Stress Analysis Based on ECG and EEG

Abstract

Stress is a significant risk factor for various diseases such as hypertension, heart attack, stroke, and even sudden death. In many studies, stress is linked to decision making, performance, and learning. The heart rate variability (HRV) can be measured from the Electrocardiogram (ECG) and is defined as the variation of the interval between two consecutive heartbeats (heart rate). Heart rate variability is also an indicator of the balance between the sympathetic and parasympathetic branches of the autonomic nervous system. HRV refers to variations of heart rate in a certain amount of time. Heart rate variability (HRV) is a relatively new method for evaluating stress. What makes HRV remarkable is that it can reflect stress changes while other physiological factors, like blood pressure, are still average or within acceptable ranges. Electroencephalogram (EEG) is the most convenient modality to analyze the cortical response to stress due to its low-cost and practical use. Additionally, since EEG has a high temporal resolution, it provides useful information to explore variability in a mental state. EEG also serves as a valuable tool for neurofeedbackbased rehabilitation. In this study, electrocardiogram (ECG) and EEG recordings were obtained simultaneously from 15 subjects. HRV features were extracted from the ECG based heart rate data. Combined ECG and EEG features were analyzed and compared in three conditions: rest, stress, and mediation. A one-way ANOVA and correlation coefficient were used for statistical analysis to explore the correlation between HRV features and features extracted from EEG.