Selection of optimal AR spectral estimation method for EEG signals using Cramer-Rao bound
Selection of optimal AR spectral estimation method for EEG signals using Cramer-Rao bound
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DOI：
10.1016/j.compbiomed.2005.12.001
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发表时间：
2007-02
影响因子：
7.7
通讯作者：
A. Subasi
中科院分区：
文献类型：
--
作者：
A. Subasi
脑电图是评估和治疗与癫痫相关的神经生理学障碍的重要临床工具。仔细分析脑电图仪(EEG)记录可以提供宝贵的见解，并提高对导致癫痫障碍的机制的理解。脑电中癫痫样放电的检测是癫痫诊断的重要内容。采用自回归(AR)方法对30例受试者的脑电信号进行了处理，得到了脑电功率谱。用Yule-Walker法、协方差法、修正协方差法、Burg法、最小二乘法和最大似然估计法对自回归方法的参数进行了估计。然后用脑电频谱分析和表征失神发作患者的3-Hz棘波和波群形式的癫痫样放电。为了获取医学信息，研究了脑电功率谱形状的变化。这些功率谱随后被用来比较所应用的方法的频率分辨率和癫痫发作的确定。给出了估计的脑电信号AR参数的Cramer-Rao界(CRB)，并利用CRB值对各种估计方法的性能进行了评估。最后，根据计算的CRB值选择最优的脑电信号AR谱估计方法。根据计算的CRB值，MLE AR方法的性能特征在脑电信号分析中非常有价值。
Electroencephalography is an essential clinical tool for the evaluation and treatment of neurophysiologic disorders related to epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important element in the diagnosis of epilepsy. In this study, EEG signals recorded from 30 subjects were processed using autoregressive (AR) method and EEG power spectra were obtained. The parameters of autoregressive method were estimated by different methods such as Yule-Walker, covariance, modified covariance, Burg, least squares, and maximum likelihood estimation (MLE). EEG spectra were then used to analyze and characterize epileptiform discharges in the form of 3-Hz spike and wave complexes in patients with absence seizures. The variations in the shape of the EEG power spectra were examined in order to obtain medical information. These power spectra were then used to compare the applied methods in terms of their frequency resolution and determination of epileptic seizure. The Cramer–Rao bounds (CRB) were derived for the estimated AR parameters of the EEG signals and the performance evaluation of the estimation methods was performed using the CRB values. Finally, the optimal AR spectral estimation method for the EEG signals was selected according to the computed CRB values. According to the computed CRB values, the performance characteristics of the MLE AR method was found extremely valuable in EEG signal analysis.