Drowsiness detection using heart rate variability.
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...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 54; no. 6; pp. 927 - 938 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=115398199&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115398199 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jun2016 vid: 54 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115398199 115398199 NLM26780463 10.1007/s11517-015-1448-7 NLM26780463 115398199 ppf: 927 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Drowsiness detection using heart rate variability. aug: au: Vicente, José Laguna, Pablo Bartra, Ariadna Bailón, Raquel Vicente, José Bailón, Raquel affil: Ficomirrors, Ficosa International, Barcelona Spain sug: subj: Heart Rate Physiology Sleep Stages Physiology Male ROC Curve Female Sleep Deprivation Physiopathology Algorithms Adult Middle Age Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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