Association of visual-based signals with electroencephalography patterns in enhancing the drowsiness detection in drivers with obstructive sleep apnea

Name: Association of visual-based signals with electroencephalography patterns in enhancing the drowsiness detection in drivers with obstructive sleep apnea
Author: Peker, Y., Semiz, B., Erdem, Cigdem Eroglu, Celik, Y., Arbatli, S., Hakkoz, M. A., Peker, N. Y., Minhas, R.

İsim	Association of visual-based signals with electroencephalography patterns in enhancing the drowsiness detection in drivers with obstructive sleep apnea
Yazar	Peker, Y., Semiz, B., Erdem, Cigdem Eroglu, Celik, Y., Arbatli, S., Hakkoz, M. A., Peker, N. Y., Minhas, R.
Basım Tarihi:	2024-03
Basım Yeri	- MDPI
Konu	Perclos, Obstructive sleep apnea, Image processing, Electroencephalography, Drowsiness, Driving simulator, Discrete wavelet transform, Closdur
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1424-8220
Kayıt Numarası	2495365e-bc37-4d31-83e6-f7b9df585a9a
Lokasyon	Electrical & Electronics Engineering
Tarih	2024-03
Notlar	TÜBİTAK
Örnek Metin	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.
DOI	10.3390/s24082625
Cilt	24

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Association of visual-based signals with electroencephalography patterns in enhancing the drowsiness detection in drivers with obstructive sleep apnea

Yazar Peker, Y., Semiz, B., Erdem, Cigdem Eroglu, Celik, Y., Arbatli, S., Hakkoz, M. A., Peker, N. Y., Minhas, R.

Basım Tarihi 2024-03

Basım Yeri - MDPI

Konu Perclos, Obstructive sleep apnea, Image processing, Electroencephalography, Drowsiness, Driving simulator, Discrete wavelet transform, Closdur

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1424-8220

Kayıt Numarası 2495365e-bc37-4d31-83e6-f7b9df585a9a

Lokasyon Electrical & Electronics Engineering

Tarih 2024-03

Notlar TÜBİTAK

Örnek Metin 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.

DOI 10.3390/s24082625

Cilt 24