Name
Title | ||
|---|---|---|
Epileptic Seizure Detection With Eeg Textural Features And Imbalanced Classification Based On Easyensemble Learning |
Abstract | ||
|---|---|---|
Imbalance data classification is a challenging task in automatic seizure detection from electroencephalogram (EEG) recordings when the durations of non-seizure periods are much longer than those of seizure activities. An imbalanced learning model is proposed in this paper to improve the identification of seizure events in long-term EEG signals. To better represent the underlying microstructure distributions of EEG signals while preserving the non-stationary nature, discrete wavelet transform (DWI') and uniform 1D-LBP feature extraction procedure are introduced. A learning framework is then designed by the ensemble of weakly trained support vector machines (SVMs). Under-sampling is employed to split the imbalanced seizure and non-seizure samples into multiple balanced subsets where each of them is utilized to train an individual SVM classifier. The weak SVMs are incorporated to build a strong classifier which emphasizes seizure samples and in the meantime analyzing the unbalanced class distribution of EEG data. Final seizure detection results are obtained in a multi-level decision fusion process by considering temporal and frequency factors. The model was validated over two long-term and one short-term public EEG databases. The model achieved a G-mean of 97.14% with respect to epoch-level assessment, an event-level sensitivity of 96.67%, and a false detection rate of 0.86/h on the long-term intracranial database. An epoch-level G-mean of 95.28% and event-level false detection rate of 0.81/h were yielded over the long-term scalp database. The comparisons with 14 published methods demonstrated the improved detection performance for imbalanced EEG signals and the generalizability of the proposed model. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1142/S0129065719500217 | INTERNATIONAL JOURNAL OF NEURAL SYSTEMS |
Keywords | Field | DocType |
Seizure detection, imbalanced classification, EasyEnsemble learning, textural feature, local binary pattern | Seizure detection,Pattern recognition,Computer science,Local binary patterns,Epileptic seizure,Artificial intelligence,Data classification,Electroencephalography | Journal |
Volume | Issue | ISSN |
29 | 10 | 0129-0657 |
Citations | PageRank | References |
1 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chengfa Sun | 1 | 1 | 0.34 |
| Hui Cui | 2 | 27 | 7.96 |
| Weidong Zhou | 3 | 17 | 3.21 |
| Weiwei Nie | 4 | 1 | 0.34 |
| Xiuying Wang | 5 | 4 | 2.80 |
| Qi Yuan | 6 | 85 | 9.61 |