Illuminating the Neural Landscape of Pilot Mental States: A Convolutional Neural Network Approach with Shapley Additive Explanations Interpretability.

Predicting pilots' mental states is a critical challenge in aviation safety and performance, with electroencephalogram data offering a promising avenue for detection. However, the interpretability of machine learning and deep learning models, which are often used for such tasks, remains a significant issue. This study aims to address these challenges by developing an interpretable model to detect four mental states-channelised attention, diverted attention, startle/surprise, and normal state-in pilots using EEG data. The methodology involves training a convolutional neural network on power spectral density features of EEG data from 17 pilots. The model's interpretability is enhanced via the use of SHapley Additive exPlanations values, which identify the top 10 most influential features for each mental state. The results demonstrate high performance in all metrics, with an average accuracy of 96%, a precision of 96%, a recall of 94%, and an F1 score of 95%. An examination of the effects of mental states on EEG frequency bands further elucidates the neural mechanisms underlying these states. The innovative nature of this study lies in its combination of high-performance model development, improved interpretability, and in-depth analysis of the neural correlates of mental states. This approach not only addresses the critical need for effective and interpretable mental state detection in aviation but also contributes to our understanding of the neural underpinnings of these states. This study thus represents a significant advancement in the field of EEG-based mental state detection.

Multimodal Approach for Pilot Mental State Detection Based on EEG.

The safety of flight operations depends on the cognitive abilities of pilots. In recent years, there has been growing concern about potential accidents caused by the declining mental states of pilots. We have developed a novel multimodal approach for mental state detection in pilots using electroencephalography (EEG) signals. Our approach includes an advanced automated preprocessing pipeline to remove artefacts from the EEG data, a feature extraction method based on Riemannian geometry analysis of the cleaned EEG data, and a hybrid ensemble learning technique that combines the results of several machine learning classifiers. The proposed approach provides improved accuracy compared to existing methods, achieving an accuracy of 86% when tested on cleaned EEG data. The EEG dataset was collected from 18 pilots who participated in flight experiments and publicly released at NASA's open portal. This study presents a reliable and efficient solution for detecting mental states in pilots and highlights the potential of EEG signals and ensemble learning algorithms in developing cognitive cockpit systems. The use of an automated preprocessing pipeline, feature extraction method based on Riemannian geometry analysis, and hybrid ensemble learning technique set this work apart from previous efforts in the field and demonstrates the innovative nature of the proposed approach.

Tinea Unguium and Tinea Pedis and Their Correlation With Diabetes Mellitus in the General Population in the Hail Region, Saudi Arabia: A Cross-Sectional Study.

Background Tinea pedis or foot ringworm is an infection of the feet affecting the soles, interdigital clefts of toes, and nails, with a dermatophyte fungus. It is also called athlete's foot. Onychomycosis of the nail is caused by dermatophytes called Tinea unguium. An abnormal nail not caused by a fungal infection is a type of dystrophic nail. Onychomycosis can infect both fingernails and toenails, but onychomycosis of the toenail is much more prevalent. Aim The study aimed to assess the knowledge, perception, and awareness among a sample from Ha'il City, Saudi Arabia, of the definitions, risk factors, symptoms, diagnosis, complications, and treatment of both Tinea pedis and Tinea unguium, along withtheir correlation with diabetic patients. Material A cross-sectional survey was distributed throughout Ha'il City. An online questionnaire was designed and distributed via various social media apps, which included questions concerning participants' sociodemographic information, alongside questions regarding the risk factors, signs, symptoms, complications, and management of both Tinea pedis and Tinea unguium. Methods SPSS for Windows v22.0 (IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp.) was used for statistical analysis. Results The overall awareness of the study's participants about Tinea Pedis and Tinea unguium infection was low (34.82%).

Childhood Atopic Dermatitis: A Nationwide Measurement of Quality of Life among Saudi Patients.

Background: Atopic dermatitis (AD) negatively affects the quality of life (QoL). However, few studies from Saudi Arabia have assessed the effect AD has on the QoL of pediatric patients. Objective: To determine the psychological impact of AD on pediatric Saudi patients using the Children's Dermatology Life Quality Index (CDLQI). Methods: This cross-sectional was conducted across five tertiary hospitals located across five cities of Saudi Arabia from December 2018 to December 2019. The study included all Saudi patients aged 5-16 years who were diagnosed with AD for at least 6 months prior to visiting the dermatology clinic of the included hospitals. The quality of life in children with AD was assessed using the Arabic version of the CDLQI. Results: A total of 476 patients were included, of which 67.4% were boys. AD had a very large and extremely large effect on the QoL in 17.4% and 11.3% of the patients, respectively; the QoL of only 5.7% of the patients was not impacted due to AD. The average CDLQI score was not significantly different between males and females (9.7 vs. 9.1, respectively; P = 0.4255). Domains related to symptoms and emotions were affected to a greater extent compared with the remaining domains, while the school domain was the least affected. The correlation between age and CDLQI (r = 0.04, P = 0.52) and between the duration of the disease and CDLQI (r = 0.062, P = 0.18) was not significant. Conclusions: This study found that AD affects the QoL of a significant proportion of the Saudi pediatric patients, thereby highlighting the need to consider QoL as a measure of treatment success.

Prevalence of Cigarette Smoking Usage among Adolescent Students in Northern Saudi Arabia.

BACKGROUND: Cigarette smoking is a major public health issue in the Kingdom of Saudi Arabia (KSA) in recent years, particularly among adolescents. Therefore, the aim of this study was to determine the prevalence of cigarette smoking usage among adolescent students in the north of the country. MATERIALS AND METHODS: This cross-sectional study investigated 305 adolescent students from the Northern KSA population, their ages ranging from 11 to 19 years old. RESULTS: Of the 287 respondents, 56/287(19.5%) were found to be current smokers. Of the 56 current smokers, 14/52 (27%), 29/52 (55.8%), and 9/52 (17.2%) smoked 1-3, 4-10 and 11+ cigarettes/day, respectively. For duration most had smoked for 26-36 months. CONCLUSIONS: The findings of the present study indicate that cigarette smoking use is still an important risk behavior among adolescent students. The findings of this study found a significant association of cigarette smoking usage and adolescents various believes and attitude for initiation of smoking and perception toward knowledge of other factors that contribute to the burden of tobacco use.