Riaz Minhas

Multichannel electroencephalography (EEG)-based drowsiness detection (DD) offers higher coverage but comes with increased computational demands, hardware requirements, and user discomfort, whereas single-channel devices are cost-effective and user-friendly but provide lower coverage. We hypothesized that an optimal channel with optimum paired EEG features could achieve coverage comparable to a multichannel system. Subject-specific, EEG-feature-specific thresholding techniques were introduced to classify 927 EEG epochs, derived from visual-based scoring through image processing of fifty drivers’ facial expressions during a 50-min driving simulation, using six individual EEG channels with paired features. Ten normalized EEG features were extracted per epoch using discrete wavelet transform (DWT), and seven thresholding techniques were applied to identify the most consistent method across subjects. Epochs were classified as drowsy or wakeful based on whether their normalized values exceeded or fell below a specific threshold. We then assessed the coverage of each channel by comparing EEG patterns with visual-based scoring. To determine the optimal feature pair for classifying each epoch in alignment with visual-based scoring, 45 feature combinations were evaluated. The pairing of power spectral density (PSD) alpha and PSD theta in channels Frontal4 (F4) and Occipital2 (O2) yielded the highest coverage, achieving 96.1% and 95% with corresponding accuracies of 95.4% and 94.7%, respectively. These results slightly surpassed the coverage achieved using six channels with a single feature, with increases of 1.47% for F4 and 0.32% for O2. Our study demonstrates that an optimal EEG channel with optimum paired EEG features can reduce channels from six to one, lowering computational demands for wearable DD devices. Graphical 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. 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.

Accurate quantification of microsleep (MS) in drivers is crucial for preventing real-time accidents. We propose one-to-one correlation between events of high-fidelity driving simulator (DS) and corresponding brain patterns, unlike previous studies focusing general impact of MS on driving performance. Fifty professional drivers with obstructive sleep apnea (OSA) participated in a 50-minute driving simulation, wearing six-channel Electroencephalography (EEG) electrodes. 970 out-of-road OOR (microsleep) events (wheel and boundary contact ≥ 1s), and 1020 on-road OR (wakefulness) events (wheel and boundary disconnection ≥ 1s), were recorded. Power spectrum density, computed using discrete wavelet transform, analyzed power in different frequency bands and theta/alpha ratios were calculated for each event. We classified OOR (microsleep) events with higher theta/alpha ratio compared to neighboring OR (wakefulness) episodes as true MS and those with lower ratio as false MS. Comparative analysis, focusing on frontal brain, matched 791 of 970 OOR (microsleep) events with true MS episodes, outperforming other brain regions, and suggested that some unmatched instances were due to driving performance, not sleepiness. Combining frontal channels F3 and F4 yielded increased sensitivity in detecting MS, achieving 83.7% combined mean identification rate (CMIR), surpassing individual channel's MIR, highlighting potential for further improvement with additional frontal channels. We quantified MS duration, with 95% of total episodes lasting between 1 to 15 seconds, and pioneered a robust correlation (r = 0.8913, p<0.001) between maximum drowsiness level and MS density. Validating simulator's signals with EEG patterns by establishing a direct correlation improves reliability of MS identification for assessing fitness-to-drive of OSA-afflicted adults.

Background: Adenoidectomy is the surgical procedure to remove the adenoids. It is conventionally performed using the curettage method. The aim is to compare between adenoid curette adenoidectomy and microdebrider assisted adenoidectomy. Methods: From April 2016 to March 2017, 50 patients (34 males and 16 females), requiring adenoidectomy were randomized into two groups each of twenty five. Group A underwent microdebrider assisted adenoidectomy. Group B underwent conventional adenoidectomy using the curettage method. The parameters studied were intra-operative time, blood loss, residual tissue, associated trauma, and post-operative symptomatic relief and complications. Results: Microdebrider assisted adenoidectomy was significantly better in terms of residual tissue left behind as compared to adenoid curette adenoidectomy (pConclusions: Microdebrider-assisted adenoidectomy is a safe and effective alternative to curettage method as it allows complete removal of adenoid tissue under direct vision.