Estimation of confidence limits for descriptive indexes derived from autoregressive analysis of time series: Methods and application to heart rate variability.
Estimation of confidence limits for descriptive indexes derived from autoregressive analysis of time series: Methods and application to heart rate variability.
复制标题
DOI：
10.1371/journal.pone.0183230
复制
发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Faes L
中科院分区：
文献类型：
--
作者：
Beda A;Simpson DM;Faes L
对个性化医疗日益增长的兴趣需要从生理信号的个体记录估计的描述性指数进行推断，统计分析集中在受试者之间/受试者内的个体差异，而不是比较假定的同质队列。为此，需要计算描述性指标的个体估计值的置信限的方法。本研究引入数值方法来计算这样的置信限，并进行统计比较的指标来自个体时间序列的自回归（AR）建模。分析方法通常是不可行的，因为指数通常是AR参数的非线性函数。我们利用蒙特卡罗（MC）和Bootstrap（BS）方法来重现AR参数的抽样分布和从中计算的指数。在这里，这些方法实现的频谱和信息理论指标的心率变异性（HRV）估计的心脏周期时间序列的AR模型。首先，MS和BC方法在广泛的合成HRV时间序列中进行了测试，显示出与金标准方法（即驱动模拟的“真实”过程的多个实现）的良好一致性。然后，考虑从执行认知任务的志愿者中测量的真实的心率变异性时间序列，记录（i）不同记录的置信限宽度的强烈变异性，（ii）个人对同一任务反应的多样性，以及（iii）群体平均反应与许多人的反应之间经常出现不一致。我们的结论是MC和BS方法是强大的估计这些AR为基础的指标的置信限，因此建议短期HRV分析。此外，基于AR的指数显示的任务反应的强烈个体间差异证明需要对HRV特征进行逐个评估。鉴于它们的一般性，MC和BS方法是有前途的应用在生物医学信号处理和超越，提供了一个强大的新工具，用于评估从个人记录估计的指数的置信限。
The growing interest in personalized medicine requires making inferences from descriptive indexes estimated from individual recordings of physiological signals, with statistical analyses focused on individual differences between/within subjects, rather than comparing supposedly homogeneous cohorts. To this end, methods to compute confidence limits of individual estimates of descriptive indexes are needed. This study introduces numerical methods to compute such confidence limits and perform statistical comparisons between indexes derived from autoregressive (AR) modeling of individual time series. Analytical approaches are generally not viable, because the indexes are usually nonlinear functions of the AR parameters. We exploit Monte Carlo (MC) and Bootstrap (BS) methods to reproduce the sampling distribution of the AR parameters and indexes computed from them. Here, these methods are implemented for spectral and information-theoretic indexes of heart-rate variability (HRV) estimated from AR models of heart-period time series. First, the MS and BC methods are tested in a wide range of synthetic HRV time series, showing good agreement with a gold-standard approach (i.e. multiple realizations of the "true" process driving the simulation). Then, real HRV time series measured from volunteers performing cognitive tasks are considered, documenting (i) the strong variability of confidence limits' width across recordings, (ii) the diversity of individual responses to the same task, and (iii) frequent disagreement between the cohort-average response and that of many individuals. We conclude that MC and BS methods are robust in estimating confidence limits of these AR-based indexes and thus recommended for short-term HRV analysis. Moreover, the strong inter-individual differences in the response to tasks shown by AR-based indexes evidence the need of individual-by-individual assessments of HRV features. Given their generality, MC and BS methods are promising for applications in biomedical signal processing and beyond, providing a powerful new tool for assessing the confidence limits of indexes estimated from individual recordings.
登录
查看更多内容
影响因子：
1.9
作者：
BUCKLAND, ST
通讯作者：
BUCKLAND, ST
影响因子：
7.7
作者：
Jáuregui-Renaud, K;Hermosillo, AG;Cárdenas, M
通讯作者：
Cárdenas, M
影响因子：
2.9
作者：
SLOAN, RP;KORTEN, JB;MYERS, MM
通讯作者：
MYERS, MM
DOI：
10.1016/s0735-1097(00)00595-7
发表时间：
2000-05-01
影响因子：
24
作者：
Bernardi, L;Wdowczyk-Szulc, J;Sleight, P
通讯作者：
Sleight, P
影响因子：
3.7
作者：
Beda, Alessandro;Jandre, Frederico C.;Simpson, David M.
通讯作者：
Simpson, David M.