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Methods Inf Med 2002; 41(04): 331-336
DOI: 10.1055/s-0038-1634390
Original article
Schattauer GmbH

Non-Linear Transform-Based Robust Adaptive Latency Change Estimation of Evoked Potentials[*]

T. Qiu
1   Department of Electronic Engineering, Dalian University of Technology, Dalian, China
,
H. Wang
1   Department of Electronic Engineering, Dalian University of Technology, Dalian, China
,
Y. Zhang
1   Department of Electronic Engineering, Dalian University of Technology, Dalian, China
,
H. Bao
1   Department of Electronic Engineering, Dalian University of Technology, Dalian, China
› Author Affiliations
Further Information

Publication History

Received 10 July 2001

Accepted 09 January 2002

Publication Date:
07 February 2018 (online)

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Summary

Objectives: To improve the latency change estimation of evoked potentials (EP) under the lower order -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 α-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 α-stable noise conditions.

Conclusions: The new algorithm is robust under the lower order -stable noise conditions, and it also provides a better performance than the DLMS, DLMP and SDA algorithms without the need to estimate thevalue of the EP signals and noises.

Keywords

Evoked potentials - adaptive - latency - signal detection - nonlinear

* This work is supported by the National Science Foundation of China by Grant 30170259 and Grant 60172072, National 973 project by Grant 2001 CCA 00700, and the Science and Technology Foundation of the Liaoning Province of China by Grant 2001101057.


 

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