State-space multitaper time-frequency analysis.
State-space multitaper time-frequency analysis.
复制标题
状态空间多音频分析。
DOI：
10.1073/pnas.1702877115
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发表时间：
2018-01-02
影响因子：
11.1
通讯作者：
Brown EN
中科院分区：
文献类型：
--
作者：
Kim SE;Behr MK;Ba D;Brown EN
传感器和记录技术的快速增长正在刺激时间序列数据的快速增长。时间序列中的非平稳和振荡结构通常是用时变谱方法分析的。这些广泛使用的技术缺乏适用于整个时间序列的统计推断框架。提出了非平稳时间序列时变谱分析的状态空间多锥(SS-MT)框架。我们将SS-MT谱图估计算法高效地在频域实现为并行的一维复数卡尔曼滤波。在对全身麻醉下记录的人体脑电的分析中，SS-MT范例提供了相对于标准多锥度方法的增强的去噪(>；10分贝)和频谱分辨率、时间序列的灵活的时间域分解以及广泛适用的经验性贝叶斯统计推断框架。时间序列是一个重要的数据类别，它包括从无线电发射、地震活动、全球定位数据和股票价格到脑电测量、生命体征和语音记录的各种记录。传感器和记录技术的快速发展增加了时间序列数据的产生，并使快速、准确的分析变得越来越重要。时间序列数据通常使用时变谱方法来描述其非平稳且经常是振荡的结构。目前的方法提供数据特征的局部估计。然而，它们没有提供适用于整个时间序列的统计推断框架。我们报告的重要进展是状态空间多锥(SS-MT)方法，它为非平稳时间序列的时变频谱分析提供了一个统计推断框架。我们将非平稳时间序列建模为定义在非重叠区间上的二阶平稳高斯过程序列。我们使用频域随机游走模型来关联跨区间的高斯过程的谱表示。SS-MT算法使用并行的一维复数卡尔曼滤波有效地计算频谱更新。期望最大化算法计算静态和动态模型参数估计。我们在模拟时间序列和接受全身麻醉的患者的脑电记录的时变频谱分析中测试了该框架。与标准多锥(MT)相比，SS-MT具有更高的谱分辨率和更低的噪声(10d B)，并允许对任意时间序列段之间的谱特性进行统计比较。SS-MT还提取信号分量的时域估计。SS-MT范式是一种广泛适用的经验性贝叶斯统计推断框架，可以帮助确保从非平稳时间序列分析中获得准确、可重复的结果。
Rapid growth in sensor and recording technologies is spurring rapid growth in time series data. Nonstationary and oscillatory structure in time series is commonly analyzed using time-varying spectral methods. These widely used techniques lack a statistical inference framework applicable to the entire time series. We develop a state-space multitaper (SS-MT) framework for time-varying spectral analysis of nonstationary time series. We efficiently implement the SS-MT spectrogram estimation algorithm in the frequency domain as parallel 1D complex Kalman filters. In analyses of human EEGs recorded under general anesthesia, the SS-MT paradigm provides enhanced denoising (>10 dB) and spectral resolution relative to standard multitaper methods, a flexible time-domain decomposition of the time series, and a broadly applicable, empirical Bayes’ framework for statistical inference. Time series are an important data class that includes recordings ranging from radio emissions, seismic activity, global positioning data, and stock prices to EEG measurements, vital signs, and voice recordings. Rapid growth in sensor and recording technologies is increasing the production of time series data and the importance of rapid, accurate analyses. Time series data are commonly analyzed using time-varying spectral methods to characterize their nonstationary and often oscillatory structure. Current methods provide local estimates of data features. However, they do not offer a statistical inference framework that applies to the entire time series. The important advances that we report are state-space multitaper (SS-MT) methods, which provide a statistical inference framework for time-varying spectral analysis of nonstationary time series. We model nonstationary time series as a sequence of second-order stationary Gaussian processes defined on nonoverlapping intervals. We use a frequency-domain random-walk model to relate the spectral representations of the Gaussian processes across intervals. The SS-MT algorithm efficiently computes spectral updates using parallel 1D complex Kalman filters. An expectation–maximization algorithm computes static and dynamic model parameter estimates. We test the framework in time-varying spectral analyses of simulated time series and EEG recordings from patients receiving general anesthesia. Relative to standard multitaper (MT), SS-MT gave enhanced spectral resolution and noise reduction (10 dB) and allowed statistical comparisons of spectral properties among arbitrary time series segments. SS-MT also extracts time-domain estimates of signal components. The SS-MT paradigm is a broadly applicable, empirical Bayes’ framework for statistical inference that can help ensure accurate, reproducible findings from nonstationary time series analyses.
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影响因子：
64.8
作者：
Haigh, Joanna D.;Winning, Ann R.;Harder, Jerald W.
通讯作者：
Harder, Jerald W.
影响因子：
4.3
作者：
BOHLIN, T
通讯作者：
BOHLIN, T
影响因子：
2.5
作者：
Daubechies, Ingrid;Lu, Jianfeng;Wu, Hau-Tieng
通讯作者：
Wu, Hau-Tieng
影响因子：
4.5
作者：
Dahlhaus, R
通讯作者：
Dahlhaus, R
影响因子：
3.7
作者：
Adak, S
通讯作者：
Adak, S