarios [17,18][10][11]. The radiofrequency (RF) signal is more robust to temperature changes or environmental thermal noise compared with infrared thermal imaging methods [12][5] and is better able to avoid insufficient image resolution, blind areas, or potential privacy problems compared with vision-based monitoring methods [19][12].
However, measuring the time interval between successive heartbeats and analyzing HRV remain significant challenges due to the weak amplitude of the heartbeat signal and interferences from respiration, trunk movement, and various noises. Most of the existing research in this field focuses on monitoring the respiration rate and heartbeat rate [20][13], while only a few studies focus on the extraction of HRV characteristics.

2. Heart Rate Variability

In 2007, Massagram et al. successfully extracted the time interval between successive heartbeats from echo signals of continuous-wave (CW) radar, proving the feasibility of monitoring HRV with the RF method [21][14]. Early studies on this subject mainly chose CW radar as RF equipment [16,21][9][14]. In some of these studies, the peak corresponding to the heartbeat signal can be clearly distinguished in the demodulated chest movement signal. So, the heartbeat signals can be easily separated by using some filters or using classical signal processing analysis methods, such as autocorrelation, from which the time interval between successive heartbeats can be extracted so that HRV can be measured [22,23,24][15][16][17]. However, CW radar has a weak anti-interference ability. When other subjects appear in the monitoring range, the movement of one individual will affect the measurement and extraction of physiological signals from the other individual, leading to a large measurement error.
FMCW radar provides an important approach to solve this problem. The modulated frequency provides a unique range resolution that CW radar does not have, which enables the separation of the echo signals of objects at specific distances. As a result, FMCW has a strong anti-interference ability and the potential for monitoring multiple subjects simultaneously [25,26][18][19]. Effective algorithms for determining the distance between monitored subjects and radar were also developed [27,28,29][20][21][22]. When there are multiple individuals at the same distance from the radar, their vital signals can still be separated and monitored simultaneously with beamforming technologies [30,31,32][23][24][25].
In practice, the amplitude of the heartbeat waveform demodulated from the FMCW radar echo signal is weak: it is orders of magnitude smaller than the amplitude of the respiration waveform and almost buried in a combination of harmonic respiration signals and noise in the frequency spectrum of the phase sequence. Therefore, the design of the heartbeat signal extraction algorithm plays an important role in monitoring.
A bandpass filter is a common method to separate heartbeat signals from demodulated chest wall motion signals because it is simple to design and easy to implement [23,33,34][16][26][27]. However, it is possible to mistake the harmonics of respiration for heartbeat signals because of their similar amplitudes and frequencies. In addition, it is difficult to accurately extract the time interval of each heartbeat because it is difficult for the filter to eliminate the influence of respiratory harmonics. Most previous studies performed HRV analysis based on the average heart rate over a short time (e.g., 3 s) [33,34][26][27]. Empirical mode decomposition (EMD) is another commonly used heartbeat signal extraction algorithm [35,36,37][28][29][30] that decomposes the chest wall motion according to the time scale characteristics of the signal itself. However, this method has limitations, such as mode aliasing and end effects. In addition, selecting the components of the heartbeat signal from the intrinsic mode function (IMF) remains a technical challenge.
With the development of signal processing technology, some novel algorithms were proposed to extract heartbeat signals and achieved accurate results. Lv et al. used the envelope mid-line method to remove the respiration signal and proposed a stochastic resonance algorithm to enhance the amplitude of heartbeat signals [30][23]. The experimental results were highly consistent with those of PPG detection. The average accuracy of the heart rate value in all subjects reached 96.56%, and the relative error of SDNN was less than 6.53%. Xiong et al. [38][31] proposed a differential enhancement (DE) method, which uses differential operation to significantly enhance the heartbeat components, especially high-order heartbeat harmonics. Combined with the autocorrelation-based periodicity extraction technique, DE can locate the true heartbeat rate (HR). However, it is challenging to accurately extract the duration of each heartbeat because the waveform after the difference operation is complicated, and it is difficult to segment through peak detection. This problem is addressed by the method proposed by Zhao et al. [39][32] with the assumption that successive human heartbeats should have the same morphology. Hence, the corresponding heartbeat waveforms should have the same overall shapes, while they may stretch or compress due to different beat lengths. On this basis, the method transforms the above segmentation problem into an optimization problem and solves the optimal “template” while seeking the optimal segmentation. In their experimental results, the extracted time intervals of each heartbeat are within milliseconds of the ECG signal. However, the algorithm requires a lot of interpolation operations during the segmentation process, which makes it difficult to monitor the HRV index in real time.


  1. Faust, O.; Hong, W.; Loh, H.W.; Xu, S.; Tan, R.S.; Chakraborty, S.; Barua, P.D.; Molinari, F.; Acharya, U.R. Heart rate variability for medical decision support systems: A review. Comput. Biol. Med. 2022, 145, 105407.
  2. Zhao, F.; Li, M.; Qian, Y.; Tsien, J.Z. Remote measurements of heart and respiration rates for telemedicine. PLoS ONE 2013, 8, e71384.
  3. De Haan, G.; Van Leest, A. Improved motion robustness of remote-PPG by using the blood volume pulse signature. Physiol. Meas. 2014, 35, 1913.
  4. Butler, M.; Crowe, J.; Hayes-Gill, B.; Rodmell, P. Motion limitations of non-contact photoplethysmography due to the optical and topological properties of skin. Physiol. Meas. 2016, 37, N27.
  5. AlNaji, A.; Gibson, K.; Lee, S.H.; Chahl, J. Monitoring of cardiorespiratory signal: Principles of remote measurements and review of methods. IEEE Access 2017, 5, 15776–15790.
  6. Al-Naji, A.; Gibson, K.; Chahl, J. Remote sensing of physiological signs using a machine vision system. J. Med. Eng. Technol. 2017, 41, 396–405.
  7. Brüser, C.; Antink, C.H.; Wartzek, T.; Walter, M.; Leonhardt, S. Ambient and unobtrusive cardiorespiratory monitoring techniques. IEEE Rev. Biomed. Eng. 2015, 8, 30–43.
  8. Li, C.; Cummings, J.; Lam, J.; Graves, E.; Wu, W. Radar remote monitoring of vital signs. IEEE Microw. Mag. 2009, 10, 47–56.
  9. Hu, W.; Zhao, Z.; Wang, Y.; Zhang, H.; Lin, F. Noncontact accurate measurement of cardiopulmonary activity using a compact quadrature Doppler radar sensor. IEEE Trans. Biomed. Eng. 2013, 61, 725–735.
  10. Li, C.; Ling, J.; Li, J.; Lin, J. Accurate Doppler radar noncontact vital sign detection using the RELAX algorithm. IEEE Trans. Instrum. Meas. 2009, 59, 687–695.
  11. Zakrzewski, M.; Raittinen, H.; Vanhala, J. Comparison of center estimation algorithms for heart and respiration monitoring with microwave Doppler radar. IEEE Sens. J. 2011, 12, 627–634.
  12. Wang, G.; Gu, C.; Inoue, T.; Li, C. A hybrid FMCW-interferometry radar for indoor precise positioning and versatile life activity monitoring. IEEE Trans. Microw. Theory Tech. 2014, 62, 2812–2822.
  13. Zhai, Q.; Han, X.; Han, Y.; Yi, J.; Wang, S.; Liu, T. A Contactless On-Bed Radar System for Human Respiration Monitoring. IEEE Trans. Instrum. Meas. 2022, 71, 1–10.
  14. Massagram, W.; Lubecke, V.M.; HØst-Madsen, A.; Boric-Lubecke, O. Assessment of heart rate variability and respiratory sinus arrhythmia via Doppler radar. IEEE Trans. Microw. Theory Tech. 2009, 57, 2542–2549.
  15. Hosseini, S.M.A.T.; Amindavar, H. A new Ka-band Doppler radar in robust and precise cardiopulmonary remote sensing. IEEE Trans. Instrum. Meas. 2017, 66, 3012–3022.
  16. Petrović, V.L.; Janković, M.M.; Lupšić, A.V.; Mihajlović, V.R.; Popović-Božović, J.S. High-accuracy real-time monitoring of heart rate variability using 24 GHz continuous-wave Doppler radar. IEEE Access 2019, 7, 74721–74733.
  17. Shih, J.Y.; Wang, F.K. Quadrature Cosine Transform (QCT) With Varying Window Length (VWL) Technique for Noncontact Vital Sign Monitoring Using a Continuous-Wave (CW) Radar. IEEE Trans. Microw. Theory Tech. 2021, 70, 1639–1650.
  18. Antolinos, E.; García-Rial, F.; Hernández, C.; Montesano, D.; Godino-Llorente, J.I.; Grajal, J. Cardiopulmonary activity monitoring using millimeter wave radars. Remote Sens. 2020, 12, 2265.
  19. Lee, H.; Kim, B.H.; Park, J.K.; Yook, J.G. A novel vital-sign sensing algorithm for multiple subjects based on 24-GHz FMCW Doppler radar. Remote Sens. 2019, 11, 1237.
  20. Choi, H.I.; Song, W.J.; Song, H.; Shin, H.C. Improved Heartbeat Detection by Exploiting Temporal Phase Coherency in FMCW Radar. IEEE Access 2021, 9, 163654–163664.
  21. Choi, H.-I.; Song, W.-J.; Song, H.; Shin, H.-C. Selecting target range with accurate vital sign using spatial phase coherency of FMCW radar. Appl. Sci. 2021, 11, 4514.
  22. Choi, H.I.; Song, H.; Shin, H.C. Target range selection of FMCW radar for accurate vital information extraction. IEEE Access 2020, 9, 1261–1270.
  23. Lv, W.; Zhao, Y.; Zhang, W.; Liu, W.; Hu, A.; Miao, J. Remote Measurement of Short-Term Heart Rate with Narrow Beam Millimeter Wave Radar. IEEE Access 2021, 9, 165049–165058.
  24. Mercuri, M.; Sacco, G.; Hornung, R.; Zhang, P.; Visser, H.J.; Hijdra, M.; Liu, Y.H.; Pisa, S.; Van Liempd, B.; Torfs, T. 2-D localization, angular separation and vital signs monitoring using a SISO FMCW radar for smart long-term health monitoring environments. IEEE Internet Things J. 2021, 8, 11065–11077.
  25. Wang, Y.; Shui, Y.; Yang, X.; Li, Z.; Wang, W. Multi-target vital signs detection using frequency-modulated continuous wave radar. EURASIP J. Adv. Signal Process. 2021, 2021, 103.
  26. Kim, J.Y.; Park, J.H.; Jang, S.Y.; Yang, J.R. Peak detection algorithm for vital sign detection using Doppler radar sensors. Sensors 2019, 19, 1575.
  27. Nosrati, M.; Tavassolian, N. High-accuracy heart rate variability monitoring using Doppler radar based on Gaussian pulse train modeling and FTPR algorithm. IEEE Trans. Microw. Theory Tech. 2017, 66, 556–567.
  28. Zhang, J.; Wu, Y.; Chen, Y.; Chen, T. Health-radio: Towards contactless myocardial infarction detection using radio signals. IEEE Trans. Mob. Comput. 2020, 21, 585–597.
  29. Sun, L.; Huang, S.; Li, Y.; Gu, C.; Pan, H.; Hong, H.; Zhu, X. Remote measurement of human vital signs based on joint-range adaptive EEMD. IEEE Access 2020, 8, 68514–68524.
  30. Lv, W.; He, W.; Lin, X.; Miao, J. Non-contact monitoring of human vital signs using FMCW millimeter wave radar in the 120 GHz band. Sensors 2021, 21, 2732.
  31. Xiong, Y.; Peng, Z.; Gu, C.; Li, S.; Wang, D.; Zhang, W. Differential enhancement method for robust and accurate heart rate monitoring via microwave vital sign sensing. IEEE Trans. Instrum. Meas. 2020, 69, 7108–7118.
  32. Zhao, M.; Adib, F.; Katabi, D. Emotion Recognition Using Wireless Signals. Commun. ACM 2018, 61, 91–100.
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