A new method based on wavelet and greedy pusuit analysis for neuro-spike detection

Author

Junwei Duan ; Long Chen ; Chen, C. L. Philip

Author_Institution

Dept. of Comput. & Inf. of Sci., Univ. of Macau, Macau, China

fYear

2014

fDate

9-10 Oct. 2014

Firstpage

74

Lastpage

78

Abstract

This paper introduces a novel greedy model for spike detection system aimed to neural signal obtained from the brain such as EEG, ECOG. This Presented algorithm combines wavelet transform and simultaneous orthogonal matching pursuit (SOMP). The proposed method is capable of data compression for spike detection. The spike detection experiment demonstrates the TPR in ROC curve is up to 98.76% and the compact factor reaches 42.52 when the length of window we used is 500. Moreover, our method is simple for low power system design and real-time hardware realization.

Keywords

brain; data compression; electroencephalography; greedy algorithms; medical signal detection; wavelet neural nets; wavelet transforms; ROC curve; SOMP; TPR; brain; compact factor; data compression; greedy pusuit analysis; neural signal; neuro-spike detection system; simultaneous orthogonal matching pursuit; wavelet pusuit analysis; wavelet transform; Algorithm design and analysis; Approximation algorithms; Electroencephalography; Finite impulse response filters; Matching pursuit algorithms; Wavelet transforms; EEG; SOMP; bio-signal; spike detection; wavelet transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Informative and Cybernetics for Computational Social Systems (ICCSS), 2014 International Conference on

Conference_Location

Qingdao

Print_ISBN

978-1-4799-4753-9

Type

conf

DOI

10.1109/ICCSS.2014.6961819

Filename

6961819