Heart Rate Variability Can Be Used to Estimate Sleepiness-related Decrements in Psychomotor Vigilance during Total Sleep Deprivation

Chua, Eric Chern‐Pin; Tan, Wen-Qi; Yeo, Sing Chen; Lau, Pauline; Lee, Ivan; Mien, Ivan Ho; Puvanendran, K; Gooley, Joshua J.

doi:10.5665/sleep.1688

Cited by 83 publications

(140 citation statements)

References 38 publications

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“…Chua et al showed that RRI power density in the 0.02-0.08 Hz range correlated with psychomotor vigilance task (PVT) experiments with 40-hour profile [8]. PVT was originally developed as a measurement of sustained attention.…”

Section: Heart Rate Variability and Drowsinessmentioning

confidence: 99%

Development of Drowsiness Detection Method by Integrating Heart Rate Variability Analysis and Multivariate Statistical Process Control

Abe

Fujiwara

Hiraoka

et al. 2016

SICE Journal of Control, Measurement, and System Integration

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: Drowsy driving accidents can be prevented if predicted in advance. The present work aims to develop a new method for detecting driver drowsiness based on the fact that the autonomic nervous function affects heart rate variability (HRV), which is a fluctuation of the RR interval (RRI) obtained from an electrocardiogram (ECG). The proposed method uses eight HRV features derived through HRV analysis as input variables of multivariate statistical process control (MSPC), which is a well-known anomaly detection method in the field of process control. In the proposed method, only one principal component was adopted in MSPC and driver drowsiness was detected through monitoring the T 2 statistic. Driving simulator experiments demonstrated that driver drowsiness was successfully detected in seven out of eight cases before accidents occurred. In addition, the proposed method was implemented in a smartphone app for on-vehicle use.

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Section: Heart Rate Variability and Drowsinessmentioning

confidence: 99%

Development of Drowsiness Detection Method by Integrating Heart Rate Variability Analysis and Multivariate Statistical Process Control

Abe

Fujiwara

Hiraoka

et al. 2016

SICE Journal of Control, Measurement, and System Integration

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“…However, the correlation between HR and KSS is stronger than the correlation between HR and VLD. It is well known from previous work that HR in beats per minute is decreasing with higher sleepiness (Chua et al 2012) and increasing with increasing mental workload (Mehler et al 2011). …”

Section: Resultsmentioning

confidence: 97%

“…All three frequency bands were normalized by the total power. In addition, a more detailed frequency analysis based on 0.02-Hz bins from 0.0 to 0.40 Hz was conducted, similar to the approach proposed by Chua et al (2012). The 0.02-Hz frequency bands were normalized by total power as well.…”

Section: Heart Rate Measuresmentioning

confidence: 99%

“…The 0.02-Hz frequency bands were normalized by total power as well. The usage of relative or normalized PSD for the frequency bands leads to different results than those obtained by absolute PSD (Chua et al 2012). …”

Section: Heart Rate Measuresmentioning

confidence: 99%

“…This is essential because individuals differ in their ability to perform under different driving conditions. The idea of smaller frequency bands were introduced by Chua et al (2012) with interesting results. From the correlation differences between relative power spectral density (PSD) of the fine-grained frequency bands on one hand and the Karolinska sleepiness scale (KSS) and variation of lane deviation (VLD) data on the other, important conclusions about the separation of driving performance and driver sleepiness state should be possible.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Can Frequency Domain Heart Rate Measures Detect Impaired Driver Performance?

Heinze

Schirmer²,

Trutschel³

et al. 2013

Proceedings of the 7th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design : Dr

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Summary: An overnight driving simulation scenario with partial sleep deprivation was utilized to induce driver performance impairment. Heart rate (HR) was recorded over the entire experiment; frequency domain HR measures were derived and correlated to variation of lane deviation (VLD), a driving performance measure, and to the driver's state, which was estimated by the Karolinska Sleepiness Scale (KSS). The aim of this study is to evaluate whether frequency domain heart rate measures can be used to detect impaired driver performance as well as reduced driver state. We generalize the concept of the conventional frequency domain HR measures -namely the very-low frequency (VLF), low frequency (LF) band and high frequency (HF) band -into finer-grained frequency bands of 0.02 Hz width. These newly defined frequency bands show a more detailed correlation to driving performance and to driver sleepiness state, taking subjectspecific differences into account.

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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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Heart Rate Variability Can Be Used to Estimate Sleepiness-related Decrements in Psychomotor Vigilance during Total Sleep Deprivation

Cited by 83 publications

References 38 publications

Development of Drowsiness Detection Method by Integrating Heart Rate Variability Analysis and Multivariate Statistical Process Control

Development of Drowsiness Detection Method by Integrating Heart Rate Variability Analysis and Multivariate Statistical Process Control

Can Frequency Domain Heart Rate Measures Detect Impaired Driver Performance?

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

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