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Drowsiness detection using heart rate variability

  • José VicenteEmail author
  • Pablo Laguna
  • Ariadna Bartra
  • Raquel Bailón
Original Article

Abstract

It is estimated that 10–30 % of road fatalities are related to drowsy driving. Driver’s drowsiness detection based on biological and vehicle signals is being studied in preventive car safety. Autonomous nervous system activity, which can be measured noninvasively from the heart rate variability (HRV) signal obtained from surface electrocardiogram, presents alterations during stress, extreme fatigue and drowsiness episodes. We hypothesized that these alterations manifest on HRV and thus could be used to detect driver’s drowsiness. We analyzed three driving databases in which drivers presented different sleep-deprivation levels, and in which each driving minute was annotated as drowsy or awake. We developed two different drowsiness detectors based on HRV. While the drowsiness episodes detector assessed each minute of driving as “awake” or “drowsy” with seven HRV derived features (positive predictive value 0.96, sensitivity 0.59, specificity 0.98 on 3475 min of driving), the sleep-deprivation detector discerned if a driver was suitable for driving or not, at driving onset, as function of his sleep-deprivation state. Sleep-deprivation state was estimated from the first three minutes of driving using only one HRV feature (positive predictive value 0.80, sensitivity 0.62, specificity 0.88 on 30 drivers). Incorporating drowsiness assessment based on HRV signal may add significant improvements to existing car safety systems.

Keywords

Sleep debt Impaired driving Heart rate variability Autonomic nervous system Linear discriminant analysis Classification Smoothed pseudo Wigner–Ville distribution 

Notes

Acknowledgments

This work was supported in part by the Ministerio de Ciencia e Innovación, Spain, under Projects TIN2014-53567-R; TEC2013-42140-R, TRA2009-0127 and UZ2014-TEC-01, in part by Grupo Consolidado BSICoS from DGA (Aragón), European Social Fund (EU) and CIBER de Bioingeniería, Biomateriales y Nanomedicina, in part by an appointment to the Research Participation Program at the Center for Devices and Radiological Health and the Center for Drug Evaluation and Research administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and the U.S. Food and Drug Administration. The computation was performed by the ICTS 0707NANBIOSIS, by the High Performance Computing Unit of the CIBER in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN) at the University of Zaragoza.

Supplementary material

11517_2015_1448_MOESM1_ESM.pdf (2.5 mb)
Supplementary material 1 (pdf 2528 KB)

References

  1. 1.
    Acharya UR, Joseph KP, Kannathal N, Lim CM, Suri JS (2006) Heart rate variability: a review. Med Biol Eng Comput 44(12):1031–1051CrossRefGoogle Scholar
  2. 2.
    Baharav A, Kotagal S, Gibbons V, Rubin BK, Pratt G, Karin J, Akselrod S (1995) Fluctuations in autonomic nervous activity during sleep displayed by power spectrum analysis of heart rate variability. Neurology 45(6):1183–1187CrossRefPubMedGoogle Scholar
  3. 3.
    Bailón R, Laouini G, Grao C, Orini M, Laguna P, Meste O (2011) The integral pulse frequency modulation model with time-varying threshold: Application to heart rate variability analysis during exercise stress testing. IEEE Trans Biomed Eng 58(3):642–652CrossRefPubMedGoogle Scholar
  4. 4.
    Bailón R, Mainardi L, Orini M, Sörnmo L, Laguna P (2010) Analysis of heart rate variability during exercise stress testing using respiratory information. Biomed Signal Process Control 5(4):299–310CrossRefGoogle Scholar
  5. 5.
    Bailón R, Sörnmo L, Laguna P (2006) ECG-derived respiratory frequency estimation, pp 215–244. Artech House IncGoogle Scholar
  6. 6.
    Catarino R, Spratley J, Catarino I, Lunet N, Pais-Clemente M (2013) Sleepiness and sleep-disordered breathing in truck drivers. Sleep Breath pp 1–10Google Scholar
  7. 7.
    Dirección General de Tráfico: La fatiga causa el 30% de los accidentes de tráfico en españa (2008)Google Scholar
  8. 8.
    Drake C, Roehrs T, Breslau N, Johnson E, Jefferson C, Scofield H, Roth T (2010) The 10-year risk of verified motor vehicle crashes in relation to physiologic sleepiness. Sleep 33(6):745PubMedPubMedCentralGoogle Scholar
  9. 9.
    Furman G, Baharav A, Cahan C, Akselrod S (2008) Early detection of falling asleep at the wheel: a heart rate variability approach. Comput Cardiol, pp 1109–112Google Scholar
  10. 10.
    Gunzelmann G, Gross JB, Gluck KA, Dinges DF (2009) Sleep deprivation and sustained attention performance: integrating mathematical and cognitive modeling. Cogn Sci 33(5):880–910CrossRefPubMedGoogle Scholar
  11. 11.
    Hodes and Associates (1972) The stanford sleepiness scale. In: Eleventh annual meeting of the association for the psychophysiological study of sleep, 1972Google Scholar
  12. 12.
    Johns M (1991) A new method for measuring daytime sleepiness: the epworth sleepiness scale. Sleep 6:540–545Google Scholar
  13. 13.
    Lachenbruch PA, Goldstein M (1979) Discriminant analysis. Biometrics 35(1): 69–85. http://www.jstor.org/stable/2529937
  14. 14.
    Li G, Chung WY (2013) Detection of driver drowsiness using wavelet analysis of heart rate variability and a support vector machine classifier. Sensors 13(12):16494–16511CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Martínez JP, Almeida R, Olmos S, Rocha AP, Laguna P (2004) A wavelet-based ecg delineator: evaluation on standard databases. IEEE Trans Biomed Eng 51(4):570–581CrossRefPubMedGoogle Scholar
  16. 16.
    Mateo J, Laguna P (2003) Analysis of heart rate variability in the presence of ectopic beats using the heart timing signal. IEEE Trans Biomed Eng 50(3):334–343CrossRefPubMedGoogle Scholar
  17. 17.
    Michail E, Kokonozi A, Chouvarda I, Maglaveras N (2008) Eeg and hrv markers of sleepiness and loss of control during car driving. In: Conf Proc IEEE Eng Med Biol Soc, pp 2566 –2569Google Scholar
  18. 18.
    Paik H (2000) Comments on neural networks. Sociol Methods Res 28(4):425–453. doi: 10.1177/0049124100028004002. http://smr.sagepub.com/content/28/4/425.abstract
  19. 19.
    Patel M, Lal S, Kavanagh D, Rossiter P (2011) Applying neural network analysis on heart rate variability data to assess driver fatigue. Expert Syst Appl 38(6):7235–7242CrossRefGoogle Scholar
  20. 20.
    Philip P, Akerstedt T (2006) Transport and industrial safety, how are they affected by sleepiness and sleep restriction? Sleep Med Rev 10(5):347–356CrossRefPubMedGoogle Scholar
  21. 21.
    Ripley BD (2007) Linear discriminant analysis. Pattern recognition and neural networks. Cambridge University Press, Cambridge, pp 91–120Google Scholar
  22. 22.
    Rodríguez-Ibáñez N, García-González M, Fernández-Chimeno M, Ramos-Castro J (2011) Drowsiness detection by thoracic effort signal analysis in real driving environments. In: Conf Proc IEEE Eng Med Biol SocGoogle Scholar
  23. 23.
    Shinar Z, Akselrod S, Dagan Y, Baharav A (2006) Autonomic changes during wake-sleep transition: a heart rate variability based approach. Auton Neurosci 130:17–27CrossRefPubMedGoogle Scholar
  24. 24.
    Strohl K (2012) Assessments of driving risk in sleep apnea., Respiratory medicineHumana Press, USACrossRefGoogle Scholar
  25. 25.
    Task Force of ESC and NASPE (1996) Heart rate variability : standards of measurement, physiological interpretation, and clinical use. Circulation 93(5):1043–1065Google Scholar
  26. 26.
    R Development Core Team (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  27. 27.
    Valenza G, Citi L, Barbieri R (2014) Estimation of instantaneous complex dynamics through lyapunov exponents: A study on heartbeat dynamics. PLoS One 9(8):e105,622. doi: 10.1371/journal.pone.0105622 CrossRefGoogle Scholar
  28. 28.
    Verster JC, Taillard J, Sagaspe P, Olivier B, Philip P (2011) Prolonged nocturnal driving can be as dangerous as severe alcohol-impaired driving. J Sleep Res 20(4):585–588CrossRefPubMedGoogle Scholar
  29. 29.
    Vicente J, Laguna P, Bartra A, Bailón R (2011) Detection of driver’s drowsiness by means of hrv analysis. Comput CardiolGoogle Scholar
  30. 30.
    Wegman F (2013) Road Safety Annual Report. Tech. rep, International Traffic Safety Data and Analysis GroupGoogle Scholar
  31. 31.
    Wessel N, Voss A, Malberg H, Ziehmann C, Voss HU, Schirdewan A, Meyerfeldt U, Kurths J (2000) Nonlinear analysis of complex phenomena in cardiological data. Herzschrittmachertherapie und Elektrophysiologie 11(3):159–173CrossRefGoogle Scholar
  32. 32.
    Willis D, Waller P, Stutts J, Roth T (1998) Drowsy driving and automobile crashes. Tech Rep DOT HS 808 707, National Highway Traffic Safety AdministrationGoogle Scholar
  33. 33.
    Yang G, Lin Y, Bhattacharya P (2010) A driver fatigue recognition model based on information fusion and dynamic bayesian network. Inf Sci (Ny) 180(10):1942–1954CrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2016

Authors and Affiliations

  1. 1.BSICoS Group, Aragon Institute of Engineering Research (I3A), IIS AragónUniversity of ZaragozaZaragozaSpain
  2. 2.Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)ZaragozaSpain
  3. 3.Ficomirrors, Ficosa InternationalBarcelonaSpain
  4. 4.Office of Science and Engineering Laboratories, Center for Devices and Radiological HealthUS Food and Drug AdministrationSilver SpringUSA

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