Name
Title | ||
|---|---|---|
Ranking features of wavelet-decomposed EEG based on significance in epileptic seizure prediction |
Abstract | ||
|---|---|---|
A method for ranking features of wavelet-decomposed EEG in order of importance in prediction of epileptic seizures is introduced. Using this method, the four most important features (extracted from each level of wavelet decomposition) are selected from ten features. The proposed set of features is then used to recognize “pre-seizure” signal, thus predicting a seizure. Our feature set outperforms previously used sets by achieving higher class separability index and correct classification rate. |
Year | Venue | Keywords |
|---|---|---|
2006 | EUSIPCO | electroencephalography,medical signal processing,signal classification,wavelet transforms,correct classification rate,epileptic seizure prediction,higher class separability index,preseizure signal,ranking features,wavelet-decomposed eeg |
Field | DocType | ISSN |
Wavelet decomposition,Ranking,Pattern recognition,Computer science,Epileptic seizure,Feature set,Artificial intelligence,Class separability,Classification rate,Electroencephalography,Wavelet | Conference | 2219-5491 |
Citations | PageRank | References |
3 | 0.38 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pedram Ataee | 1 | 3 | 0.38 |
| Alireza Nasiri Avanaki | 2 | 48 | 8.82 |
| Hadi Fatemi Shariatpanahi | 3 | 3 | 0.38 |
| Seyed Mohammadreza Khoee | 4 | 3 | 0.38 |