使用近似熵和样本熵测量体温时间序列的规律性。
Measuring body temperature time series regularity using Approximate Entropy and Sample Entropy.
作者信息
Cuesta-Frau D, Miro-Martinez P, Oltra-Crespo S, Varela-Entrecanales M, Aboy M, Novak D, Austin D
机构信息
Technological Institute of Informatics, Polytechnic University of Valencia, Alcoi Campus, 03801 Alcoi, Spain.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:3461-4. doi: 10.1109/IEMBS.2009.5334602.
Approximate Entropy (ApEn) and Sample Entropy (SampEn) have proven to be a valuable analyzing tool for a number of physiological signals. However, the characterization of these metrics is still lacking. We applied ApEn and SampEn to body temperature time series recorded from patients in critical state. This study was aimed at finding the optimal analytical configuration to best distinguish between survivor and non-survivor records, and at gaining additional insight into the characterization of such tools. A statistical analysis of the results was conducted to support the parameter and metric selection criteria for this type of physiological signal.
近似熵（ApEn）和样本熵（SampEn）已被证明是用于分析多种生理信号的有价值工具。然而，这些指标的特征描述仍很欠缺。我们将ApEn和SampEn应用于危重症患者记录的体温时间序列。本研究旨在找到最佳分析配置，以最好地区分存活者和非存活者记录，并进一步深入了解此类工具的特征。对结果进行了统计分析，以支持针对此类生理信号的参数和指标选择标准。
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