Revista Mexicana de Ingenieria Biomedica

Preventive Detection of Driver Drowsiness from EEG Signals using Fuzzy Expert Systems

Thu, 29 Feb 2024 00:00:00 +0000

Currently, the percentage of traffic accidents has increased, and according to statistics, this percentage will continue to increase every year, so it is necessary to develop new technologies to prevent this kind of accidents. This paper presents a drowsiness detection system based on electroencephalogram (EEG) signals using a pair of channels (Fp1 and Fp2) applied to drivers before entering their vehicles. First, this model detects the relationship between the area under the curve (AUC) of alpha brain waves, an effective parameter for detecting drowsiness. Then, the extracted information is passed to a fuzzy expert system (FES) that classifies the subject's state as "alert" or "sleepy"; the criterion used was a threshold and training with subjective levels. The proposed system was compared with neural network models, such as support vector machine (SVM), K nearest neighbors (KNN), and random forest (RF). Measurements of one hundred and twenty minutes were performed on each of the ten drivers for two days to test the system. The tests confirm that this system is suitable for preventive measures and that the fuzzy system is superior to traditional neural network methods.

Nonlinear Mathematical Analysis based on an Insulin-Pancreatic Cells Model in the Presence of Epinephrine

Diana Gamboa, Paúl J. Campos — Tue, 12 Mar 2024 00:00:00 +0000

In this work, a nonlinear model is studied based on ordinary differential equations that describe the relationship between the mass of cells and the secretion of epinephrine. It analyzes the impact of stress associated with the cause of increased blood pressure and glucose levels in the body. The mathematical analysis is based on the appliance of the nonlinear control theory to define the maximum load capacity for each state variable, establishing a bounded positive invariant domain through the Localization of Compact Invariants Sets (LCIS) method. The objective is to determine the effects of epinephrine secretion on the increase of blood glucose levels; therefore, this analysis's results define the necessary and sufficient conditions in which epinephrine raises insulin and glucose levels in the presence of cells. The interest in studying this type of disease focuses on searching for a treatment or an analysis that guarantees complete control of glucose levels. This work's development and mathematical analysis strengthen current research on insulin-dependent diabetes mellitus around critical epinephrine factors that imply an increase in glucose in the body.

Study of the Length of time Window in Emotion Recognition based on EEG Signals

Wed, 20 Mar 2024 00:00:00 +0000