Name
Title | ||
|---|---|---|
Adaptive multi-parent crossover GA for feature optimization in epileptic seizure identification. |
Abstract | ||
|---|---|---|
EEG signal analysis involves multi-frequency non-stationary brain waves from multiple channels. Segmenting these signals, extracting features to obtain the important properties of the signal and classification are key aspects of detecting epileptic seizures. Despite the introduction of several techniques, it is very challenging when multiple EEG channels are involved. When many channels exist, a spatial filter is required to eliminate noise and extract relevant information. This adds a new dimension of complexity to the frequency feature space. In order to stabilize the classifier of the channels, feature selection is very important. Furthermore, and to improve the performance of a classifier, more data is required from EEG channels for complex problems. The increase of such data poses some challenges as it becomes difficult to identify the subject dependent bands when the channels increase. Hence, an automated process is required for such identification. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.asoc.2018.11.012 | Applied Soft Computing |
Keywords | Field | DocType |
Genetic Algorithms,Adaptive multi-crossover,Feature extraction,Epileptic seizures,EEG identification | Feature vector,Crossover,Feature selection,Pattern recognition,Support vector machine,Curse of dimensionality,Artificial intelligence,Classifier (linguistics),Optimization problem,Machine learning,Mathematics,Genetic algorithm | Journal |
Volume | ISSN | Citations |
75 | 1568-4946 | 3 |
PageRank | References | Authors |
0.46 | 13 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Salah al-Sharhan | 1 | 106 | 13.21 |
| Andrew Thomas Bimba | 2 | 3 | 0.80 |