Drowsiness detection using heart rate variability

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


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.


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



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

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Supplementary material 1 (pdf 2528 KB)


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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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