Comparison of trend detection algorithms in the analysis of physiological time-series data
Comparison of trend detection algorithms in the analysis of physiological time-series data
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DOI：
10.1109/tbme.2005.844029
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发表时间：
2005-03
影响因子：
4.6
通讯作者：
W. Melek;Ziren Lu;A. Kapps;W. Fraser
中科院分区：
文献类型：
--
作者：
W. Melek;Ziren Lu;A. Kapps;W. Fraser
本文介绍了使用模糊逻辑、统计、回归和小波技术开发的各种趋势检测方法的比较性能分析。本文的主要贡献是引入了一种新的方法，即利用噪声抑制模糊聚类来提高趋势检测方法的性能。此外，这项工作的另一个贡献是一项比较调查，为选择适合不同应用需求的趋势检测方法提供了系统的指导方针。本文考虑的用于检验趋势检测算法的代表性生理变量的例子有：1)血压信号（舒张和收缩）；2)基于心电图信号RR区间的心率。此外，人工合成的生理数据被各种类型的现实生活噪声污染，并被用于测试趋势检测方法的性能，并开发对噪声不敏感的趋势检测算法。
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.