Neural Codes in Human Extracranial EEG: Identification of Epilepsy Features

Author

Valiante, Taufik ; Chiu, Alan W L ; Bardakjian, Berj L.

Author_Institution

Dept. of Surg., Toronto Univ., Ont.

fYear

2006

Firstpage

432

Lastpage

435

Abstract

Features of epilepsy from human extracranial EEG recordings were obtained using the wavelet artificial neural network (WANN). The WANN is also a robust signal processing tool for the estimation of nonlinear time-frequency relation and it had previously been shown to be able to classify and predict state transitions in the in vitro hippocampal slice model exhibiting spontaneous epilepsy. The variations in the power-frequency spectrum were analyzed. The accuracy of state classification was improved when more training data was used, the corresponding changes in synaptic weights between artificial neural units associated with more training data was studied to determine the correlations between learning in WANN and frequency information in human epilepsy

Keywords

diseases; electroencephalography; medical signal processing; neural nets; signal classification; time-frequency analysis; wavelet transforms; epilepsy features; human extracranial EEG; neural codes; nonlinear time-frequency relation; power-frequency spectrum; robust signal processing tool; state classification; synaptic weights; wavelet artificial neural network; Artificial neural networks; Electroencephalography; Epilepsy; Humans; In vitro; Robustness; Signal processing; State estimation; Time frequency analysis; Training data; artificial neural network; human extracranial recording; neural code; spontaneous seizures; wavelet transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the

Conference_Location

Shanghai

Print_ISBN

0-7803-8741-4

Type

conf

DOI

10.1109/IEMBS.2005.1616438

Filename

1616438