Master of Science / Department of Electrical and Computer Engineering / Steven Warren / Pulse oximetry, a noninvasive circulatory system monitoring technique, has been widely adopted in clinical and homecare applications for the determination of heart rate and blood oxygen saturation, where measurement locations are typically limited to fingertips and earlobes. Prior research indicates a variety of additional clinical parameters that can be derived from a photoplethysmogram (PPG), the fundamental time-domain signal yielded by a pulse oximeter sensor. The gap between this research potential and practical device applications can be decreased by improvements in device design (e.g., sensor performance and geometry, sampling fidelity and reliability, etc.) and PPG signal processing.
This thesis documents research focused on a novel pulse oximeter design and the accompanying PPG signal processing and interpretation. The filter-free reflectance design adopted in the module supplements new methods for signal sampling, control, and processing, with a goal to acquire high-fidelity raw data that can provide additional physiologic data for state-of-health analyses. Effective approaches are also employed to improve signal stability and quality, including shift-resistant baseline control, an anti-aliasing sampling frequency, light emitting diode intensity autoregulation, signal saturation inhibition, etc. MATLAB interfaces provide data visualization and processing for multiple applications. A feature detection algorithm (decision-making rule set) is presented as the latest application, which brings the element of intelligence into the pulse oximeter design by enabling onboard signal quality verification.
Two versions of the reflectance sensor were designed, built, calibrated, and utilized in data acquisition work. Raw data, which are composed of four channels of signals at a 240 Hz sampling rate and a 12-bit precision, successfully stream to a personal computer via a serial connection or wireless link. Due to the optimized large-area sensor and the intensity autoregulation mechanism, PPG signal acquisition from measurement sites other than fingertips and earlobes, e.g., the wrist, become viable and retain signal quality, e.g., signal-to-noise ratio. With appropriate thresholds, the feature detection algorithm can successfully indicate motion occurrence, signal saturation, and signal quality level. Overall, the experimental results from a variety of subjects and body locations in multiple applications demonstrate high quality PPGs, prototype reliability, and prospects for further research value.
| Identifer | oai:union.ndltd.org:KSU/oai:krex.k-state.edu:2097/4143 |
| Date | January 1900 |
| Creators | Li, Kejia |
| Publisher | Kansas State University |
| Source Sets | K-State Research Exchange |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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