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Association of Visual-Based Signals with Electroencephalography Patterns in Enhancing the Drowsiness Detection in Drivers with Obstructive Sleep Apnea

Minhas, Riaz ; Peker, Nur Yasin ; Hakkoz, Mustafa Abdullah ; Arbatli, Semih ; Celik, Yeliz ; Erdem, Cigdem Eroglu ; Semiz, Beren and Peker, Yuksel LU (2024) In Sensors 24(8).
Abstract

Individuals with obstructive sleep apnea (OSA) face increased accident risks due to excessive daytime sleepiness. PERCLOS, a recognized drowsiness detection method, encounters challenges from image quality, eyewear interference, and lighting variations, impacting its performance, and requiring validation through physiological signals. We propose visual-based scoring using adaptive thresholding for eye aspect ratio with OpenCV for face detection and Dlib for eye detection from video recordings. This technique identified 453 drowsiness (PERCLOS ≥ 0.3 || CLOSDUR ≥ 2 s) and 474 wakefulness episodes (PERCLOS < 0.3 and CLOSDUR < 2 s) among fifty OSA drivers in a 50 min driving simulation while wearing six-channel EEG electrodes.... (More)

Individuals with obstructive sleep apnea (OSA) face increased accident risks due to excessive daytime sleepiness. PERCLOS, a recognized drowsiness detection method, encounters challenges from image quality, eyewear interference, and lighting variations, impacting its performance, and requiring validation through physiological signals. We propose visual-based scoring using adaptive thresholding for eye aspect ratio with OpenCV for face detection and Dlib for eye detection from video recordings. This technique identified 453 drowsiness (PERCLOS ≥ 0.3 || CLOSDUR ≥ 2 s) and 474 wakefulness episodes (PERCLOS < 0.3 and CLOSDUR < 2 s) among fifty OSA drivers in a 50 min driving simulation while wearing six-channel EEG electrodes. Applying discrete wavelet transform, we derived ten EEG features, correlated them with visual-based episodes using various criteria, and assessed the sensitivity of brain regions and individual EEG channels. Among these features, theta–alpha-ratio exhibited robust mapping (94.7%) with visual-based scoring, followed by delta–alpha-ratio (87.2%) and delta–theta-ratio (86.7%). Frontal area (86.4%) and channel F4 (75.4%) aligned most episodes with theta–alpha-ratio, while frontal, and occipital regions, particularly channels F4 and O2, displayed superior alignment across multiple features. Adding frontal or occipital channels could correlate all episodes with EEG patterns, reducing hardware needs. Our work could potentially enhance real-time drowsiness detection reliability and assess fitness to drive in OSA drivers.

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author
; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
CLOSDUR, discrete wavelet transform, driving simulator, drowsiness, electroencephalography, image processing, obstructive sleep apnea, PERCLOS
in
Sensors
volume
24
issue
8
article number
2625
publisher
MDPI AG
external identifiers
  • pmid:38676243
  • scopus:85191371465
ISSN
1424-8220
DOI
10.3390/s24082625
language
English
LU publication?
yes
id
2d10ade7-a8ad-4362-b938-903c21aac16f
date added to LUP
2024-05-03 12:45:37
date last changed
2024-05-17 15:11:31
@article{2d10ade7-a8ad-4362-b938-903c21aac16f,
  abstract     = {{<p>Individuals with obstructive sleep apnea (OSA) face increased accident risks due to excessive daytime sleepiness. PERCLOS, a recognized drowsiness detection method, encounters challenges from image quality, eyewear interference, and lighting variations, impacting its performance, and requiring validation through physiological signals. We propose visual-based scoring using adaptive thresholding for eye aspect ratio with OpenCV for face detection and Dlib for eye detection from video recordings. This technique identified 453 drowsiness (PERCLOS ≥ 0.3 || CLOSDUR ≥ 2 s) and 474 wakefulness episodes (PERCLOS &lt; 0.3 and CLOSDUR &lt; 2 s) among fifty OSA drivers in a 50 min driving simulation while wearing six-channel EEG electrodes. Applying discrete wavelet transform, we derived ten EEG features, correlated them with visual-based episodes using various criteria, and assessed the sensitivity of brain regions and individual EEG channels. Among these features, theta–alpha-ratio exhibited robust mapping (94.7%) with visual-based scoring, followed by delta–alpha-ratio (87.2%) and delta–theta-ratio (86.7%). Frontal area (86.4%) and channel F4 (75.4%) aligned most episodes with theta–alpha-ratio, while frontal, and occipital regions, particularly channels F4 and O2, displayed superior alignment across multiple features. Adding frontal or occipital channels could correlate all episodes with EEG patterns, reducing hardware needs. Our work could potentially enhance real-time drowsiness detection reliability and assess fitness to drive in OSA drivers.</p>}},
  author       = {{Minhas, Riaz and Peker, Nur Yasin and Hakkoz, Mustafa Abdullah and Arbatli, Semih and Celik, Yeliz and Erdem, Cigdem Eroglu and Semiz, Beren and Peker, Yuksel}},
  issn         = {{1424-8220}},
  keywords     = {{CLOSDUR; discrete wavelet transform; driving simulator; drowsiness; electroencephalography; image processing; obstructive sleep apnea; PERCLOS}},
  language     = {{eng}},
  number       = {{8}},
  publisher    = {{MDPI AG}},
  series       = {{Sensors}},
  title        = {{Association of Visual-Based Signals with Electroencephalography Patterns in Enhancing the Drowsiness Detection in Drivers with Obstructive Sleep Apnea}},
  url          = {{http://dx.doi.org/10.3390/s24082625}},
  doi          = {{10.3390/s24082625}},
  volume       = {{24}},
  year         = {{2024}},
}