Name
Abstract | ||
|---|---|---|
Seizure prediction for untreatable epileptic patients, one of the major challenges of present neuroinformatics researchers, will allow a substantial improvement in their safety and quality of life. Neural networks, because of their plasticity and degrees of freedom, seem to be a good approach to consider the enormous variability of physiological systems. Several architectures and training algorithms are comparatively proposed in this work showing that it is possible to find an adequate network for one patient, but care must be taken to generalize to other patients. It is claimed that each patient will have his (her) own seizure prediction algorithms. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-87559-8_50 | ICANN (2) |
Keywords | Field | DocType |
own seizure prediction algorithm,good approach,neural network,enormous variability,seizure prediction,present neuroinformatics researcher,adequate network,physiological system,untreatable epileptic patient,major challenge,epileptic seizure prediction,towards personalized neural networks,data mining,degree of freedom,quality of life | Neuroinformatics,Computer science,Epilepsy,Epileptic seizure,Prediction algorithms,Artificial intelligence,Artificial neural network,Machine learning | Conference |
Volume | ISSN | Citations |
5164 | 0302-9743 | 3 |
PageRank | References | Authors |
0.66 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| António Dourado | 1 | 90 | 9.41 |
| Ricardo Martins | 2 | 14 | 1.90 |
| João Duarte | 3 | 67 | 5.10 |
| Bruno Direito | 4 | 41 | 6.55 |