Cited by 69Non-Invasive Vital Sign MonitoringHeart Rate Variability and Autonomic ControlECG Monitoring and Analysis

Analysis of a Pulse Rate Variability Measurement Using a Smartphone Camera

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Bánhalmi, András, Borbás, János, Fidrich, Márta, Bilicki, Vilmos, Gingl, Zoltán, Rudas, László·2018·Journal of Healthcare Engineering

Abstract

Background. Heart rate variability (HRV) provides information about the activity of the autonomic nervous system. Because of the small amount of data collected, the importance of HRV has not yet been proven in clinical practice. To collect population-level data, smartphone applications leveraging photoplethysmography (PPG) and some medical knowledge could provide the means for it. Objective. To assess the capabilities of our smartphone application, we compared PPG (pulse rate variability (PRV)) with ECG (HRV). To have a baseline, we also compared the differences among ECG channels. Method. We took fifty parallel measurements using iPhone 6 at a 240 Hz sampling frequency and Cardiax PC-ECG devices. The correspondence between the PRV and HRV indices was investigated using correlation, linear regression, and Bland-Altman analysis. Results. High PPG accuracy: the deviation of PPG-ECG is comparable to that of ECG channels. Mean deviation between PPG-ECG and two ECG channels: RR: 0.01 ms–0.06 ms, SDNN: 0.78 ms–0.46 ms, RMSSD: 1.79 ms–1.21 ms, and pNN50: 2.43%–1.63%. Conclusions. Our iPhone application yielded good results on PPG-based PRV indices compared to ECG-based HRV indices and to differences among ECG channels. We plan to extend our results on the PPG-ECG correspondence with a deeper analysis of the different ECG channels.

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  1. [22]Lin G. H. Chang Y. H. Lin K. P. Comparison of heart rate variability measured by ECG in different signal lengths Journal of Medical and Biological Engineering 2005 25 2 67 71
  2. [25]Clifford G. D. Signal processing methods for heart rate variability, [Ph.D. thesis] 2002 University of Oxford
  3. [26]Drijkoningen L. Lenaerts F. Van der Auwera J. Validation of a smartphone based photoplethysmographic beat detection algorithm for normal and ectopic complexes Computing in Cardiology 2014 2014 Cambridge, MA, USA 845 848

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Authors
Bánhalmi, A., Borbás, J., Fidrich, M., Bilicki, V., Gingl, Z., & Rudas, L.
Affiliations
University of Szeged (HU)
Corresponding author
Bánhalmi, András
Venue
Journal of Healthcare Engineering
Publisher
Wiley
Published
2018
Volume
2018
Pages
1-15
ISSN
  • 2040-2295 (ISSN-L)
  • 2040-2309
OpenAlex
W2794220693
Cited by
69
References
29
Citations by year
2026: 3 · 2025: 5 · 2024: 5 · 2023: 10 · 2022: 9 · 2021: 15 · 2020: 10 · 2019: 11 · 2018: 1
FWCI
3.70
Type
journal article
Language
en
Open access
Locations
  • Journal of Healthcare Engineering (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • SZTE Publicatio Repozitórium (University of Szeged) (acceptedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • European Regional Development Fund
  • European Union
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
Keywords
pulse rate variability, heart rate variability, photoplethysmography, smartphone camera, mobile application, ECG comparison, autonomic nervous system, RR interval, SDNN, RMSSD, pNN50
MeSH terms
Smartphone, Adult, Electrocardiography, Female, Heart Rate, Humans, Male, Pulse, Signal Processing, Computer-Assisted, Photoplethysmography

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