EEG spike detection using backpropagation networks

Russell C. Eberhart, Roy W. Dobbins, W. Robert S. Webber

Research output: Contribution to conferencePaperpeer-review

Abstract

Summary form only given, as follows. The design of a system to analyze electroencephalogram (EEG) signals for the detection of epileptiform spikes is described. The ultimate goal is real-time multichannel spike detection. Two main areas of development are reviewed. The first is the processing and characterization of the raw EEG data, including issues related to data rates, the number of data channels, and the tradeoffs between the amount of data preprocessing and the complexities of the neural network required. The second is the selection and implementation of the neural network architecture, including choices between supervised and unsupervised learning schemes, and among the many available learning algorithms for each network architecture. Interim results involving the analysis of single-channel EEG data are discussed. The relationship of the spike detection project to a similar effort in seizure detection is described.

Original languageEnglish (US)
Number of pages1
StatePublished - Dec 1 1989
EventIJCNN International Joint Conference on Neural Networks - Washington, DC, USA
Duration: Jun 18 1989Jun 22 1989

Other

OtherIJCNN International Joint Conference on Neural Networks
CityWashington, DC, USA
Period6/18/896/22/89

ASJC Scopus subject areas

  • Engineering(all)

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