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|Title:||Dynamic Threshold Analysis of Daily Oxygen Saturation for Improved Management of COPD Patients|
|Keywords:||Remote patient monitoring;Telehealth;SpO2|
|Citation:||IEEE Journal of Biomedical and Health Informatics (J-BHI)|
|Abstract:||This study presents a novel dynamic threshold algorithm that is applied to daily self-measured SpO2 data for management of COPD patients in remote patient monitoring to improve accuracy of detection of exacerbation. Conventional approaches based on a fixed threshold applied to a single SpO2 reading to detect deterioration in patient condition are known to have poor accuracy and result in high false alarm rates. This study develops and evaluates use of a dynamic threshold algorithm to reduce false alarm rates. Daily data from four COPD patients with a record of clinical interventions during the period were selected for analysis. We model the SpO2 time series data as a combination of a trend and a stochastic component (residual). We estimate the long-term trend using a locally weighed least squares (low-pass) filter over a long-term processing window. Results show that the time evolution of the long-term trend indicated exacerbation with improved accuracy compared to a fixed threshold in our study population. Deterioration in the condition of a patient also resulted in an increase in the standard deviation of the residual (σres), from 2% or less when the patient is in a healthy condition to 4% or more when condition deteriorates. Statistical analysis of the residuals showed they had a normal distribution when the condition of the patient was stable but had a long tail on the lower side during deterioration.|
|Description:||© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.|
|Appears in Collections:||Dept of Computer Science Research Papers|
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