EEG tabanli Epilepsi Nöbet Belirlemesine bir Derin Ögrenme Yaklasimi

Translated title of the contribution: A deep learning approach to EEG based epilepsy seizure determination

M. Husrev Cilasun, Hulya Yalcin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Citations (Scopus)

Abstract

Epilepsy is a common chronic neurological disorder. Epilepsy seizures are the result of the transient and unexpected electrical disturbance of the brain. About fifty million people worldwide have epilepsy, and nearly two out of every three new cases are discovered in developing countries. The detection of epilepsy is possible by analyzing EEG signals. Many researchers have been working on developing a variety of methods for the analysis the EEG signal. In this work, a deep convolutional neural network approach is implemented to detect epilepsy seizure based on EEG signals. Our approach outperforms the previous work used in the analysis of EEG signals, since it eliminates the need for application of preprocessing and dimensionality reduction steps on the data. Experimental results suggest that deep learning networks stand out as a promising approach for neurological diagnosis on EEG data.

Translated title of the contributionA deep learning approach to EEG based epilepsy seizure determination
Original languageTurkish
Title of host publication2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1573-1576
Number of pages4
ISBN (Electronic)9781509016792
DOIs
Publication statusPublished - 20 Jun 2016
Event24th Signal Processing and Communication Application Conference, SIU 2016 - Zonguldak, Turkey
Duration: 16 May 201619 May 2016

Publication series

Name2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings

Conference

Conference24th Signal Processing and Communication Application Conference, SIU 2016
Country/TerritoryTurkey
CityZonguldak
Period16/05/1619/05/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

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