Deriving heart rate variability indices from cardiac monitoring—An indicator of driver sleepiness

Abstract

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. ARTICLE HISTORY

Heart Rate Variability for Classification of Alert Versus Sleep Deprived Drivers in Real Road Driving Conditions

“…R-peaks were extracted from the ECG using the Pan Tompkins algorithm [32] and the RR time series were extracted as the time difference between heart beats. The corresponding normal to normal (NN) time series were obtained by removing outliers using the standard deviation method [33], [34]. Here the threshold was set to 4 standard deviations.…”

Accurate and early detection of sleepiness, fatigue and stress levels in drivers through Heart Rate Variability parameters: a systematic review

“…All included studies had an observational, nonrandomized design [15,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40]. Data regarding participants enrolled in each study, investigated HRV parameters, clinical setting, and major findings were reported in Table 1 (Ref.…”

“…Data regarding participants enrolled in each study, investigated HRV parameters, clinical setting, and major findings were reported in Table 1 (Ref. [15,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40]). The most of studies investigated the value of HRV measurements for sleepiness or drowsiness detection in drivers [15, 23-27, 29-31, 33, 34, 37, 39, 40], followed by stress [28,35,38] and fatigue [32,36] detection.…”

“…The most of studies investigated the value of HRV measurements for sleepiness or drowsiness detection in drivers [15, 23-27, 29-31, 33, 34, 37, 39, 40], followed by stress [28,35,38] and fatigue [32,36] detection. In addition, seven studies included drivers on real-roads [23,24,28,[33][34][35]40], while the rest performed experiments on driving simulators. The quality of included studies in the present systematic review was low, given that all studies were observational and non-randomized, with a small number of participants included (Supplementary Table 2).…”

Improvement of Road Safety through Appropriate Cargo Securing Using Outliers

“…The subject of several professional publications is the identification of outliers and their evaluation, specifically their imaging using boxplots [30][31][32]. These methods are applied to data from natural, technical, as well as social sciences [33][34][35], including transportation, where subjects such as urban traffic [36,37] and the evaluation of psychological and physical parameters of drivers [38,39] are discussed. Professional publications do not cover the use of these methods for the identification of outliers and their statistical evaluation in terms of shocks in road cargo transport.…”