Epileptic Detection and Classification Using Convolutional Neural Network with Dual Tree Complex Wavelet Features
Main Article Content
Abstract
Epilepsy is a kind of brain disease that can be diagnosed by observation of EEG signals. Mostly it occurs within the children. However, some of the cases are observed in adults. It is a challenging task for physicians to detect this disease at an early stage. Authors in this work have classified the Epileptic and normal EEG signal by adopting the deep learning approach. For efficient features, dual tree complex wavelet (DTCWT) is considered. The decomposed wavelet features are used as the input to the convolutional neural network (CNN) classifier. Around 97% classification accuracy is observed by using the proposed approach.
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.