Signal quality measures on pulse oximetry and blood pressure signals acquired from self-measurement in a home environment

• Main Authors: Abd Sukor, J., Mohktar, M.S., Redmond, S.J., Lovell, N.H.
• Format: Article
• Language: English
• Published: 2015
• Subjects: T Technology (General); TA Engineering (General). Civil engineering (General); TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.um.edu.my/14046/1/Signal_quality_measures_on_pulse_oximetry_and_blood_pressure.pdf; http://eprints.um.edu.my/14046/; http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6917002

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 (kappa) 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.