Name
Abstract | ||
|---|---|---|
The automatic identification of epileptic electroencephalogram (EEG) signals can give assistance to doctors in diagnosis of epilepsy, and provide the higher security and quality of life for people with epilepsy. Feature extraction of EEG signals determines the performance of the whole recognition system. In this paper, a novel method using the local binary pattern (LBP) based on the wavelet transform (WT) is proposed to characterize the behavior of EEG activities. First, the WT is employed for time-frequency decomposition of EEG signals. After that, the "uniform" LBP operator is carried out on the wavelet-based time-frequency representation. And the generated histogram is regarded as EEG feature vector for the quantification of the textural information of its wavelet coefficients. The LBP features coupled with the support vector machine (SVM) classifier can yield the satisfactory recognition accuracies of 98.88% for interictal and ictal EEG classification and 98.92% for normal, interictal and ictal EEG classification on the publicly available EEG dataset. Moreover, the numerical results on another large size EEG dataset demonstrate that the proposed method can also effectively detect seizure events from multi-channel raw EEG data. Compared with the standard LBP, the "uniform" LBP can obtain the much shorter histogram which greatly reduces the computational burden of classification and enables it to detect ictal EEG signals in real time. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1142/S0129065718500107 | INTERNATIONAL JOURNAL OF NEURAL SYSTEMS |
Keywords | Field | DocType |
Epileptic EEG, local binary pattern, wavelet, textural information | Pattern recognition,Computer science,Local binary patterns,Epilepsy,Artificial intelligence,Operator (computer programming),Electroencephalography,Wavelet | Journal |
Volume | Issue | ISSN |
28 | 8 | 0129-0657 |
Citations | PageRank | References |
3 | 0.38 | 52 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qi Yuan | 1 | 85 | 9.61 |
| Weidong Zhou | 2 | 17 | 3.21 |
| Fangzhou Xu | 3 | 10 | 5.87 |
| Yan Leng | 4 | 21 | 5.51 |
| Dongmei Wei | 5 | 3 | 1.39 |