Modeling Spectral Properties in Stationary Processes of Varying Dimensions with Applications to Brain Local Field Potential Signals.
Modeling Spectral Properties in Stationary Processes of Varying Dimensions with Applications to Brain Local Field Potential Signals.
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DOI：
10.3390/e22121375
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发表时间：
2020-12-05
期刊：
影响因子：
--
通讯作者：
Ombao H
中科院分区：
文献类型：
--
作者：
Sundararajan RR;Frostig R;Ombao H
在某些应用中，比较两个不等维的多元时间序列的随机性质是很重要的。提出了一种比较两种不同维数的多元平稳过程中光谱信息扩散的新方法。为了测量差异，提出了频率比谱比统计量，并推导了其渐近性质。FS-ratio不受平稳过程尺寸的影响，捕获了各频段频谱功率的比例。在这里，我们开发了一种技术来自动识别携带显著频谱功率的频段。我们应用我们的方法来跟踪32通道局部场电位（LFP）信号在实验性诱导中风后的复杂性变化。在每个历元（与实验持续时间不同的时间段），将非平稳LFP信号分解为平稳和非平稳潜在源，并通过这些潜在平稳源及其跨历元变化的维数来分析复杂性。分析表明，在Beta频段（12-30赫兹）的频谱信息显示出最大的结构变化和复杂性。
In some applications, it is important to compare the stochastic properties of two multivariate time series that have unequal dimensions. A new method is proposed to compare the spread of spectral information in two multivariate stationary processes with different dimensions. To measure discrepancies, a frequency specific spectral ratio (FS-ratio) statistic is proposed and its asymptotic properties are derived. The FS-ratio is blind to the dimension of the stationary process and captures the proportion of spectral power in various frequency bands. Here we develop a technique to automatically identify frequency bands that carry significant spectral power. We apply our method to track changes in the complexity of a 32-channel local field potential (LFP) signal from a rat following an experimentally induced stroke. At every epoch (a distinct time segment from the duration of the experiment), the nonstationary LFP signal is decomposed into stationary and nonstationary latent sources and the complexity is analyzed through these latent stationary sources and their dimensions that can change across epochs. The analysis indicates that spectral information in the Beta frequency band (12–30 Hertz) demonstrated the greatest change in structure and complexity due to the stroke.
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DOI：
10.1214/17-aoas1119
发表时间：
2018-09
期刊：
The annals of applied statistics
影响因子：
--
作者：
Wang Y;Ombao H;Chung MK
通讯作者：
Chung MK
DOI：
10.1111/rssc.12169
发表时间：
2017-04-01
影响因子：
1.6
作者：
Cribben, Ivor;Yu, Yi
通讯作者：
Yu, Yi
影响因子：
0.9
作者：
Dwivedi, Yogesh;Rao, Suhasini Subba
通讯作者：
Rao, Suhasini Subba
影响因子：
1.9
作者：
Fontaine, Charles;Frostig, Ron D.;Ombao, Hernando
通讯作者：
Ombao, Hernando
影响因子：
0.9
作者：
Sundararajan, Raanju Ragavendar;Pourahmadi, Mohsen
通讯作者：
Pourahmadi, Mohsen