Development of Motion Artifact Rejection Algorithms for Ambulatory Heart Rate and Arterial Oxygen Measurement By A Wearable Pulse Oximeter Public
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Over the past decade, there has been an increasing interest in the real-time monitoring of ambulatory vital signs such as heart rate (HR) and arterial blood oxygen saturation (SpO2) using wearable medical sensors during field operations. These measurements can convey valuable information regarding the state of health and allow first responders and front-line medics to better monitor and prioritize medical intervention of military combatants, firefighters, miners and mountaineers in case of medical emergencies. However, the primary challenge encountered when using these sensors in a non-clinical environment has been the presence of persistent motion artifacts (MA) embedded in the acquired physiological signal. These artifacts are caused by the random displacement of the sensor from the skin and lead to erroneous output readings. Several signal processing techniques, such as time and frequency domain segmentation, signal reconstruction techniques and adaptive noise cancellation (ANC), have been previously developed in an offline environment to address MA in photoplethysmography (PPG) with varying degrees of success. However, the performance of these algorithms in a spasmodic noise environment usually associated with basic day to day ambulatory activities has still not been fully investigated. Therefore, the focus of this research has been to develop novel MA algorithms to combat the effects of these artifacts. The specific aim of this thesis was to design two novel motion artifact (MA) algorithms using a combination of higher order statistical tools namely Kurtosis (K) for classifying 10 s PPG data segments, as either Â‘cleanÂ’ or Â‘corruptÂ’ and then extracting the aforementioned vital parameters. To overcome the effects of MA, the first algorithm (termed Â‘MNAÂ’) processes these Â‘corruptÂ’ PPG data segments by identifying abnormal amplitudes changes. The second algorithm (termed 'MNA'), filters these Â‘corruptÂ’ data segments using a 16th order normalized least mean square (NLMS) ANC filter and then extracts HR and SpO2.
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