Date of Award

12-2019

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

Kenia Nunes Bruhn

Fourth Advisor

Ted Conway

Abstract

Chapter 1 - An introduction to autonomic nervous system (ANS) and cardiovascular system (CVS): The performance of the ANS can be evaluated by quantizing the changes made by the autonomic nervous system. The most common parameters that are being used in autonomic nervous system assessment tests are blood pressure, heart rate, pressure pulse wave velocity, heart rate variability, and pulse waveform. In this dissertation, we designed and developed a system that assesses the activity of these two systems. Therefore, we represented four methods to 1- evaluate the dependence of neuro- and cardiovascular systems on sex, 2- non-invasively estimate the blood pressure, 3- assess the feasibility of replacing the tilt tables with zero-gravity chairs, and 4- simulating the heart rate variability. Chapter 2 - Feature extraction: Since most of the biomedical data cannot be directly used for machine learning and pattern recognition purposes, the attributes of the data are transformed into sets of values called features. The extracted features reveal information about the observed data that may not be noticeable from the raw data. Chapter 3 - Customized measurement system: One of the essential steps in biomedical machine learning methods is the data collection procedure. A recording system was designed and developed that allows for a two-channel (finger and toe) photoplethysmography recordings as well as a single-channel electrocardiography recording. Chapter 4 - Sex-related differences in photoplethysmography signals: It has been shown that sex plays an important role in relation to certain diseases [2]. It has been reported that men have higher risk levels for cardiovascular diseases compared to pre-menopausal women [28]. This risk factor is valid for many cardiovascular diseases such as hypertension and coronary artery diseases. Therefore, it is essential to understand the contributing physiological factors leading to the mentioned differences in terms of sex. In order to perform a comprehensive analysis, the dependence of physiological parameters (weight, height, BMI, age, and sex), temporal and morphological features of photoplethysmography signals from the toe and finger, and also cardiovascular information (heart rate and the variance) and their effect on one another was investigated. The result showed that there were differences between the photoplethysmography signals from the toe and finger. These findings can be used to improve the performance of methods such as continuous non-invasive blood pressure. Chapter 5 - Cuff-less and subject-independent blood pressure monitoring: The most commonly performed non-invasive blood pressure measurement is with the use of an inflatable cuff, which does not allow for continuous BP recordings [37]. Even the conventional 24-hour ambulatory blood pressure monitoring can only be done at regular intervals, and they still require the use of a cuff [38]. Even though there are two food and drug administration (FDA)-cleared methods, including Sotera VISI Mobile and SOMNOtouch continuous noninvasive BP monitoring systems, the research on finding a clinically valid cuff-less method is still ongoing since existing methods still struggle with accuracy and calibration concerns [39]. We came up with two methods to improve the accuracy of our BP estimation algorithm by 1- adding/using parameters from toe photoplethysmography signal; 2- including additional parameters with considerable dependence on the blood pressure. While calibration-based methods need to modify the estimator model for each subject and then track the BP trajectory, designing a subject-independent algorithm –that is not based on an initial or periodic calibration is one of the challenges in BP estimation. The importance of this study is not only based on its ability to estimate continuous BP without any initial cuff-based measurement, but also the integration and assessment of the toe photoplethysmography signal, and additional subject information such as weight, age, height, and BMI. This study showed that involving these additional parameters results in improved accuracy. Chapter 6 - Comparison of zero-gravity chairs and tilt tables: The tilt table test is frequently used to assess the body’s response to changes in position as an indicator of the ANS activity, and its performance. This test has been used to diagnose dysfunctionality in the autonomic nervous system or syncope. Using the tilt table test, the changes in vital cardiovascular parameters such as blood pressure, heart rate, and heart rate variability is tracked during the imposed changes in the posture. The signals that are commonly recorded during the procedure include ECG, blood oxygen saturation level, photoplethysmography signal, blood pressure, and heart rate variability. In this study, we used a zero-gravity chair to measure the heart rate variability and compared the data with measurements from a conventional tilt table. The measurement protocol involved the recruitment of participants and testing them on both zero-gravity chair and tilt table. A comprehensive analysis was performed to evaluate the results of these two setups. Finally, it was concluded that the results from the tests performed on the zero-gravity chairs were statistically correlated to the tilt table and therefore, it could be a feasible alternative to the tilt tables. Chapter 7 - HRV simulation: Due to the diversity of clinical and research applications associated with HRV analysis, detection software and devices have been under development. Although the testing software exists for this type of application, further improvements need to be made. Producing customized heartbeat signal simulations with adjustable HRV characteristics can provide researchers with the means to more effectively test the accuracy of HRV detection methodologies. By modeling the relationship between HRV quantities, heartbeat data set can be produced with predetermined values for the various heart rate variability indicators. The goal of this study is to make a mathematical model that generates artificial ECG signals based on predefined features.

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