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An Attention-Based Wavelet Convolution Neural Network for Epilepsy EEG Classification.
- Source :
- IEEE Transactions on Neural Systems & Rehabilitation Engineering; 2022, Vol. 30, p957-966, 10p
- Publication Year :
- 2022
-
Abstract
- As a kind of non-invasive, low-cost, and readily available brain examination, EEG has attached significance to the means of clinical diagnosis of epilepsy. However, the reading of long-term EEG records has brought a heavy burden to neurologists and experts. Therefore, automatic EEG classification for epileptic patients plays an essential role in epilepsy diagnosis and treatment. This paper proposes an Attention Mechanism-based Wavelet Convolution Neural Network for epilepsy EEG classification. Attention Mechanism-based Wavelet Convolution Neural Network firstly uses multi-scale wavelet analysis to decompose the input EEGs to obtain their components in different frequency bands. Then, these decomposed multi-scale EEGs are input into the Convolution Neural Network with an attention mechanism for further feature extraction and classification. The proposed algorithm achieves 98.89% triple classification accuracy on the Bonn EEG database and 99.70% binary classification accuracy on the Bern-Barcelona EEG database. Our experiments prove that the proposed algorithm achieves a state-of-the-art classification effect on epilepsy EEG. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15344320
- Volume :
- 30
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Neural Systems & Rehabilitation Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 170416068
- Full Text :
- https://doi.org/10.1109/TNSRE.2022.3166181