一种更平滑的状态空间多窗谱图。
A Smoother State Space Multitaper Spectrogram.
作者信息
Song Andrew H, Chakravarty Sourish, Brown Emery N
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:33-36. doi: 10.1109/EMBC.2018.8512190.
A recent work (Kim et al. 2018) has reported a novel statistical modeling framework, the State-Space Multitaper (SSMT) method, to estimate time-varying spectral representation of non-stationary time series data. It combines the strengths of the multitaper spectral (MT) analysis paradigm with that of state-space (SS) models. In this current work, we explore a variant of the original SSMT framework by imposing a smoothness promoting SS model to generate smoother estimates of power spectral densities for non-stationary data. Specifically, we assume that the continuous processes giving rise to observations in the frequencies of interest follow multiple independent Integrated Wiener Processes (IWP). We use both synthetic data and electroencephalography (EEG) data collected from a human subject under anesthesia to compare the IWP- SSMT with the SSMT method and demonstrate the former's utility in yielding smoother descriptions of underlying processes. The original SSMT and IWP-SSMT can co-exist as a part of a model selection toolkit for nonstationary time series data.
最近的一项研究（Kim等人，2018年）报告了一种新颖的统计建模框架——状态空间多窗谱（SSMT）方法，用于估计非平稳时间序列数据的时变谱表示。它结合了多窗谱（MT）分析范式和状态空间（SS）模型的优势。在当前这项研究中，我们通过采用一个促进平滑性的状态空间模型来探索原始SSMT框架的一个变体，以便为非平稳数据生成更平滑的功率谱密度估计。具体而言，我们假设在感兴趣频率上产生观测值的连续过程遵循多个独立的积分维纳过程（IWP）。我们使用合成数据以及从一名处于麻醉状态的人类受试者身上收集的脑电图（EEG）数据，将IWP - SSMT与SSMT方法进行比较，并证明前者在更平滑地描述潜在过程方面的效用。原始的SSMT和IWP - SSMT可以作为非平稳时间序列数据模型选择工具包的一部分共存。
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