Wavelet-approximated generalized matched filter for the detection of multisensor extracellular action potentials

Author

Szymanska, Agnieszka A. ; Nenadic, Zoran

Author_Institution

Dept. of Biomed. Eng., Univ. of California, Irvine, Irvine, CA, USA

fYear

2013

fDate

6-8 Nov. 2013

Firstpage

810

Lastpage

814

Abstract

Signal detection represents the first processing step in the analysis of extracellular action potentials (EAPs). By combining the theory of wavelets with statistical signal detection, we derived an approximation of the generalized matched filter that is suitable for semi-supervised (noise known, signal unknown) detection of EAPs in multisensor recordings. When tested on experimental data recorded by a 4-sensor electrode (tetrode), the filter yielded significant signal-to-noise ratio improvements with respect to the original data and several popular multivariate signal processing methods.

Keywords

filtering theory; matched filters; medical signal detection; sensor fusion; statistical analysis; wavelet transforms; 4-sensor electrode; EAP; multisensor extracellular action potential detection; multisensor recordings; multivariate signal processing methods; semisupervised detection; signal-to-noise ratio; statistical signal detection; tetrode; wavelet-approximated generalized matched filter; Continuous wavelet transforms; Covariance matrices; Neurons; Sensors; Signal to noise ratio; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on

Conference_Location

San Diego, CA

ISSN

1948-3546

Type

conf

DOI

10.1109/NER.2013.6696058

Filename

6696058