In the modern world, wearable smart devices are continuously used to monitor people’s health. This study aims to develop an automatic mental stress detection system for researchers based on Electrocardiogram (ECG) signals from smart T-shirts using machine learning classifiers. We used 20 subjects, including 10 from mental stress (after twelve hours of continuous work in the laboratory) and 10 from normal (after completing the sleep or without any work). We also applied three scoring techniques: Chalder Fatigue Scale (CFS), Specific Fatigue Scale (SFS), Depression, Anxiety, and Stress Scale (DASS), to confirm the mental stress. The total duration of ECG recording was 1800 min, including 1200 min during mental stress and 600 min during normal. We calculated two types of features, such as demographic and extracted by ECG signal. In addition, we used Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Logistic Regression (LR) to classify the intra-subject (mental stress and normal) and inter-subject classification. The DT leave-one-out model has better performance in terms of recall (93.30%), specificity (96.70%), precision (94.40%), accuracy (93.30%), and F1 (93.50%) in the intra-subject classification. Additionally, The classification accuracy of the system in classifying inter-subjects is 94.10% when using a DT classifier. However, our findings suggest that the wearable smart T-shirt based on the DT classifier may be used in big data applications and health monitoring. Mental stress can lead to mitochondrial dysfunction, oxidative stress, blood pressure, cardiovascular disease, and various health problems. Therefore, real-time ECG signals help assess cardiovascular and related risk factors in the initial stage based on machine learning techniques.

Context: The decoctions of Ficus carica Linn. (Moraceae) leaves are used in the folklore treatment of diabetes. Objective: To evaluate the effect of F. carica on glucose and lipids levels, carbohydrate metabolism enzymes and β-cells protective effects in type 2 diabetes. Material and methods: Diabetes was induced in 15 days high-fat diet (HFD)-fed Wistar rats by intraperitoneal injection of streptozotocin (STZ) (40 mg/kg). The ethyl acetate extract (250 and 500 mg/kg) of F. carica leaves was administered for 28 days. Oral glucose tolerance (OGTT) and intraperitoneal insulin tolerance tests (ITT) were evaluated on 15th and 25th days, respectively. Results: The ethyl acetate extract (250 and 500 mg/kg) of n F. carica leaves showed significant effect (p < 0.005) in the levels of blood glucose, total cholesterol (TC), triglycerides (TG), body weight and hepatic glycogen. In OGTT, F. carica (250 and 500 mg/kg) significantly (p < 0.005) detained the increase in blood glucose levels at 60 and 120 min and in ITT, F. carica enhanced the glucose utilization significantly (p < 0.005) over 30 and 60 min compared to diabetic control. Further, the altered activities of key carbohydrate metabolizing enzymes such as glucose-6-phosphatase, fructose-1,6-bisphosphatase and hexokinase in the liver tissue of diabetic rats were significantly (p < 0.005) reverted to near normal levels upon treatment with F. carica. Immumohistochemical studies of islets substantiated the cytoprotective effect on pancreatic β-cells. Discussion and conclusions: F. carica leaves exerted significant effect on carbohydrate metabolism enzymes with promising hypoglycemic and hypolipidemic activities in type 2 diabetic rats.