Real-Time Telemonitoring of Student’s Stress Levels using HRV Analysis

Abstract

Telemonitoring, is the use of information technology to monitor the vital parameters of patients at a distance and providing them to the doctor at remote location. In today’s world, our life is rooted with stress by family circumstances, financial concerns, job, etc. High stress levels can lead to heart attacks and chronic headaches or psychological diseases like mental illness, anger, anxiety, and depression. By using physiological signals for example Electrocardiogram (ECG), human stress levels can be determined. Stress induces a change in Heart Rate Variability (HRV), so it is used as an indicator for stress. HRV analysis can be derived from ECG signal and reflects automatic nervous system state of the human body. In this work, ECG signals of the students are collected before presentation, immediately after presentation and one hour after presentation to perform HRV analysis. This analysis is accomplished using time domain, frequency domain and non-linear methods. After HRV analysis, some parameters are extracted to identify and classify stress. The obtained ECG parameters can be studied by an expert sitting at a distance through internet using LabVIEW web publishing tool. The validation of this algorithm is done by implementing this work on ECG signals, collected from MIT-BIH database. The set of collected data is called the Stress Recognition in Automobile Driver database (DRIVEDB). In the absence of doctor, this experiment gives display of the patient’s condition as a report, making medical diagnosis safer and robust.