Use of wavelet transform as a preprocessor for the neural network detection of EEG spikes

Tulga Kalayci, Ozcan Ozdamar, Nurgun Erdol

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

In this study, Wavelet Transform is used to process EEG data as input to a feed forward neural network for the detection of epileptogenic transient waveforms. The compression capability of wavelet transform provided the inclusion of data before and after the spike for contextual information without increasing input size of the neural network. The network is trained for the detection of spikes and non-spikes. The results show that wavelet transform can be used to provide more relevant information for improving the detection of epileptogenic spikes for automated EEG monitoring of seizure patients.

Original languageEnglish
Title of host publicationConference Proceedings - IEEE SOUTHEASTCON
Place of PublicationPiscataway, NJ, United States
PublisherPubl by IEEE
Pages1-3
Number of pages3
ISBN (Print)078031798X
StatePublished - Jan 1 1994
EventProceedings of the IEEE SOUTHEASTCON '94 - Miami, FL, USA
Duration: Apr 10 1994Apr 13 1994

Other

OtherProceedings of the IEEE SOUTHEASTCON '94
CityMiami, FL, USA
Period4/10/944/13/94

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Kalayci, T., Ozdamar, O., & Erdol, N. (1994). Use of wavelet transform as a preprocessor for the neural network detection of EEG spikes. In Conference Proceedings - IEEE SOUTHEASTCON (pp. 1-3). Publ by IEEE.