Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions.
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In this paper we assessed the possibility of using the pulse rate variability (PRV) extracted from the photoplethysmography signal as an alternative measurement of the HRV signal in non-stationary conditions. The study is based on analysis of the changes observed during a tilt table test in the heart rate modulation of 17 young subjects. First, the classical indices of HRV analysis were compared to the indices from PRV in intervals where stationarity was assumed. Second, the time-varying spectral properties of both signals were compared by time-frequency (TF) and TF coherence analysis. Third, the effect of replacing PRV with HRV in the assessment of the changes of the autonomic modulation of the heart rate was considered. Time-invariant HRV and PRV indices showed no statistically significant differences (p > 0.05) and high correlation (>0.97). Time-frequency analysis revealed that the TF spectra of both signals were highly correlated (0.99 +/- 0.01); the difference between the instantaneous power, in the LF and HF bands, obtained from HRV and PRV was small (<10(-3) s(-2)) and their temporal patterns were highly correlated (0.98 +/- 0.04 and 0.95 +/- 0.06 in the LF and HF bands, respectively) and TF coherence in the LF and HF bands was high (0.97 +/- 0.04 and 0.89 +/- 0.08, respectively). Finally, the instantaneous power in the LF band was observed to significantly increase during head-up tilt by both HRV and PRV analysis. These results suggest that although some differences in the time-varying spectral indices extracted from HRV and PRV exist, mainly in the HF band associated with respiration, PRV could be used as a surrogate of HRV during non-stationary conditions, at least during the tilt table test.
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Photoplethysmography is considered to represent an accurate approximation of the IBI.
“Photoplethysmography is considered to represent an accurate approximation of the IBI (Gil et al., 2010).”
From Heart Rate Variability and Cardiac Vagal Tone in Psychophysiological Research – Recommendations for Experiment Planning, Data Analysis, and Data Reporting (2017) · cites this paper as Gil et al., 2010
The Data and signal preprocessing section defines intervals between PPG pulses alongside ECG beat intervals, and the Conclusion reports strong agreement between variability measures derived from them. But the paper does not establish that PPG accurately approximates each individual interbeat interval: the Introduction and Limitations explain that pulse-transit-time variability and pulse-detection error create differences. (LLM, conf=0.84)
Experimental — accuracy not yet measuredTo characterize stress, arterial pulsewave signals are transformed into HRV, which represents the chronic stress level and an individual's stress vulnerability.
“To characterize stress, arterial pulsewave signals are transformed into HRV, which represents the chronic stress level and an individual’s stress vulnerability.98”
From Wearable sensors for monitoring the physiological and biochemical profile of the athlete (2019) · cites this paper as [98]
The stress signals of skin temperature, skin conductance, and pulsewave are dependent on the stimulation time and categorized into acute stress (response under 3 seconds stimulation) and chronic stress (response over 3 seconds stimulation).
“The stress signals of skin temperature, skin conductance, and pulsewave are dependent on the stimulation time and categorized in two types: acute stress (response under 3 seconds stimulation)11 and chronic stress (response over 3 seconds stimulation)39.”
From A Flexible and Wearable Human Stress Monitoring Patch (2016) · cites this paper as [39]
For stress characterization, arterial pulsewave signals are required to be transformed into human heart rate variability (HRV).
“For stress characterization, the arterial pulsewave signals is required to be transformed into the human heart rate variability (HRV)39.”
From A Flexible and Wearable Human Stress Monitoring Patch (2016) · cites this paper as [39]
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- Authors
- Gil, E., Orini, M., Bailón, R., Vergara, J. M., Mainardi, L., & Laguna, P.
- Affiliations
- Universidad de Zaragoza (ES)
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (ES)
- Politecnico di Milano (IT)
- Venue
- Physiological Measurement
- Publisher
- IOP Publishing
- Published
- 2010-08-11
- Volume
- 31
- Issue
- 9
- Pages
- 1271-1290
- ISSN
- 0967-3334 (ISSN-L)
- 1361-6579
- OpenAlex
- W2010524207
- Cited by
- 461
- References
- 49
- Citations by year
- 2026: 14 · 2025: 21 · 2024: 26 · 2023: 42 · 2022: 44 · 2021: 36 · 2020: 42 · 2019: 44 · 2018: 39 · 2017: 38 · 2016: 28 · 2015: 36 · 2014: 15 · 2013: 13 · 2012: 13
- FWCI
- 7.90
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Physiological Measurement (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion)
- Funders
- Ministerio de Ciencia y Tecnología
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Disease Prevention
- Keywords
- photoplethysmography, pulse rate variability, heart rate variability, non-stationary signals, tilt table test, time-frequency analysis, time-frequency coherence, respiration, surrogate measurement
- MeSH terms
- Autonomic Nervous System, Female, Heart Rate, Humans, Male, Time Factors, Photoplethysmography, Tilt-Table Test
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