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活动记录仪中的非参数方法:最新进展。

Nonparametric methods in actigraphy: An update.

作者信息

Gonçalves Bruno S B, Cavalcanti Paula R A, Tavares Gracilene R, Campos Tania F, Araujo John F

机构信息

Programa de Pós-Graduação em Psicobiologia, UFRN, Natal, RN, Brazil ; Laboratório de Neurobiologia e Ritmicidade Biológica, UFRN, Natal, RN, Brazil ; Instituto Federal Sudeste de Minas Gerais, Campus Barbacena, Barbacena, MG, Brazil.

Programa de Pós-Graduação em Fisioterapia, UFRN, Natal, RN, Brazil.

出版信息

Sleep Sci. 2014 Sep;7(3):158-64. doi: 10.1016/j.slsci.2014.09.013. Epub 2014 Sep 29.

Abstract

Circadian rhythmicity in humans has been well studied using actigraphy, a method of measuring gross motor movement. As actigraphic technology continues to evolve, it is important for data analysis to keep pace with new variables and features. Our objective is to study the behavior of two variables, interdaily stability and intradaily variability, to describe rest activity rhythm. Simulated data and actigraphy data of humans, rats, and marmosets were used in this study. We modified the method of calculation for IV and IS by modifying the time intervals of analysis. For each variable, we calculated the average value (IVm and ISm) results for each time interval. Simulated data showed that (1) synchronization analysis depends on sample size, and (2) fragmentation is independent of the amplitude of the generated noise. We were able to obtain a significant difference in the fragmentation patterns of stroke patients using an IVm variable, while the variable IV60 was not identified. Rhythmic synchronization of activity and rest was significantly higher in young than adults with Parkinson׳s when using the ISM variable; however, this difference was not seen using IS60. We propose an updated format to calculate rhythmic fragmentation, including two additional optional variables. These alternative methods of nonparametric analysis aim to more precisely detect sleep-wake cycle fragmentation and synchronization.

摘要

利用活动记录仪(一种测量总体运动的方法),人们对人类的昼夜节律进行了充分研究。随着活动记录仪技术不断发展,数据分析跟上新变量和新特征的步伐很重要。我们的目标是研究两个变量——日间稳定性和日内变异性的行为,以描述休息 - 活动节律。本研究使用了人类、大鼠和狨猴的模拟数据及活动记录仪数据。我们通过修改分析的时间间隔来改进日内变异性(IV)和日间稳定性(IS)的计算方法。对于每个变量,我们计算了每个时间间隔的平均值(IVm和ISm)结果。模拟数据表明:(1)同步分析取决于样本量,(2)片段化与所产生噪声的幅度无关。使用IVm变量时,我们能够在中风患者的片段化模式中获得显著差异,而IV60变量未被识别。使用ISM变量时,年轻人活动与休息的节律同步性显著高于帕金森病成年人;然而,使用IS60时未观察到这种差异。我们提出一种更新的格式来计算节律性片段化,包括另外两个可选变量。这些非参数分析的替代方法旨在更精确地检测睡眠 - 觉醒周期的片段化和同步性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07fc/4559593/1e5f245ada47/gr1.jpg

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