Adaptive modeling and spectral estimation of nonstationary biomedical signals based on Kalman filtering
Adaptive modeling and spectral estimation of nonstationary biomedical signals based on Kalman filtering
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DOI：
10.1109/tbme.2005.851465
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发表时间：
2005-08-01
影响因子：
4.6
通讯作者：
Goldstein, B
中科院分区：
文献类型：
--
作者：
Aboy, M;Márquez, OW;Goldstein, B
提出了一种估计非平稳信号瞬时功率谱密度（PSD）的算法。该算法基于双卡尔曼滤波，自适应地在每个时刻产生自回归模型参数的估计。该算法在非平稳信号中表现出比经典非参数方法更好的PSD跟踪性能，并且不假设数据的局部平稳。此外，它提供了更好的时频分辨率，并且对模型不匹配具有鲁棒性。我们通过一个涉及创伤性脑损伤（TBI）患者颅内压信号（ICP）的PSD估计的样本应用来证明其有效性。
We describe an algorithm to estimate the instantaneous power spectral density (PSD) of nonstationary signals. The algorithm is based on a dual Kalman filter that adaptively generates an estimate of the autoregressive model parameters at each time instant. The algorithm exhibits superior PSD tracking performance in nonstationary signals than classical nonparametric methodologies, and does not assume local stationarity of the data. Furthermore, it provides better time-frequency resolution, and is robust to model mismatches. We demonstrate its usefulness by a sample application involving PSD estimation of intracranial pressure signals (ICP) from patients with traumatic brain injury (TBI).