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Comparison of trend detection algorithms in the analysis of physiological time-series data

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4 Author(s)
W. W. Melek ; Dept. of Mech. Eng., Waterloo Univ., Ont., Canada ; Z. Lu ; A. Kapps ; W. D. Fraser

This paper presents a comparative performance analysis of various trend detection methods developed using fuzzy logic, statistical, regression, and wavelet techniques. The main contribution of this paper is the introduction of a new method that uses noise rejection fuzzy clustering to enhance the performance of trend detection methodologies. Furthermore, another contribution of this work is a comparative investigation that produced systematic guidelines for the selection of a proper trend detection method for different application requirements. Examples of representative physiological variables considered in this paper to examine the trend detection algorithms are: 1) blood pressure signals (diastolic and systolic); and 2) heartbeat rate based on RR intervals of electrocardiography signal. Furthermore, synthetic physiological data intentionally contaminated with various types of real-life noise has been generated and used to test the performance of trend detection methods and develop noise-insensitive trend-detection algorithms.

Published in:

IEEE Transactions on Biomedical Engineering  (Volume:52 ,  Issue: 4 )