Deriving heart rate variability indices from cardiac monitoring—An indicator of driver sleepiness
Artikel i vetenskaplig tidskrift, 2019

Published with license by Taylor & Francis. Objective: Driver fatigue is considered to be a major contributor to road traffic crashes. Cardiac monitoring and heart rate variability (HRV) analysis is a candidate method for early and accurate detection of driver sleepiness. This study has 2 objectives: to evaluate the (1) suitability of different preprocessing strategies for detecting and removing outlier heartbeats and spectral transformation of HRV signals and their impact of driver sleepiness assessment and (2) relation between common HRV indices and subjective sleepiness reported by a large number of drivers in real driving situations, for the first time. Methods: The study analyzed >3,500 5-min driving epochs from 76 drivers on a public motorway in Sweden. The electrocardiograph (ECG) data were recorded in 3 studies designed to evaluate the physiological differences between awake and sleepy drivers. The drivers reported their perceived level of sleepiness according to the Karolinska Sleepiness Scale (KSS) every 5 min. Two standard methods were used for identifying outlier heartbeats: (1) percentage change (PC), where outliers were defined as interbeat intervals deviating >30% from the mean of the four previous intervals and (2) standard deviation (SD), where outliers were defined as interbeat interval deviating >4 SD from the mean interval duration in the current epoch. Three standard methods were used for spectral transformation, which is needed for deriving HRV indices in the frequency domain: (1) Fourier transform; (2) autoregressive model; and (3) Lomb-Scargle periodogram. Different preprocessing strategies were compared regarding their impact on derivation of common HRV indices and their relation to KSS data distribution, using box plots and statistical tests such as analysis of variance (ANOVA) and Student’s t test. Results: The ability of HRV indices to discriminate between alert and sleepy drivers does not differ significantly depending on which outlier detection and spectral transformation methods are used. As expected, with increasing sleepiness, the heart rate decreased, whereas heart rate variability overall increased. Furthermore, HRV parameters representing the parasympathetic branch of the autonomous nervous system increased. An unexpected finding was that parameters representing the sympathetic branch of the autonomous nervous system also increased with increasing KSS level. We hypothesize that this increment was due to stress induced by trying to avoid an incident, because the drivers were in real driving situations. Conclusions: The association of HRV indices to KSS did not depend on the preprocessing strategy. No preprocessing method showed superiority for HRV association to driver sleepiness. This was also true for combinations of methods for frequency domain HRV indices. The results prove clear relationships between HRV indices and perceived sleepiness. Thus, HRV analysis shows promise for driver sleepiness detection.

outlier detection

Heart rate variability

spectral transformation

driver sleepiness

Författare

Ruben Buendia

Högskolan i Borås

MedTech West

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Medicinska signaler och system

Fabio Forcolin

Student vid Chalmers

J. G. Karlsson

Autoliv AB

Bengt-Arne Sjöqvist

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Medicinska signaler och system

MedTech West

Anna Anund

Statens Väg- och Transportforskningsinstitut (VTI)

Stefan Candefjord

MedTech West

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Biomedicinsk elektromagnetik

Traffic Injury Prevention

1538-9588 (ISSN) 1538-957X (eISSN)

Vol. 20 3 249-254

Ämneskategorier

Infrastrukturteknik

Tillämpad psykologi

Farkostteknik

DOI

10.1080/15389588.2018.1548766

Mer information

Senast uppdaterat

2019-11-19