Advancing aviation safety through machine learning and psychophysiological data: a systematic review

In the aviation industry, safety remains vital, often compromised by pilot errors attributed to factors such as workload, fatigue, stress, and emotional disturbances. To address these challenges, recent research has increasingly leveraged psychophysiological data and machine learning techniques, offering the potential to enhance safety by understanding pilot behavior. This systematic literature review rigorously follows a widely accepted methodology, scrutinizing 80 peer-reviewed studies out of 3352 studies from five key electronic databases. The paper focuses on behavioral aspects, data types, preprocessing techniques, machine learning models, and performance metrics used in existing studies. It reveals that the majority of research disproportionately concentrates on workload and fatigue, leaving behavioral aspects like emotional responses and attention dynamics less explored. Machine learning models such as tree-based and support vector machines are most commonly employed, but the utilization of advanced techniques like deep learning remains limited. Traditional preprocessing techniques dominate the landscape, urging the need for advanced methods. Data imbalance and its impact on model performance is identified as a critical, under-researched area. The review uncovers significant methodological gaps, including the unexplored influence of preprocessing on model efficacy, lack of diversification in data collection environments, and limited focus on model explainability. The paper concludes by advocating for targeted future research to address these gaps, thereby promoting both methodological innovation and a more comprehensive understanding of pilot behavior

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

Miscellaneous EEG preprocessing and machine learning for pilots' mental states classification: implications

Higher cognitive process efforts may result in mental exhaustion, poor performance, and long-term health issues. An EEG-based methods for detecting a pilot's mental state have recently been created utilizing machine learning algorithms. EEG signals include a significant noise component, and these approaches either ignore this or use a random mix of preprocessing techniques to reduce noise. In the absence of uniform preprocessing procedures for cleaning, it would be impossible to compare the efficacy of machine learning models across research, even if they employ data obtained from the same experiment. In this study, we intend to evaluate how preprocessing approaches affect the performance of machine learning models. To do this, we concentrated on fundamental preprocessing techniques, such as a band-pass filter and independent component analysis. Using a publicly accessible actual physiological dataset gathered from a pilot who was exposed to a variety of mental events, we explore the influence of these preprocessing strategies on two machine learning models, SVMs and ANNs. Our findings indicate that the performance of the models is unaffected by preprocessing techniques. Moreover, our findings indicate that the models were able to anticipate the mental states from merged data collected in two environments. These findings demonstrate the necessity for a standardized methodological framework for the application of machine learning models to EEG inputs

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

A comprehensive analysis of machine learning and deep learning models for identifying pilots’ mental states from imbalanced physiological data

This study focuses on identifying pilots' mental states linked to attention-related human performance-limiting states (AHPLS) using a publicly released, imbalanced physiological dataset. The research integrates electroencephalography (EEG) with non-brain signals, such as electrocardiogram (ECG), galvanic skin response (GSR), and respiration, to create a deep learning architecture that combines one-dimensional Convolutional Neural Network (1D-CNN) and Long Short-Term Memory (LSTM) models. Addressing the data imbalance challenge, the study employs resampling techniques, specifically downsampling with cosine similarity and oversampling using Synthetic Minority Over-sampling Technique (SMOTE), to produce balanced datasets for enhanced model performance. An extensive evaluation of various machine learning and deep learning models, including XGBoost, AdaBoost, Random Forest (RF), Feed-Forward Neural Network (FFNN), standalone 1D-CNN, and standalone LSTM, is conducted to determine their efficacy in detecting pilots' mental states. The results contribute to the development of efficient mental state detection systems, highlighting the XGBoost algorithm and the proposed 1D-CNN+LSTM model as the most promising solutions for improving safety and performance in aviation and other industries where monitoring mental states is essential

Innovative Approaches To Nursing Administration Education; A Systematic Review Based Study

Background: Nursing administration education plays a crucial role in the development of skilled leaders in the ever-evolving healthcare industry. To meet the current challenges in healthcare, it is crucial to employ innovative pedagogical approaches. These approaches include the integration of virtual reality simulations, fostering interdisciplinary collaboration, utilizing real-world case studies, implementing telehealth platforms, and establishing mentorship programs. Addressing this need for forward-thinking nursing administrators is imperative. Aim: This study comprehensively examines the impact of these innovative strategies on nursing administration education. It assesses how their integration enhances decision-making, communication, strategic thinking, technological proficiency, and leadership skills among students. The goal is to illuminate the potential of these approaches in molding skilled healthcare leaders. Method: A mixed-methods approach is utilized. Qualitative interviews with nursing administration students exposed to innovative approaches provide insights. Thematic analysis is employed to extract meaningful patterns, capturing both subjective experiences and skill development outcomes. Results: Findings highlight the transformative potential of innovative approaches in nursing administration education. Virtual reality simulations enhance decision-making, interdisciplinary collaboration fosters effective communication and teamwork, real-world case studies cultivate strategic thinking, telehealth platforms enhance remote service proficiency, and mentorship programs empower leadership competencies. Conclusion: This study underscores the pivotal role of innovation in shaping adept nursing administrators. Integration of innovative approaches equips healthcare leaders with holistic perspectives, adaptable skills, and technological readiness. As healthcare systems evolve, these approaches offer promise for addressing challenges effectively. Innovative Contribution: By delving into cutting-edge nursing administration education, this study offers insights that reshape healthcare leadership. It bridges theory and practice, equipping future administrators to navigate the dynamic healthcare landscape

Efficacy and safety of transcatheter arterial embolization for active arterial esophageal bleeding: a single-center experience

PURPOSEThe study aimed to evaluate the safety and clinical efficacy of transcatheter arterial embolization (TAE) for the treatment of arterial esophageal bleeding.METHODSNine patients (8 male, 1 female; mean age, 62.3±7.5 years) who underwent TAE for arterial esophageal bleeding between January 2004 and January 2020 were included. Preceding endoscopic treatment was unsuccessful in five patients and was not attempted in four patients due to the non-cooperation of the patients in endoscopic treatment. The etiologies of bleeding were esophageal cancer (n=4), Mallory-Weiss syndrome (n=3), erosive esophagitis (n=1), and esophageal ulcer (n=1). Technical and clinical success, recurrent bleeding, procedure-related complications, and clinical outcomes were retrospectively reviewed.RESULTSThe angiographic findings for bleeding were contrast media extravasation (n=8) or tumor staining without a definite bleeding focus (n=1). The bleeding focus at the distal esophagus (n=8) was the left gastric artery, whereas that at the middle esophagus (n=1) was the right bronchial artery. Technical success was achieved in all patients. The embolic agents were n-butyl cyanoacrylate (NBCA, n=5), gelatin sponge particles (n=2), microcoils (n=1), and NBCA with gelatin sponge particles (n=1). Clinical success was achieved in 77.8% of cases (7/9); two patients with recurrent bleeding one day after the first TAE showed culprit arteries different from the bleeding foci at the first TAE. One patient who underwent embolization of both the left and short gastric arteries died of gastric infract/perforation one month after TAE.CONCLUSIONTAE can be an alternative to the treatment of arterial esophageal bleeding. TAE can be attempted in the treatment of recurrent bleeding, but there is a risk of ischemia/infarct in the gastrointestinal tract involved

Public awareness of the coronary artery disease and its risk factors in the population of Hail region, Saudi Arabia: a cross-sectional study

Background: Coronary Artery Disease (CAD) is a severe cardiovascular complication prevalent in the Kingdom of Saudi Arabia. The risk factors of this disease are so high that it became one of the major causes of mortality among middle-aged individuals. This study aimed to investigate the degree of awareness about risk factors for CAD among the Hail region population.  Methods: The study was carried out in the Hail region, Saudi Arabia, from April 2020 to May 2020. Data collected from five hundred and thirty-seven participants participated through an online survey. The process of selection of participants was through volunteer testing and an online review poll that was disseminated to them to complete. No limitations on age or sex were applied to the surveys.Results: Awareness of TV watching (88.5%), smoking (87.9%), lack of physical activities (78.4%) and family history of CAD (74.7%) as the leading cause of CAD has a notable higher percentage among the studied population whereas the family history of diabetes mellitus (51.6%), having diabetes mellitus (57.7%), family history of hypertension (65.7%) and family history of hyperlipidemia (69.1%) have the lowest percentages. Regarding the gender, the male participants have the poorest awareness degrees about risk factors for the CAD.Conclusion: The study revealed that the family history of hyperlipidemia, Family history of DM, having DM and family history of hypertension have the poorest degrees of awareness of the risk factors for CAD among the studied population.Keywords: Coronary artery disease; Risk factors; Awareness; Hail regio

Knowledge about imaging modalities, risks, and protection in radiology among medical students at the University of Hail

Aim: The aim of this study was to evaluate awareness and knowledge about radiation risks and safety principles among medical students at the College of Medicine, University of Hail, Hail, Saudi Arabia, in their clinical years. Materials and Methods: In this cross-sectional study, an anonymous electronic questionnaire was sent to 174 randomly selected students in clinical years 4–6. The questionnaire contained 38 questions. The respondents’ answers to these questions were used to classify them according to their demographic characteristics and to evaluate their knowledge about common imaging modalities, radiation risks, and safety measures. The data were analyzed using the Statistical Package for the Social Sciences (SPSS) software, version 22. Results: Seventy-five (51.7%) of 145 respondents were female and 70 (48.3%) were male. Fifty-five respondents (37.9%) were in year 4, 38 (26.2%) were in year 5, and 52 (35.9%) were in year 6. The mean score for knowledge about common imaging modalities was 4.10 ± 2.030 of 10, that for knowledge about the risks of radiation was 3.17 ± 1.954 (range, 0–8) of 13, and that for knowledge about radiation protection measures was low at 0.79 ± 0.922 (range, 0–4) of 8. Overall, there was an improvement in knowledge about the imaging modalities and the risks of radiation as the number of clinical years increased (P = 0.000), but it was still unsatisfactory. Conclusion: The results of this study indicate that the medical students at the University of Hail have very limited knowledge about radiation risks and safety measures. These findings highlight the need for urgent action to improve students’ knowledge of these topics