Adaptive Bayesian Spectral Analysis of Nonstationary Biomedical Time Series
Adaptive Bayesian Spectral Analysis of Nonstationary Biomedical Time Series
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发表时间：
2016-09
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通讯作者：
S. Bruce;M. Hall;D. Buysse;R. Krafty
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作者：
S. Bruce;M. Hall;D. Buysse;R. Krafty
许多生物医学时间序列信号的研究旨在测量时间序列的频域特性与临床和行为协变量之间的关联。然而，由于缺乏可以评估关系随时间变化的性质的方法，这些关联的随时间变化的动态在很大程度上被忽略。本文介绍了一种同时自动分析时变功率谱和协变量之间关联的方法。该过程自适应地将时间网格和协变量值划分为未知数量的近似静止块，并通过惩罚样条非参数估计块内的局部谱。该方法是在完全贝叶斯框架中制定的，其中划分点的数量和位置是随机的，并使用可逆跳跃马尔可夫链蒙特卡罗技术进行拟合。对分区分布进行平均的估计和推断可以准确分析平滑和突变的光谱。在一项针对老年人作为其患病配偶的主要照顾者的研究中，所提出的方法用于分析心率变异性随时间变化的频谱与自我报告的睡眠质量之间的关联。
Many studies of biomedical time series signals aim to measure the association between frequency-domain properties of time series and clinical and behavioral covariates. However, the time-varying dynamics of these associations are largely ignored due to a lack of methods that can assess the changing nature of the relationship through time. This article introduces a method for the simultaneous and automatic analysis of the association between the time-varying power spectrum and covariates. The procedure adaptively partitions the grid of time and covariate values into an unknown number of approximately stationary blocks and nonparametrically estimates local spectra within blocks through penalized splines. The approach is formulated in a fully Bayesian framework, in which the number and locations of partition points are random, and fit using reversible jump Markov chain Monte Carlo techniques. Estimation and inference averaged over the distribution of partitions allows for the accurate analysis of spectra with both smooth and abrupt changes. The proposed methodology is used to analyze the association between the time-varying spectrum of heart rate variability and self-reported sleep quality in a study of older adults serving as the primary caregiver for their ill spouse.