Literature DB >> 19163227

EEG and HRV markers of sleepiness and loss of control during car driving.

Emmanouil Michail1, Athina Kokonozi, Ioanna Chouvarda, Nicos Maglaveras.   

Abstract

Heart Rate Variability (HRV) reflects the balance between sympathetic and parasympathetic activity. Slower HRV rhythms (LF) indicate increased sympathetic and/or lower vagal activity, wakefulness characteristics, while faster HRV rhythms (HF) indicate lower sympathetic and/or increased parasympathetic and vagal activity, sleepy characteristics. In this work we demonstrate that power spectral analysis of drivers' heart rate can report driving errors caused by sleepiness. Furthermore, variation of Fractal Dimension (FD) can aid significant information for the assessment of the driving situation. ECG and EEG data were collected from sleep-deprived subjects exposed to real field driving conditions. A lower ratio of low frequency to high frequency components (LF/HF), and lower LF values were reported on the occurrence of driving errors.

Entities:  

Mesh:

Year:  2008        PMID: 19163227     DOI: 10.1109/IEMBS.2008.4649724

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  14 in total

1.  Drowsiness detection using heart rate variability.

Authors:  José Vicente; Pablo Laguna; Ariadna Bartra; Raquel Bailón
Journal:  Med Biol Eng Comput       Date:  2016-01-16       Impact factor: 2.602

2.  Non-contact determination of parasympathetic activation induced by a full stomach using microwave radar.

Authors:  Shinji Gotoh; Satoshi Suzuki; Hayato Imuta; Masayuki Kagawa; Zorig Badarch; Takemi Matsui
Journal:  Med Biol Eng Comput       Date:  2009-07-05       Impact factor: 2.602

3.  Heart rate variability for evaluating vigilant attention in partial chronic sleep restriction.

Authors:  Andreas Henelius; Mikael Sallinen; Minna Huotilainen; Kiti Müller; Jussi Virkkala; Kai Puolamäki
Journal:  Sleep       Date:  2014-07-01       Impact factor: 5.849

4.  EEG-based Drowsiness Detection for Safe Driving Using Chaotic Features and Statistical Tests.

Authors:  Zahra Mardi; Seyedeh Naghmeh Miri Ashtiani; Mohammad Mikaili
Journal:  J Med Signals Sens       Date:  2011-05

5.  EEG-based analysis of human driving performance in turning left and right using Hopfield neural network.

Authors:  Mitra Taghizadeh-Sarabi; Kavous Salehzadeh Niksirat; Sohrab Khanmohammadi; Mohammadali Nazari
Journal:  Springerplus       Date:  2013-12-10

6.  Sound Effects on Physiological State and Behavior of Drivers in a Highway Tunnel.

Authors:  Yanqun Yang; Yang Feng; Said M Easa; Xiujing Yang; Jiang Liu; Wei Lin
Journal:  Front Psychol       Date:  2021-06-23

Review 7.  Detecting driver drowsiness based on sensors: a review.

Authors:  Arun Sahayadhas; Kenneth Sundaraj; Murugappan Murugappan
Journal:  Sensors (Basel)       Date:  2012-12-07       Impact factor: 3.576

8.  Behavioral and Neurophysiological Signatures of Benzodiazepine-Related Driving Impairments.

Authors:  Bradly T Stone; Kelly A Correa; Timothy L Brown; Andrew L Spurgin; Maja Stikic; Robin R Johnson; Chris Berka
Journal:  Front Psychol       Date:  2015-11-26

9.  Sleep Deprivation in Young and Healthy Subjects Is More Sensitively Identified by Higher Frequencies of Electrodermal Activity than by Skin Conductance Level Evaluated in the Time Domain.

Authors:  Hugo F Posada-Quintero; Jeffrey B Bolkhovsky; Natasa Reljin; Ki H Chon
Journal:  Front Physiol       Date:  2017-06-20       Impact factor: 4.566

10.  A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability.

Authors:  Muhammad Awais; Nasreen Badruddin; Micheal Drieberg
Journal:  Sensors (Basel)       Date:  2017-08-31       Impact factor: 3.576

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