Comparison of outlier heartbeat identification and spectral transformation strategies for deriving heart rate variability indices for drivers at different stages of sleepiness

Forcolin, Fabio; Buendia, Ruben; Candefjord, Stefan; Karlsson, Johan; Sjöqvist, Bengt Arne; Anund, Anna

doi:10.1080/15389588.2017.1393073

Cited by 12 publications

(14 citation statements)

References 31 publications

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“…The inclusion criteria were healthy, experienced drivers between 25 and 65 years of age. A comprehensive description of the participants is provided in Forcolin et al (2018). All studies were ethically approved (EPN 142-07; EPN 142-07 T34-09).…”

Section: Experimental Designmentioning

confidence: 99%

“…The drivers were monitored by a 12-lead ECG commercial Holter system (Vitaport 2 and Vitaport 3, Temec, The Netherlands) during the complete driving session. A comprehensive explanation of the design and procedure of the 3 studies on which the current work is based can be found in Forcolin et al (2018).…”

Section: Experimental Designmentioning

confidence: 99%

“…The study was performed on the most commonly used HRV indices in the different information domains; these are summarized in Table 1 and further explained in "Heart Rate Variability" (1996). Details of the preprocessing methods are described in Forcolin et al (2018).…”

Section: Data Preparationmentioning

confidence: 99%

“…HRV is the physiological phenomenon of the beat-to-beat temporal variation of the heart. HRV indices are derived from the heart interbeat interval (IBI) signal (Forcolin et al 2018). Different approaches to measure driver sleepiness can be classified as physiological-, vehicle-, or behavioral-based measurements.…”

Section: Introductionmentioning

confidence: 99%

“…These 3 methods were considered in the present study. In Forcolin et al (2018), a more comprehensive background of the preprocessing methods considered here can be found. Forcolin and colleagues evaluated the level of agreement between these methods, using the same data as in the present study.…”

Section: Introductionmentioning

confidence: 99%

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

Buendia

Forcolin

Karlsson

et al. 2019

Traffic Injury Prevention

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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

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Section: Experimental Designmentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

Section: Data Preparationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Buendia

Forcolin

Karlsson

et al. 2019

Traffic Injury Prevention

Self Cite

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Detection of mental fatigue state using heart rate variability and eye metrics during simulated flight

Qin

Liu

et al. 2021

Hum Ftrs & Erg Mfg Svc

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Pilot mental fatigue is a growing concern in the aviation field due to its significant contributions to human errors and aviation accidents. Long work hours, sleep loss, circadian rhythm disruption, and workload are well‐known reasons, but there is a need to accurately detect pilot mental fatigue to improve aviation safety. However, due to the highly restricted cockpit environment and the complex nature of mental fatigue, feasible in‐flight detection remains under‐investigated. The objective of this study is to define a promising approach for mental fatigue detection based on psychophysiological measurements in flying‐relevant environments. Eleven participants engaged in a simulated flight experiment, where several conventional heart rate variability (HRV) and ocular indices were examined to study their relevance to mental fatigue. Additionally, a Toeplitz Inverse Covariance‐Based Clustering (TICC) method was performed to determine the ground truth, after which supervised machine learning was adopted to enable automated mental fatigue detection using HRV and eye metrics. Results showed that HRV and eye metrics were sensitive to the mental fatigue induced by prolonged flight‐relevant tasks. The TICC method helped determine the ground truth for mental fatigue and identify its three distinct levels. Furthermore, a supervised learning‐based detection of mental fatigue was achieved using a support vector machine with the greatest detection accuracy of 91.8%. The findings and methodology of this study provide new insights into the fatigue countermeasures in restricted cockpit environment and lay the groundwork for further explorations into the mental fatigue induced by prolonged flight missions.

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Detecting sleep in drivers during highly automated driving: the potential of physiological parameters

Wörle

Metz

Thiele

et al. 2019

IET Intelligent Transport Systems

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Comparison of outlier heartbeat identification and spectral transformation strategies for deriving heart rate variability indices for drivers at different stages of sleepiness

Cited by 12 publications

References 31 publications

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

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

Detection of mental fatigue state using heart rate variability and eye metrics during simulated flight

Detecting sleep in drivers during highly automated driving: the potential of physiological parameters

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