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 contribution | A deep learning approach to EEG based epilepsy seizure determination |
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Original language | Turkish |
Title of host publication | 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1573-1576 |
Number of pages | 4 |
ISBN (Electronic) | 9781509016792 |
DOIs | |
Publication status | Published - 20 Jun 2016 |
Event | 24th Signal Processing and Communication Application Conference, SIU 2016 - Zonguldak, Turkey Duration: 16 May 2016 → 19 May 2016 |
Publication series
Name | 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings |
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Conference
Conference | 24th Signal Processing and Communication Application Conference, SIU 2016 |
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Country/Territory | Turkey |
City | Zonguldak |
Period | 16/05/16 → 19/05/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.