基于非线性变换的诱发电位稳健自适应潜伏期变化估计
Non-linear transform-based robust adaptive latency change estimation of evoked potentials.
作者信息
Qiu T, Wang H, Zhang Y, Bao H
机构信息
Department of Electronic Engineering, Dalian University of Technology, Dalian, China.
出版信息
Methods Inf Med. 2002;41(4):331-6.
OBJECTIVES
To improve the latency change estimation of evoked potentials (EP) under the lower order alpha-stable noise conditions by proposing and analyzing a new adaptive EP latency change detection algorithm (referred to as the NLST).
METHODS
The NLST algorithm is based on the fractional lower order moment and the nonlinear transform for the error function. The computer simulation and data analysis verify the robustness of the new algorithm.
RESULTS
The theoretical analysis shows that the iteration equation of the NLST transforms the lower order alpha-stable process en (k) into a second order moment process by a nonlinear transform. The simulations and the data analysis showed the robustness of the NLST under the lower order alpha-stable noise conditions.
CONCLUSIONS
The new algorithm is robust under the lower order alpha-stable noise conditions, and it also provides a better performance than the DLMS, DLMP and SDA algorithms without the need to estimate the alpha value of the EP signals and noises.
目的
通过提出并分析一种新的自适应诱发电位（EP）潜伏期变化检测算法（称为NLST），改善在低阶α稳定噪声条件下诱发电位潜伏期变化的估计。
方法
NLST算法基于分数低阶矩和误差函数的非线性变换。计算机模拟和数据分析验证了新算法的鲁棒性。
结果
理论分析表明，NLST的迭代方程通过非线性变换将低阶α稳定过程en(k)转换为二阶矩过程。模拟和数据分析表明NLST在低阶α稳定噪声条件下具有鲁棒性。
结论
新算法在低阶α稳定噪声条件下具有鲁棒性，并且在无需估计EP信号和噪声的α值的情况下，也比DLMS、DLMP和SDA算法具有更好的性能。
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