A comparison of algorithms for detection of spikes in the electroencephalogram.

Full Abstract

Identification of the short transient waveform, called a spike, in the cortical electroencephalogram (EEG) plays an important role during diagnosis of neurological disorders such as epilepsy. It has been suggested that artificial neural networks (ANN) can be employed for spike detection in the EEG, if suitable features are provided as input to an ANN. In this paper, we explore the performance of neural network-based classifiers using features selected by algorithms suggested by four previous investigators. Of these, three algorithms model the spike by mathematical parameters and use them as features for classification while the fourth algorithm uses raw EEG to train the classifier. The objective of this paper is to examine if there is any inherent advantage to any particular set of features, subject to the condition that the same data are used for all feature selection algorithms. Our results suggest that artificial neural networks trained with features selected using any one of the above three algorithms as well as raw EEG directly fed to the ANN will yield similar results.

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Author information

Author/s: Pang, Clement C C (CC); Upton, Adrian R M (AR); Shine, Glenn (G); Kamath, Markad V (MV);

Affiliation: Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8N 3Z5, Canada.

Journal and publication information

Publication Type: Comparative Study; Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't

Journal: IEEE transactions on bio-medical engineering (IEEE Trans Biomed Eng), published in United States. (Language: eng)

Reference: 2003-Apr; vol 50 (issue 4) : pp 521-6

Dates: Created 2003/04/30; Completed 2003/05/22; Revised 2006/11/15;

PMID: 12723065, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms
Electroencephalography - classification; methods
Epilepsy - diagnosis; physiopathology
Humans

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