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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| Main Authors: | , , , |
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| Format: | Article |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2014
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1109/JBHI.2014.2361654 http://dx.doi.org/10.1109/JBHI.2014.2361654 http://eprints.uthm.edu.my/6496/1/jumaidi_abdul_sukor_U.pdf |
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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 ± 4.20 mmHg and
1.46 ± 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. |
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