Time-frequency component analysis of somatosensory evoked potentials in rats.
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.


CONCLUSIONS
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.
Zhang
Zhi-Guo
ZG
Department of Orthopaedics and Traumatology, The University of Hong Kong, Pokfulam, Hong Kong, PR China. zgzhang@eee.hku.hk
Yang
Jun-Lin
JL
Chan
Shing-Chow
SC
Luk
Keith Dip-Kei
KD
Hu
Yong
Y
D000465:Algorithms; D000818:Animals; D001931:Brain Mapping / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005073:Evoked Potentials, Somatosensory / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D051381:Rats; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013003:Somatosensory Cortex / Q000502:physiology*