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首页> 外文期刊>American Journal of Physiology >Scale-space analysis of time series in circulatory research.
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Scale-space analysis of time series in circulatory research.

机译：循环研究中时间序列的尺度空间分析。

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Statistical analysis of time series is still inadequate within circulation research. With the advent of increasing computational power and real-time recordings from hemodynamic studies, one is increasingly dealing with vast amounts of data in time series. This paper aims to illustrate how statistical analysis using the significant nonstationarities (SiNoS) method may complement traditional repeated-measures ANOVA and linear mixed models. We applied these methods on a dataset of local hepatic and systemic circulatory changes induced by aortoportal shunting and graded liver resection. We found SiNoS analysis more comprehensive when compared with traditional statistical analysis in the following four ways: 1) the method allows better signal-to-noise detection; 2) including all data points from real time recordings in a statistical analysis permits better detection of significant features in the data; 3) analysis with multiple scales of resolution facilitates a more differentiated observation of the material; and 4) the method affords excellent visual presentation by combining group differences, time trends, and multiscale statistical analysis allowing the observer to quickly view and evaluate the material. It is our opinion that SiNoS analysis of time series is a very powerful statistical tool that may be used to complement conventional statistical methods.
机译：时间序列的统计分析在循环研究中仍然不足。随着越来越多的计算能力和来自血液动力学研究的实时记录的出现，人们越来越多地按时间序列处理大量数据。本文旨在说明使用显着非平稳性（SiNoS）方法进行的统计分析如何补充传统的重复测量方差分析和线性混合模型。我们将这些方法应用于由门静脉分流和分级肝切除术引起的局部肝脏和全身循环变化的数据集。与传统的统计分析相比，我们发现SiNoS分析在以下四个方面更加全面：1）该方法可以更好地检测信噪比; 2）将来自实时记录的所有数据点包括在统计分析中，可以更好地检测数据中的重要特征; 3）具有多种分辨率的分析有助于对材料进行更差异化的观察;和4）该方法结合了小组差异，时间趋势和多尺度统计分析，提供了出色的视觉呈现效果，使观察者可以快速查看和评估材料。我们认为，时间序列的SiNoS分析是一种非常强大的统计工具，可用于补充常规统计方法。

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