{"id":{"repo_id":"carleton","oai_identifier":"oai:carleton.scholaris.ca:20.500.14718/32304"},"canonical_url":"https://search.dev.ndltd.org/etd/carleton/oai:carleton.scholaris.ca:20.500.14718/32304","repository":{"repo_id":"carleton","name":"Carleton University","base_url":"https://carleton.scholaris.ca/server/oai/request"},"display":{"title":"Signal Quality Analysis in Pulse Oximetry: Modelling and Detection of Motion Artifact","abstract":"Pulse oximetry is a non-invasive technique for measuring the amount of oxygen in a patient's blood (SpO2). It is considered standard of care in the hospital for monitoring cardio-respiratory function. While it has potential uses in ambulatory or wearable applications, pulse oximetry is 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 SpO2 are analyzed using motion contaminated data. Second, two models are identified from previous literature that may explain the effects of motion artifact. These models are developed analytically and evaluated using isolated motion artifact signals. Finally, three signal quality assessment algorithms are proposed. These algorithms are shown to discriminate between clean and contaminated signals. This thesis attempts to inform the development of techniques to mitigate the effects of poor signal quality on pulse oximetry.","abstract_html":"Pulse oximetry is a non-invasive technique for measuring the amount of oxygen in a patient's blood (SpO2). It is considered standard of care in the hospital for monitoring cardio-respiratory function. While it has potential uses in ambulatory or wearable applications, pulse oximetry is 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 SpO2 are analyzed using motion contaminated data. Second, two models are identified from previous literature that may explain the effects of motion artifact. These models are developed analytically and evaluated using isolated motion artifact signals. Finally, three signal quality assessment algorithms are proposed. These algorithms are shown to discriminate between clean and contaminated signals. This thesis attempts to inform the development of techniques to mitigate the effects of poor signal quality on pulse oximetry.","abstract_has_math":false,"creators":["Clarke, Geoffrey William Jon"],"institution":"Carleton University","degree_name":"Master of Applied Science (M.App.Sc.)","degree_level":"Master's","degree_discipline":"Engineering, Biomedical","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015","date_published":"2015","updated_at":"2026-07-24T01:34:24Z","subjects":[],"languages":["en"],"rights":["Copyright © 2015 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. 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It is considered standard of care in the hospital for monitoring cardio-respiratory function. While it has potential uses in ambulatory or wearable applications, pulse oximetry is 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 SpO2 are analyzed using motion contaminated data. Second, two models are identified from previous literature that may explain the effects of motion artifact. These models are developed analytically and evaluated using isolated motion artifact signals. Finally, three signal quality assessment algorithms are proposed. These algorithms are shown to discriminate between clean and contaminated signals. 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