2019
DOI: 10.1080/15389588.2018.1548766
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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 s… Show more

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Cited by 40 publications
(30 citation statements)
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“…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.…”
Section: B Pre-processingmentioning
confidence: 99%
“…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.…”
Section: B Pre-processingmentioning
confidence: 99%
“…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.…”
Section: Resultsmentioning
confidence: 99%
“…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.…”
Section: Resultsmentioning
confidence: 99%
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“…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.…”
Section: Literature Reviewmentioning
confidence: 99%