Name
Abstract | ||
|---|---|---|
We analyzed the spatio-temporal patterns of the depth EEG recorded from a patient with lateral temporal-lobe epilepsy. Statistical analysis based on the Jensen-Shannon entropy (JS-E) as well as linear power spectral analyses were performed. The spatio-temporal patterns from JS-E and β rhythm successfully detected the onset timing of the seizure and revealed the temporal topology of the epileptic focus. The robustness of these patterns was proved by inter-trial consistency. As the patterns are well matched with the clinical diagnosis, it could be used for the identification of onset time and focus region of epileptic seizure. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11428831_117 | International Conference on Computational Science (1) |
Keywords | Field | DocType |
onset time,clinical diagnosis,statistical analysis,jensen-shannon entropy,spatio-temporal pattern,depth eeg,epileptic seizure,epileptic focus,focus region,onset timing | Mathematical optimization,Energy analysis,Pattern recognition,Computer science,Epilepsy,Onset timing,Epileptic seizure,Clinical diagnosis,Artificial intelligence,Rhythm,Electroencephalography,Statistical analysis | Conference |
Volume | ISSN | ISBN |
3514 | 0302-9743 | 3-540-26032-3 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jung Ae Kim | 1 | 0 | 0.34 |
| Sunyoung Cho | 2 | 98 | 10.58 |
| Sang Kun Lee | 3 | 13 | 2.40 |
| Hyunwoo Nam | 4 | 13 | 3.39 |
| Seung Kee Han | 5 | 24 | 9.42 |