Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada.
Biomed Eng Online. 2011 Oct 10;10:90. doi: 10.1186/1475-925X-10-90.
Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis.
时间序列变化模式的分析，称为变异性分析，是一个快速发展的学科，在不同的科学领域有着越来越多的应用。在医学，特别是重症监护领域，人们一直致力于评估变异性的临床实用性。然而，适用于该领域的技术的发展和复杂性使得对变异性的解释和理解更加具有挑战性。我们的目标是提供一个适合临床应用的变异性分析技术的最新综述。我们回顾了 70 多种变异性技术，为每种技术提供了基础理论和假设的简要描述，以及临床应用的总结。我们提出了一个经过修订的变异性技术领域分类，包括统计、几何、能量、信息和不变量。我们讨论了计算过程，这通常需要对时间序列进行数学变换。我们的目的是总结广泛的文献，促进一个共同的词汇，以改善不同研究之间的思想交流和结果分析。最后，我们总结了变异性分析这门不断发展的科学所面临的挑战。
