The nature of statistical learning theory
The nature of statistical learning theory
Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features
Journal of Medical Systems
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
Cross-correlation aided support vector machine classifier for classification of EEG signals
Expert Systems with Applications: An International Journal
Classification of electroencephalogram signals with combined time and frequency features
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Simple and practical algorithm for sparse Fourier transform
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Nearly optimal sparse fourier transform
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
Hi-index | 12.05 |
Epilepsy is one of the most common neurological disorders with 0.8% of the world population. The epilepsy is unpredictable and recurrent, so it is very difficult to treat. In this paper, we propose a new Electroencephalography (EEG) seizure detection method by using the dual-tree complex wavelet (DTCWT) - Fourier features. These features achieve perfect classification rates (100%) for the EEG database from the University of Bonn. These classification rates outperform a number of existing EEG seizure detection methods published in the literature. However, it should be mentioned that several recent works also achieved this perfect classification rate (100%). Our proposed method should be as good as these works since our method only performs the DTCWT transform for up to 5 scales and our method only conducts the FFT to the 4th and 5th scales of the DTCWT decomposition. In addition, we could replace the conventional FFT in our method by sparse FFT so that our method could be even faster.