Epileptic Electroencephalogram Signal Classification Based on Shearlet and Contourlet Transforms

According to the World Health Organization (2023), epilepsy is a chronic brain disorder that affects around 50 million people of all ages worldwide, mainly (approximately 80%) those living in low- and middle-income regions. The most common method used to diagnose epilepsy is Electroencephalography (...

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Bibliographic Details
Main Authors: Amorim, Paulo H. J., Moraes, Thiago F., Da Silva, Jorge V. L., Pedrini, Helio
Format: Conference Proceeding
Language:English
Subjects:
contourlets
convolutional neural networks
electroencephalogram signals
Electroencephalography
Epilepsy
Feature extraction
Organizations
Pattern classification
shear-lets
Time-frequency analysis
Transforms
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Summary:According to the World Health Organization (2023), epilepsy is a chronic brain disorder that affects around 50 million people of all ages worldwide, mainly (approximately 80%) those living in low- and middle-income regions. The most common method used to diagnose epilepsy is Electroencephalography (EEG), which involves monitoring brain activity. However, an-alyzing EEG recordings to detect epileptic seizures can be a tiresome and time-consuming task, even for experts. In addition, different physicians may have varying levels of experience, leading to differing diagnostic opinions. As the main contribution of this work, we propose and evaluate a novel method that combines time-frequency transforms and 1D convolutional neural networks to extract and process EEG features. Experiments performed on a widely used public data set show that our approach is competitive when compared to other EEG signal classification methods.
ISSN:1946-0759
DOI:10.1109/ICMLA58977.2023.00115