Piecewise-linear trend detection in longitudinal physiological measurements.
Recently, telecare solutions have been demonstrated as an effective means of monitoring chronic disease at a distance. A clinician may be managing many tens or hundreds of remote patients, prompting the need for a decision support system (DSS) to provide a more automated approach to managing these vast amounts of data. While simple threshold-based alert techniques provide some utility in notifying clinicians of extreme out-of-range parameter values, more incipient changes in a subject's condition may be sooner recognized by identifying trends in the longitudinal parameter data. Here we describe an approach for obtaining a piecewise-linear fit, to longitudinal physiological trend data, comparable with a similar fitting performed by a human observer, using a graphical user interface. The technique has been applied to both simulated and real data, and a comparison performed against the human scoring for each. On simulated data, the method matches or betters the human performance in most cases; with the greatest improvement observed in more noisy data. Similarly, for real physiological data, the deviation from the human marking, as a fraction of total variability of the signal, is less than 0.35.
Redmond
Stephen J
SJ
Graduate School of Biomedical Engineering, University of New South Wales, NSW 2052, Sydney, Australia.
Basilakis
Jim
J
Xie
Yang
Y
Celler
Branko G
BG
Lovell
Nigel H
NH
D000465:Algorithms; D001331:Automation; D003196:Computer Graphics; D003198:Computer Simulation; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D006801:Humans; D016011:Normal Distribution; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted; D012984:Software; D017216:Telemedicine / Q000295:instrumentation* / Q000379:methods*; D013997:Time Factors; D014584:User-Computer Interface