No abstract

Pulse oximetry is a non-invasive technique for measuring the amount of oxygen in a patient's arterial blood, as a percentage of the blood's oxygen carrying capacity (SpO 2 ). This measurement is considered standard of care in the hospital for monitoring the cardio-respiratory function of a patient. While it has potential uses in ambulatory or wearable monitoring applications, pulse oximetry is particularly susceptible to motion artifact contamination. This thesis presents efforts to quantify and model the effects of motion artifact, and automatically detect periods of poor signal quality.First, the effects of motion artifact on SpO 2 are analyzed using motion contaminated data. Second, two models are identified from previous literature that may explain the effects of motion artifact on pulse oximetry. These models are developed analytically and evaluated using isolated motion artifact signals. Finally, three automatic signal quality assessment algorithms are proposed. These algorithms are shown to discriminate between clean and motion contaminated signals.Overall, this thesis attempts to inform the development of software and hardware based techniques to mitigate the effects of poor signal quality on pulse oximetry.iii

Abstract-Oxygen saturation measurements from pulse oximetry (SpO 2 ) can be unreliable in the presence of motion artifacts. While pulse oximetry is a crucial measurement in controlled environments, such as surgery or intensive care, its vulnerability to motion artifacts has slowed its adoption in wearable continuous monitoring devices. Measurement error can cause errors or delays in clinical decision-making. In remote monitoring applications, pulse oximeters should report measurement confidence along with SpO 2 to help clinicians make decisions about the validity of alarm conditions. This paper seeks to relate signal quality to SpO 2 measurement confidence.In this study, clean photoplethysmograph (PPG) signals were collected from a pulse oximeter and contaminated with motion artifact. A range of linear combinations of signal and artifact were generated and SpO 2 measurements were calculated. Since true SpO 2 remained constant, measurement variation was caused solely by signal contamination. Unacceptably high measurement error was found below the 15-20 dB signal to noise ratio (SNR) range.Two models based on Additive White Gaussian Noise (AWGN) were evaluated for their similarity to the motion artifact data. The first had identical noise on both red and infrared PPG signals; the second has uncorrelated noise. Both models successfully predicted negative measurement bias at low SNR, but only the second predicted the observed measurement variance.