Name
Title | ||
|---|---|---|
Epileptic Seizure Detection for Multichannel EEG Signals with Support Vector Machines |
Abstract | ||
|---|---|---|
Epilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. The electroencephalogram (EEG) signals play an important role in the diagnosis of epilepsy. In addition, multi-channel EEG signals have much more discrimination information than a single channel. However, traditional recognition algorithms of EEG signals are lack of multichannel EEG signals. In this paper, we propose a new method of epileptic seizure detection based on multichannel EEG signals. Both unipolar and bipolar EEG signals are considered in our approach. We make use of approximate entropy (ApEn) and statistic values to extract features. Furthermore, we tested the performance of four different Support Vector Machines (SVMs). The results reveal that the grid SVM achieves the highest totally classification accuracy (98.91%). |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/BIBE.2011.13 | BIBE |
Keywords | Field | DocType |
multi-channel eeg signal,recognition algorithms,medical disorders,common chronic neurological disorder,medical signal detection,multichannel eeg signals,neurophysiology,eeg signal,bipolar eeg signal,grid computing,electroencephalogram,statistical analysis,electroencephalography,recurrent unprovoked seizures,multichannel eeg signal,medical signal processing,different support,grid method,chronic neurological disorder,electroencephalogram signals,epileptic seizure detection,feature extraction,vector machines,signal classification,statistic values,approximate entropy,classification accuracy,entropy,discrimination information,support vector machines,unipolar eeg signal,accuracy,bioinformatics,support vector machine | Approximate entropy,Computer science,Epilepsy,Epileptic seizure,Artificial intelligence,Recognition algorithm,Electroencephalography,Pattern recognition,Neurophysiology,Support vector machine,Speech recognition,Feature extraction,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-61284-975-1 | 6 | 0.72 |
References | Authors | |
7 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chia-ping Shen | 1 | 43 | 6.07 |
| Chih-Min Chan | 2 | 77 | 1.29 |
| Feng-Sheng Lin | 3 | 13 | 2.03 |
| Ming-Jang Chiu | 4 | 82 | 8.56 |
| Jeng-Wei Lin | 5 | 35 | 7.52 |
| Jui-Hung Kao | 6 | 6 | 1.39 |
| Charlie Chung-Ping Chen | 7 | 1396 | 137.82 |
| Feipei Lai | 8 | 846 | 81.35 |