Signal Quality Measures on Pulse Oximetry and Blood Pressure Signals Acquired from Self-Measurement in a Home Environment

Recently, decision support system (DSSs) have become more widely accepted as a support tool for use with telehealth systems, helping clinicians to summarize and digest what would otherwise be an unmanageable volume of data. One of the pillars of a home telehealth system is the performance of unsuper...

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Bibliographic Details
Published in:	IEEE journal of biomedical and health informatics Vol. 19; no. 1; pp. 102 - 108
Main Authors:	Abd Sukor, Jumadi, Mohktar, Mas S., Redmond, Stephen J., Lovell, Nigel H.
Format:	Journal Article
Language:	English
Published:	United States IEEE 01-01-2015 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Aged Algorithms Automation Biomedical measurement Blood Pressure Blood Pressure Determination - standards Blood Pressure Determination - statistics & numerical data Classification algorithms Decision support systems Ecosystem Estimation Female Geriatric Assessment - methods Humans Male Measurement techniques Medical equipment Middle Aged Noise Oximetry - standards Oximetry - statistics & numerical data Physiology Pollution measurement Pulse measurements Pulse Oximetry Quality Assurance, Health Care - methods Reproducibility of Results Self Care - standards Self Care - statistics & numerical data Sensitivity and Specificity signal quality Telemedicine Telemedicine - standards Telemedicine - statistics & numerical data pulse oximetry signal quality Blood pressure (BP)
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Summary:	Recently, decision support system (DSSs) have become more widely accepted as a support tool for use with telehealth systems, helping clinicians to summarize and digest what would otherwise be an unmanageable volume of data. One of the pillars of a home telehealth system is the performance of unsupervised physiological self-measurement by patients in their own homes. Such measurements are prone to error and noise artifact, often due to poor measurement technique and ignorance of the measurement and transduction principles at work. These errors can degrade the quality of the recorded signals and ultimately degrade the performance of the DSS system, which is aiding the clinician in their management of the patient. Developed algorithms for automated quality assessment for pulse oximetry and blood pressure (BP) signals were tested retrospectively with data acquired from a trial that recorded signals in a home environment. The trial involved four aged subjects who performed pulse oximetry and BP measurements by themselves at their home for ten days, three times per day. This trial was set up to mimic the unsupervised physiological self-measurement as in a telehealth system. A manually annotated "gold standard" (GS) was used as the reference against which the developed algorithms were evaluated after analyzing the recordings. The assessment of pulse oximetry signals shows 95% of good sections and 67% of noisy sections were correctly detected by the developed algorithm, and a Cohen's Kappa coefficient (κ) of 0.58 was obtained in 120 pooled signals. The BP measurement evaluation demonstrates that 75% of the actual noisy sections were correctly classified in 120 pooled signals, with 97% and 91% of the signals correctly identified as worthy of attempting systolic and/or diastolic pressure estimation, respectively, with a mean error and standard deviation of 2.53\pm 4.20 mmHg and 1.46\pm 5.29 mmHg when compared to a manually annotated GS. These results demonstrate the feasibility, and highlight the potential benefit, of incorporating automated signal quality assessment algorithms for pulse oximetry and BP recording within a DSS for telehealth patient management.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2168-2194 2168-2208
DOI:	10.1109/JBHI.2014.2361654