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Int J Sports Med 2015; 36(06): 474-480
DOI: 10.1055/s-0034-1398530
Training & Testing
© Georg Thieme Verlag KG Stuttgart · New York

Facial Video-Based Photoplethysmography to Detect HRV at Rest

J. Moreno
1   Laboratory of Sport Psychology, Universitat Autònoma de Barcelona, Bellaterra, Spain
4   Health&SportLAB, PRUAB, Bellaterra, Spain
,
J. Ramos-Castro
2   Electronic Engineering Department, Universitat Politècnica de Catalunya, Barcelona, Spain
,
J. Movellan
3   Emotient.com, San Diego CA, USA
,
E. Parrado
1   Laboratory of Sport Psychology, Universitat Autònoma de Barcelona, Bellaterra, Spain
,
G. Rodas
5   Medical Services F.C. Barcelona, F.C. Barcelona, Barcelona, Spain
,
L. Capdevila
1   Laboratory of Sport Psychology, Universitat Autònoma de Barcelona, Bellaterra, Spain
4   Health&SportLAB, PRUAB, Bellaterra, Spain
› Author Affiliations
Further Information

Publication History



accepted after revivion 04 November 2014

Publication Date:
20 February 2015 (online)

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Abstract

Our aim is to demonstrate the usefulness of photoplethysmography (PPG) for analyzing heart rate variability (HRV) using a standard 5-min test at rest with paced breathing, comparing the results with real RR intervals and testing supine and sitting positions. Simultaneous recordings of R–R intervals were conducted with a Polar system and a non-contact PPG, based on facial video recording on 20 individuals. Data analysis and editing were performed with individually designated software for each instrument. Agreement on HRV parameters was assessed with concordance correlations, effect size from ANOVA and Bland and Altman plots. For supine position, differences between video and Polar systems showed a small effect size in most HRV parameters. For sitting position, these differences showed a moderate effect size in most HRV parameters. A new procedure, based on the pixels that contained more heart beat information, is proposed for improving the signal-to-noise ratio in the PPG video signal. Results were acceptable in both positions but better in the supine position. Our approach could be relevant for applications that require monitoring of stress or cardio-respiratory health, such as effort/recuperation states in sports.

Key words

photoplethysmography - heart rate variability - facial video signal - cardio-respiratory fitness - autonomic nervous system - computer vision
 

  • References

  • 1 Acharya UR, Joseph KP, Kannathal N, Lim CM, Suri JS. Heart rate variability: a review. Med Biol Eng Comput 2006; 44: 1031-1051
    CrossrefPubMedGoogle Scholar
  • 2 Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiol Meas 2007; 28: 1-39
    CrossrefPubMedGoogle Scholar
  • 3 Anosov O, Patzak A, Kononovich Y, Persson PB. High-frequency oscillations of the heart rate during ramp load reflect the human anaerobic threshold. Eur J Appl Physiol 2000; 83: 388-3104
    CrossrefPubMedGoogle Scholar
  • 4 Bland JM, Altman DG. Agreement between methods of measurement with multiple observations per individual. J Biopharm Stat 2007; 17: 571-582
    CrossrefPubMedGoogle Scholar
  • 5 Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986; 1: 307-310
    PubMedGoogle Scholar
  • 6 Bosquet L, Merkari S, Arvisais D, Aubert AE. Is heart rate a convenient tool to monitor over-reaching? A systematic review of the literature. Br J Sports Med 2008; 42: 709-714
    CrossrefPubMedGoogle Scholar
  • 7 Buchheit M, Gindre C. Cardiac parasympathetic regulation: respective associations with cardiorespiratory fitness and training load. Am J Physiol 2006; 291: H451-H458
    CrossrefPubMedGoogle Scholar
  • 8 Camm AJ, Malik M, Bigger JT, Breithardt G, Cerutti S, Cohen RJ, Coumel P, Fallen EL, Kennedy HL, Kleiger RE, Lombardi F, Malliani A, Moss AJ, Rottman JN, Schmidt G, Schwartz PJ, Singer D. Heart rate variability-standards of measurement, physiological interpretation, and clinical use. Circulation 1996; 93: 1043-1065
    CrossrefPubMedGoogle Scholar
  • 9 Charlot K, Cornolo J, Brugniaux JV, Richalet JP, Pichon A. Interchangeability between heart rate and photoplethysmography variabilities during sympathetic stimulations. Physiol Meas 2009; 30: 1357-1369
    CrossrefPubMedGoogle Scholar
  • 10 Cohen J. (ed.) Statistical Power Analysis for the Behavioral Sciences. Hillsdale: Lawrence Erlbaum Associates; 1988
  • 11 Cottin F, Médigue C, Lopes P, Leprêtre PM, Heubert R, Billat V. Ventilatory thresholds assessment from heart rate variability during an incremental exhaustive running test. Int J Sports Med 2007; 28: 287-294
    Article in Thieme ConnectPubMedGoogle Scholar
  • 12 Daskalov I, Christov I. Improvement of resolution in measurement of electrocardiogram RR intervals by interpolation. Med Eng Phys 1997; 19: 375-379
    CrossrefPubMedGoogle Scholar
  • 13 Eckhardt M, Fasel I, Movellan J. Towards practical facial feature detection. Int J Pattern Recogn 2009; 23: 379-400
    CrossrefPubMedGoogle Scholar
  • 14 Gamelin FX, Berthoin S, Bosquet L. Validity of the polar S810 heart rate monitor to measure R-R intervals at rest. Med Sci Sport Exerc 2006; 38: 887-893
    CrossrefPubMedGoogle Scholar
  • 15 Gil E, Orini M, Bailon R, Vergara JM, Mainardi L, Laguna P. Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions. Physiol Meas 2010; 31: 1271-1290
    CrossrefPubMedGoogle Scholar
  • 16 Greve M, Kviesis-Kipge E, Rubenis O, Rubins U, Mecnika V, Grabovskis A, Marcinkevics Z. Comparison of pulse rate variability derived from digital photoplethysmography over the temporal artery with the heart rate variability derived from a Polar heart rate monitor. Proceedings of the 7th ESGCO 2012;
    PubMedGoogle Scholar
  • 17 Harris DJ, Atkinson G. Ethical standards in sports and exercise science research: 2014 update. Int J Sports Med 2013; 34: 1025-1028
    Article in Thieme ConnectPubMedGoogle Scholar
  • 18 Karavirta L, Tulppo MP, Laaksonen DE, Nyman K, Laukkanen RT, Kinnunen H, Hakkinen A, Hakkinen K. Heart rate dynamics after combined endurance and strength training in older men. Med Sci Sports Exerc 2009; 41: 1436-1443
    CrossrefPubMedGoogle Scholar
  • 19 Kiviniemi A, Hautala A, Kinnunen H, Tulppo M. Endurance training guided individually by daily heart rate variability measurements. Eur J Appl Physiol 101: 743-751
    PubMedGoogle Scholar
  • 20 Lin L. A concordance correlation coefficient to evaluate reproducibility. Biometrics 1989; 45: 255-268
    CrossrefPubMedGoogle Scholar
  • 21 Nunan D, Donovan G, Jakovljevic DG, Hodges LD, Sandercock GR, Brodie DA. 2009; Validity and reliability of short-term heart- rate variability from the Polar S810. Med Sci Sport Exerc 2009; 41: 243-250
    PubMedGoogle Scholar
  • 22 Parrado E, García MA, Ramos J, Cervantes JC, Rodas G, Capdevila L. Comparison of Omega Wave System and Polar S810i to detect RR intervals at rest. Int J Sports Med 2010; 31: 336-341
    Google Scholar
  • 23 Poh MZ, McDuff DJ, Picard RW. Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE Transact Biomed Engin 2011; 58: 7-11
    PubMedGoogle Scholar
  • 24 Poh MZ, McDuff DJ, Picard RW. Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Optics Express 2010; 18: 10762-10774
    CrossrefPubMedGoogle Scholar
  • 25 Posada-Quintero HF, Delisle-Rodríguez D, Cuadra-Sanz MB, Fernandez RR. Evaluation of pulse rate variability obtained by the pulse onsets of the photoplethysmographic signal. Physiol Meas 2013; 34: 179-187
    CrossrefPubMedGoogle Scholar
  • 26 Quintana DS, Healthers JAJ, Kemp AH. On the validity of using the Polar RS800 heart rate monitor for heart rate variability research. Eur J Appl Physiol 2012; 112: 4179-4180
    CrossrefPubMedGoogle Scholar
  • 27 Schäfer A, Vagedes J. How accurate is pulse rate variability as an estimate of heart rate variability? A review on studies comparing photoplethysmographic technology with an electrocardiogram. Int J Cardiol 2013; 166: 15-29
    CrossrefPubMedGoogle Scholar
  • 28 Selvaraj N, Jaryal A, Santhosh J, Deepal KK, Anand S. Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography. J Med Eng Technol 2008; 32: 478-484
    PubMedGoogle Scholar
  • 29 Sun Y, Hu S, Azorin-Peris V, Kalawsky R, Stephen G. Noncontact imaging photoplethysmography to effectively access pulse rate variability. J Biomed Opt 2013; 18: 061205
    PubMedGoogle Scholar
  • 30 Sun Y, Papin C, Azorin-Peris V, Kalawsky R, Greenwald S, Hu SJ. Use of ambient light in remote photoplethysmographic systems: comparison between a high-performance camera and a low-cost webcam. J Biomed Opt 2012; 17: 037005
    CrossrefPubMedGoogle Scholar
  • 31 Task Force of the European Society of cardiology and The North American Society of Pacing and Electrophysiology . Heart Rate Variability. Standards of measurement, physiological interpretation, and clinical use. Eur Heart J 1996; 17: 354-381
    CrossrefPubMedGoogle Scholar
  • 32 Verkruysse W, Svaasand LO, Nelson JS. Remote plethysmographic imaging using ambient light. Optics Express 2008; 16: 1-12
    CrossrefPubMedGoogle Scholar
  • 33 Wallen MB, Hasson D, Theorell T, Canlon B, Osika W. Possibilities and limitations of the polar RS800 in measuring heart rate variability at rest. Eur J Appl Physiol 2012; 112: 1153-1165
    CrossrefPubMedGoogle Scholar
  • 34 Weippert M, Kumar M, Kreuzfeld S, Arndt D, Rieger A, Stoll R. Comparison of three mobile devices for measuring R–R intervals and heart rate variability: Polar S810i, Suunto t6 and an ambulatory ECG system. Eur J Appl Physiol 2010; 109: 779-786
    CrossrefPubMedGoogle Scholar
  • 35 Wong JS, Lu WA, Wu KT, Liu M, Chen GY, Kuo CD. A comparative study of pulse rate variability and heart rate variability in healthy subjects. J Clin Monit Comput 2012; 26: 107-114
    CrossrefPubMedGoogle Scholar