Title page for ETD etd-083107-202452

Document Type thesis

Author Name Comtois, Gary W.

URN etd-083107-202452

Title Implementation of Accelerometer-Based Adaptive Noise Cancellation in a Wireless Wearable Pulse Oximeter Platform for Remote Physiological Monitoring and Triage

Degree MS

Department Biomedical Engineering

Advisors
Dr. Yitzhak Mendelson, Advisor
Dr. Glenn R. Gaudette, Committee Member
Dr. R. James Duckworth, Committee Member

Keywords
remote physiological monitoring
wearable pulse oximetry
adaptive noise cancellation
motion artifact

Date of Presentation/Defense 2007-07-23

Availability unrestricted

Abstract
A wireless wearable battery-operated pulse oximeter has been developed in our laboratory for field triage applications. The wearable pulse oximeter, which uses a forehead-mounted sensor to provide arterial oxygen saturation (SpO₂) and heart rate (HR) information, would enable field medics to monitor vital physiological information following critical injuries, thereby helping to prioritize life saving medical interventions. This study was undertaken to investigate if accelerometry (ACC)-based adaptive noise cancellation (ANC) is effective in minimizing SpO₂ and HR errors induced during jogging to simulate certain motion artifacts expected to occur in the field. Preliminary tests confirmed that processing the motion corrupted photoplethysmographic (PPG) signals by simple Least-Mean-Square (LMS) and Recursive Least-Squares (RLS) ANC algorithms can help to improve the signal-to-noise ratio of motion-corrupted PPG signals, thereby reducing SpO₂ and HR errors during jogging. The study showed also that the degree of improvement depends on filter order. In addition, we found that it would be more feasible to implement an LMS adaptive filter within an embedded microcontroller environment since the LMS algorithm requires significantly less operations.

Files
GComtois_Thesis_Final.pdf

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Document Type	thesis
Author Name	Comtois, Gary W.
URN	etd-083107-202452
Title	Implementation of Accelerometer-Based Adaptive Noise Cancellation in a Wireless Wearable Pulse Oximeter Platform for Remote Physiological Monitoring and Triage
Degree	MS
Department	Biomedical Engineering
Advisors	Dr. Yitzhak Mendelson, Advisor Dr. Glenn R. Gaudette, Committee Member Dr. R. James Duckworth, Committee Member
Keywords	remote physiological monitoring wearable pulse oximetry adaptive noise cancellation motion artifact
Date of Presentation/Defense	2007-07-23
Availability	unrestricted
Abstract A wireless wearable battery-operated pulse oximeter has been developed in our laboratory for field triage applications. The wearable pulse oximeter, which uses a forehead-mounted sensor to provide arterial oxygen saturation (SpO₂) and heart rate (HR) information, would enable field medics to monitor vital physiological information following critical injuries, thereby helping to prioritize life saving medical interventions. This study was undertaken to investigate if accelerometry (ACC)-based adaptive noise cancellation (ANC) is effective in minimizing SpO₂ and HR errors induced during jogging to simulate certain motion artifacts expected to occur in the field. Preliminary tests confirmed that processing the motion corrupted photoplethysmographic (PPG) signals by simple Least-Mean-Square (LMS) and Recursive Least-Squares (RLS) ANC algorithms can help to improve the signal-to-noise ratio of motion-corrupted PPG signals, thereby reducing SpO₂ and HR errors during jogging. The study showed also that the degree of improvement depends on filter order. In addition, we found that it would be more feasible to implement an LMS adaptive filter within an embedded microcontroller environment since the LMS algorithm requires significantly less operations.
Files	GComtois_Thesis_Final.pdf