Date of Award

7-2021

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Biomedical and Chemical Engineering and Sciences

First Advisor

Mehmet Kaya

Second Advisor

Meredith Carroll

Third Advisor

Ersoy Subasi

Fourth Advisor

Linxia Gu

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 neurofeedback-based 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. There are many studies that have used HRV analysis only to analyze stress and there are several studies based on only EEG measurements to investigate stress. However, there were only three previous studies that combined ECG and EEG for short-term stress assessment. The first study used only one HRV feature and found a significant negative correlation between SDNN and relatively high beta power. The second study used only one electrode on the forehead for stress assessment and concluded that stress level detection accuracy using a machine learning algorithm was significantly higher when EEG features were used in combination with HRV features. The third study included HRV analysis with only the mean and standard deviation of heart rate data, and it used only one EEG feature for stress assessment. These studies did not include meditation sessions either. We extended these studies and we recorded data from the frontal lobe, and five frontal pole electrodes as these areas are known to be the major sites in terms of response to stressors. This present study demonstrated that there are five considerable relationships among EEG and HRV features associated with stress. The four HRV frequency domain features LF (low frequency), HF (high frequency), LF/HF, and rMSSD (Root Mean Square of the Successive Differences) correlated with left alpha and beta bands during stress sessions, and rMSSD correlated with alpha power asymmetry. Those results confirmed that cardiac stimulation during stress was followed by cortical activation. Our results suggest that stress may be reliably assessed by frequency-domain features and relative left alpha and beta EEG power at anterior frontal sites. This study aims to incorporate EEG and a detailed HRV analysis to better understand and analyze stress. Investigating the correlation between EEG and HRV under stress conditions is valuable since they provide complementary information regarding stress. The ability to use EEG features in combination with HRV would support diagnosis and monitor treatment/therapy progress and offers more insight to evaluate and enhance performance, learning, and decision making. Another contribution of this study can be stress management by using the HRV and EEG data as inputs for treatment applications, including meditation studies, bio-feedback training, attention deficit disorder (ADD), attention deficit hyperactivity disorder (ADHD), depression and anxiety disorders.

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