Research

Time-frequency component analysis of somatosensory evoked potentials in rats

Zhi-Guo Zhang^1* , Jun-Lin Yang^2* , Shing-Chow Chan³ , Keith Dip-Kei Luk¹ and Yong Hu¹

¹  Department of Orthopaedics and Traumatology, The University of Hong Kong, Pokfulam, Hong Kong, PR China

²  Department of Orthopaedics, The 1st Affiliated Hospital of Sun Yat-Sen University, Guangzhou, PR China

³  Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong, PR China

author email corresponding author email* Contributed equally

BioMedical Engineering OnLine 2009, 8:4doi:10.1186/1475-925X-8-4

Published:	9 February 2009

Abstract

Background

Somatosensory evoked potential (SEP) signal usually contains a set of detailed temporal components measured and identified in a time domain, giving meaningful information on physiological mechanisms of the nervous system. The purpose of this study is to measure and identify detailed time-frequency components in normal SEP using time-frequency analysis (TFA) methods and to obtain their distribution pattern in the time-frequency domain.

Methods

This paper proposes to apply a high-resolution time-frequency analysis algorithm, the matching pursuit (MP), to extract detailed time-frequency components of SEP signals. The MP algorithm decomposes a SEP signal into a number of elementary time-frequency components and provides a time-frequency parameter description of the components. A clustering by estimation of the probability density function in parameter space is followed to identify stable SEP time-frequency components.

Results

Experimental results on cortical SEP signals of 28 mature rats show that a series of stable SEP time-frequency components can be identified using the MP decomposition algorithm. Based on the statistical properties of the component parameters, an approximated distribution of these components in time-frequency domain is suggested to describe the complex SEP response.

Conclusion

This study shows that there is a set of stable and minute time-frequency components in SEP signals, which are revealed by the MP decomposition and clustering. These stable SEP components have specific localizations in the time-frequency domain.