Name
Abstract | ||
|---|---|---|
Electroencephalogram (EEG) has established itself as an important means of identifying and analyzing epileptic seizure activity in humans. In most cases, identification of the epileptic EEG signal is done manually by skilled professionals, who are small in number. In this paper, we try to automate the detection process. We use wavelet transform for feature extraction and obtain statistical parameters from the decomposed wavelet co-efficients. A feed-forward backpropagating artificial neural network (ANN) is used for the classification. We use genetic algorithm for choosing the training set and also implement a post-classification stage using harmonic weights to increase the accuracy. Average specificity of 99.19%, sensitivity of 91.29% and selectivity of 91.14% are obtained. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.cmpb.2008.02.005 | Computer Methods and Programs in Biomedicine |
Keywords | Field | DocType |
Electroencephalogram (EEG),Artificial neural network (ANN),Genetic algorithm,Resilient backpropagation,Discrete wavelet transform (DWT) | Pattern recognition,Computer science,Speech recognition,Feature extraction,Epileptic seizure,Artificial intelligence,Artificial neural network,Rprop,Electroencephalography,Genetic algorithm,Wavelet transform,Wavelet | Journal |
Volume | Issue | ISSN |
91 | 2 | 0169-2607 |
Citations | PageRank | References |
27 | 1.57 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| L M Patnaik | 1 | 1617 | 198.06 |
| Ohil K. Manyam | 2 | 38 | 3.87 |