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Drowsiness is a main factor for various costly defects, even fatal accidents in areas such as construction, transportation, industry and medicine, due to the lack of monitoring vigilance in the mentioned areas. The implementation of a drowsiness detection system can greatly help to reduce the defects and accident rates by alerting individuals when they enter a drowsy state. This research proposes an electroencephalography (EEG)-based approach for detecting drowsiness. EEG signals are passed through a preprocessing chain composed of artifact removal and segmentation to ensure accurate detection followed by different feature extraction methods to extract the different features related to drowsiness. This work explores the use of various machine learning algorithms such as Support Vector Machine (SVM), the K nearest neighbor (KNN), the Naive Bayes (NB), the Decision Tree (DT), and the Multilayer Perceptron (MLP) to analyze EEG signals sourced from the DROZY database, carefully labeled into two distinct states of alertness (awake and drowsy). Segmentation into 10 s intervals ensures precise detection, while a relevant feature selection layer enhances accuracy and generalizability. The proposed approach achieves high accuracy rates of 99.84% and 96.4% for intra (subject by subject) and inter (cross-subject) modes, respectively. SVM emerges as the most effective model for drowsiness detection in the intra mode, while MLP demonstrates superior accuracy in the inter mode. This research offers a promising avenue for implementing proactive drowsiness detection systems to enhance occupational safety across various industries.

Functional gastrointestinal disorders (FGIDs) are highly prevalent in medical students around the world. However, there is no specific data on FGIDs in Tunisia. The objectives of this study were to evaluate the prevalence of FGIDs in medical students according to the rome III criteria and to identify risk factors associated with these disorders. A self-administered questionnaire survey was carried out among the students from the first and the second year of medical studies. We studied the influence of socio-demographic characteristics, lifestyle, health care seeking, psychosomatic symptoms and hospital anxiety and depression scale on the prevalence of FGIDs among these students. Three hundred and forty-three students (20.3 ± 0.8years) were included in our study. The prevalence of FGIDs was 54.2%. The main FGIDs found were the unspecified functional bowel disorder (46.6%), functional constipation (11.6%), irritable bowel syndrome (7.6%) and functional dyspepsia (6.7%). In logistic regression, abnormal BMI (OR = 2.1, 95% CI= 1–4.3), living in school dormitory (OR = 3.7, 95% CI = 1.7–7.8), low water intake (OR = 2.2, 95% CI = 1.1–4.2), digestive medication use (OR = 3.4, 95% CI= 1.3–8.5), and probable or definite anxiety (OR = 2.5, 95% CI = 1.1–5.8) were the five risk factors associated with FGIDs. We demonstrate a high prevalence of FGIDs (54.2%) among our students. Risk factors for FGIDs were abnormal BMI, living in school dormitory, low water intake, digestive medication use and anxiety.

Background: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a global threat impacting the lives of millions of people worldwide. Automated detection of lung infections from Computed Tomography scans represents an excellent alternative; however, segmenting infected regions from CT slices encounters many challenges. Objective: Developing a diagnosis system based on deep learning techniques to detect and quantify COVID-19 infection and pneumonia screening using CT imaging. Method: Contrast Limited Adaptive Histogram Equalization pre-processing method was used to remove the noise and intensity in homogeneity. Black slices were also removed to crop only the region of interest containing the lungs. A U-net architecture, based on CNN encoder and CNN decoder approaches, is then introduced for a fast and precise image segmentation to obtain the lung and infection segmentation models. For better estimation of skill on unseen data, a fourfold cross-validation as a resampling procedure has been used. A three-layered CNN architecture, with additional fully connected layers followed by a Softmax layer, was used for classification. Lung and infection volumes have been reconstructed to allow volume ratio computing and obtain infection rate. Results: Starting with the 20 CT scan cases, data has been divided into 70% for the training dataset and 30% for the validation dataset. Experimental results demonstrated that the proposed system achieves a dice score of 0.98 and 0.91 for the lung and infection segmentation tasks, respectively, and an accuracy of 0.98 for the classification task. Conclusions: The proposed workflow aimed at obtaining good performances for the different system’s components, and at the same time, dealing with reduced datasets used for training.

This study presents an architecture‐optimising methodology for embedding an learning vector quantization (LVQ) neural network on an field programmable gate array (FPGA) device. The embedded architecture contains both learning and decision circuitry and is optimised towards the lowest power/energy consumption. The low‐power/energy architecture is obtained through the selection of the best one amongst a number of architectures produced by FPGA software design tools that combine power, area and the ergonomic utilisation of internal FPGA resources. A complete characterisation of power at the architectural level was carried out using the Xpower tool. An analytical power model was determined by the following parameters: area, delay and LVQ topology. Concerning the authors’ architecture, there is a 28% gain in the area design. Moreover, it consumes 8% power in the nanoboard 3000 compared with the other ones.

This paper shows that the grammar formalism can be pushed to be applied for the description and the classification of electrocardiograms signals (ECG). We will describe an ECG signal as a sequence of tokens based on specific vocabularies and a set of grammatical rules. QRS complexes, RR distances, PR and QT intervals will be calculated. This type of work is intended for medical diagnosis assistance of an ECG signal.