Picture of two heads

Open Access research that challenges the mind...

The Strathprints institutional repository is a digital archive of University of Strathclyde research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including those from the School of Psychological Sciences & Health - but also papers by researchers based within the Faculties of Science, Engineering, Humanities & Social Sciences, and from the Strathclyde Business School.

Discover more...

Representation of somatosensory evoked potentials using discrete wavelet transform

Hoppe, U. and Schnabel, K. and Weiss, S. and Rundshagen, I. (2002) Representation of somatosensory evoked potentials using discrete wavelet transform. Journal of Clinical Monitoring and Computing, 17 (3-4). pp. 227-233. ISSN 1387-1307

Full text not available in this repository. (Request a copy from the Strathclyde author)


Somatosensory evoked potentials (SEP) have been shown to be a useful tool in monitoring of the central nervous system (CNS) during anaesthesia. SEP analysis is usually performed by an experienced human operator. For automatic analysis, appropriate parameter extraction and signal representation methods are required. The aim of this work is to evaluate the discrete wavelet transform (DWT) as such a method for an SEP representation. Median nerve SEP were derived in 52 female patients, scheduled for elective surgery with SEP monitoring, under clinically proven conditions in the awake state. The discrete wavelet transform implemented as the multiresolution analysis was adopted for evaluating SEP. The suitability of the wavelet coefficients was investigated by calculating the error between the averaged response and the corresponding wavelet reconstructions. SEP can be represented by a very small number of wavelet coefficients. Although the individual SEP waveform has an influence on the number and selection of wavelet coefficients, in all subjects more than 84% of the SEP waveform energy can be represented by a set 16 wavelet coefficients. The discrete wavelet transformation provides an efficient tool for SEP representation and parameterisation. Depending on the specific problem the DWT, can be adjusted to the desired accuracy, which is important for the subsequent development of automatic SEP analysers.