Efficient Generalized Electroencephalography-Based Drowsiness Detection Approach with Minimal Electrodes.

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.

Effect of metformin combined with lifestyle modification versus lifestyle modification alone on proinflammatory-oxidative status in drug-naïve pre-diabetic and diabetic patients: A randomized controlled study.

BACKGROUND: Targeting biomarkers of oxidative-proinflammatory stress may result in improvement of modifiable metabolic syndrome, pre-diabetes and diabetes risk factors and subsequent risk reduction. METHODS: 64 newly diagnosed antihyperglycemic treatment-naïve prediabetic and type 2 diabetes mellitus (T2DM) patients were randomly assigned using block design to either metformin combined with therapeutic lifestyle changes (TLC) or TLC alone. Body mass index (BMI), waist circumference, blood pressure, fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), fasting lipid profile, plasma oxidative status and tumor necrosis factor (TNF)-α were measured at baseline, after 3 months and after 6 months from baseline. RESULTS: Except for HbA1c, baseline values did not differ significantly between the two groups. The post 3-months relative reductions in BMI (P=0.014) and HbA1c (P=0.037) in metformin combined with TLC intervention were significantly greater than those in TLC alone group. TNFα plasma levels were decreased significantly vs. baseline by metformin combined with TLC intervention (-22.90±46.76%, P=0.01). Conversely, TLC alone basically worsened proinflammatory status (42.40±40.82 %), P<0.001. Metformin with TLC treatment effected a therapeutic decrement of the oxidative stress (-15.44±35.32%, P=0.029 vs. baseline) unlike TLC alone (61.49±122.66%, P=0.01 vs. baseline). Both interventions' effects were sustained in the 6-month follow up periods. CONCLUSION: In both intervention groups, the relative changes in plasma TNFα were significantly correlated (P<0.01) with systolic blood pressure and the relative changes in oxidative stress were markedly correlated (P<0.05) with total cholesterol.