Seismocardiographic assessment of cardiopulmonary health
Abstract/Contents
- Abstract
- The increasing global incidence of cardiovascular disease has resulted in a corresponding increase in global health care costs associated with the diagnosis and treatment of such disease. Traditional approaches for long-term monitoring of cardiovascular disease frequently requires hospitalization, clinical monitoring by trained professionals, and often involves the use of cumbersome, obtrusive, and expensive hardware. This work investigates the use of a low-cost, miniature, high-precision, chest-worn accelerometer for use in continuous or periodic long-term monitoring applications, such as in home monitoring settings. Over and above valuable cardiac information, a seismocardiogram (SCG) signal measured noninvasively on an accelerometer is shown here to also contain respiratory information resulting from complex cardiopulmonary interactions. A family of time- and frequency-domain cardiopulmonary SCG features are introduced here. In the time-domain, respiration-dependent features of the SCG signal that correspond to respiratory intensity and timing variations in cardiac events are shown to be quantifiably measurable on the SCG on healthy human subjects. The physiological mechanisms underlying these features are investigated; the periodicity, phase relations, and manifestations of breath hold on these SCG features are shown to be consistent with physiologically expected trends. In the frequency domain, spectral variations in the SCG signal as a function of respiration were investigated during three key conditions of respiration—inspiration, expiration, and apnea. The power spectral distribution in the SCG signal showed statistically significant variations between expiratory and inspiratory beats, and as a result of apnea. This work demonstrates that the time-domain methods for analyzing and classifying respiratory variations in the SCG signal and the frequency-domain approaches for SCG feature separation and classification could provide complementary information regarding the respiratory modulation of the SCG caused by cardiopulmonary interactions. The effects of a cardiovascular stressor, a valsalva maneuver, on spectral components of the chest accelerometer signal were investigated. These chest acceleration components derived from the accelerometer were shown to be impacted by and modulated in response to this cardiovascular trigger; these variations were congruent with changes in concurrently obtained reference measurements of stroke volume and pulse pressure. SCG preprocessing algorithms for improving robustness of SCG feature detection, were developed and evaluated. In particular, these included algorithms for reducing sensor noise detected via the accelerometer to improve the signal to noise ratio of the detected cardiac acceleration signals; enhancing and emphasizing the desired SCG features; SCG feature detection; and heart rate assessment using the detected SCG features. These algorithms were developed on training datasets (under separate stationary and motion conditions) and then evaluated using respective test datasets. The results from both the stationary and motion test datasets met the criteria for validity of heart rate monitors and interchangeability of measurement approaches suggested by prior work. Finally, as a proof-of-concept diagnostic application of the signal processing, feature extraction, and analysis methods that were developed to establish these new families of time and frequency domain cardiopulmonary SCG features, SCG signals were measured on patients with pulmonary hypertension (PH). Pulmonary hypertension (PH) is an ultimately fatal cardiopulmonary condition characterized by a prolonged increase in pulmonary arterial pressure, or right ventricular afterload. We demonstrated that time and frequency domain cardiac and cardiopulmonary features extracted from accelerometer-derived SCG signals measured on these PH patients showed a significant trend with worsened cardiac function in PH patients when compared to a healthy control population. Additionally, effects of a simple cardiovascular stressor in the form of a "breath hold stress test" on the statistical separation between the signal features obtained from the healthy population and the PH patients, was investigated. The "breath hold stress test" showed a meaningful increase in the separation between the PH and control populations—exaggerating the SCG features that were indicative of poorer cardiopulmonary performance for the PH population. The wearable form factor, and low cost of the accelerometer, as well as the potential diagnostic utility of the SCG make this sensor a compelling tool for future studies aimed at noninvasive wearable diagnostic monitoring of patients with cardiopulmonary diseases in homes, hospitals, and clinics for effective and inexpensive personalized disease management.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Pandia, Keya |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. |
| Primary advisor | De la Zerda, Adam |
| Thesis advisor | De la Zerda, Adam |
| Thesis advisor | Howe, Roger Thomas |
| Thesis advisor | Nishimura, Dwight George |
| Advisor | Howe, Roger Thomas |
| Advisor | Nishimura, Dwight George |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Keya Pandia. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Keya Rajeev Pandia
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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