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回忆过去的时间序列:用于可视化生物医学信号趋势的简单色彩方法。

Remembrance of time series past: simple chromatic method for visualizing trends in biomedical signals.

作者信息

Burykin Anton, Mariani Sara, Henriques Teresa, Silva Tiago F, Schnettler William T, Costa Madalena D, Goldberger Ary L

机构信息

Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA.

出版信息

Physiol Meas. 2015 Jul;36(7):N95-102. doi: 10.1088/0967-3334/36/7/N95. Epub 2015 May 27.

Abstract

Analysis of biomedical time series plays an essential role in clinical management and basic investigation. However, conventional monitors streaming data in real-time show only the most recent values, not referenced to past dynamics. We describe a chromatic approach to bring the 'memory' of the physiologic system's past behavior into the current display window.The method employs the estimated probability density function of a time series segment to colorize subsequent data points.For illustrative purposes, we selected open-access recordings of continuous: (1) fetal heart rate during the pre-partum period, and (2) heart rate and systemic blood pressure from a critical care patient during a spontaneous breathing trial. The colorized outputs highlight changes from the 'baseline' reference state, the latter defined as the mode value assumed by the signal, i.e. the maximum of its probability density function.A colorization method may facilitate the recognition of relevant features of time series, especially shifts in baseline dynamics and other trends (including transient and longer-term deviation from baseline values) which may not be as readily noticed using traditional displays. This method may be applicable in clinical monitoring (real-time or off-line) and in research settings. Prospective studies are needed to assess the utility of this approach.

摘要

生物医学时间序列分析在临床管理和基础研究中起着至关重要的作用。然而,传统的实时流式数据监测器仅显示最近的值,而未参考过去的动态变化。我们描述了一种彩色化方法,将生理系统过去行为的“记忆”引入当前显示窗口。该方法利用时间序列段的估计概率密度函数为后续数据点着色。为了说明目的,我们选择了公开获取的连续记录:(1)产前胎儿心率,以及(2)一名重症监护患者在自主呼吸试验期间的心率和全身血压。彩色化输出突出了与“基线”参考状态的变化,后者定义为信号假定的众数,即其概率密度函数的最大值。一种彩色化方法可能有助于识别时间序列的相关特征,特别是基线动态变化和其他趋势(包括与基线值的短期和长期偏差),而使用传统显示可能不太容易注意到这些。该方法可能适用于临床监测(实时或离线)以及研究环境。需要进行前瞻性研究来评估这种方法的效用。

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