Name
Abstract | ||
|---|---|---|
In this study, we investigated three measures capable of detecting absence seizures with increased sensitivity based on different underlying assumptions. Namely, an information-based method known as Approximate Entropy, a nonlinear alternative (Order Index), and a linear variance analysis approach. The results on the long-term EEG data suggest increased accuracy in absence seizure detection achieving sensitivity as high as 97.33% with no further application of any sophisticated classification scheme. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/EMBC.2013.6611002 | EMBC |
Keywords | Field | DocType |
nonlinear alternative analysis,medical disorders,medical signal detection,information-based method,nonlinear eeg analysis,absence seizure epilepsy detection,eeg data,electroencephalography,linear eeg analysis,linear variance analysis,order index analysis,approximate entropy,entropy,indexes,time series analysis,accuracy,sensitivity | Absence seizure,Approximate entropy,Nonlinear system,Computer science,Epilepsy,Artificial intelligence,Electroencephalography,Analysis of variance,Computer vision,Pattern recognition,Classification scheme,Speech recognition,Eeg analysis | Conference |
Volume | ISSN | Citations |
2013 | 1557-170X | 6 |
PageRank | References | Authors |
0.52 | 3 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vangelis Sakkalis | 1 | 110 | 23.68 |
| Giorgos A. Giannakakis | 2 | 52 | 7.08 |
| Christina Farmaki | 3 | 6 | 0.52 |
| Abdou Mousas | 4 | 6 | 0.52 |
| Matthew Pediaditis | 5 | 7 | 1.89 |
| Pelagia Vorgia | 6 | 12 | 2.14 |
| Manolis Tsiknakis | 7 | 18 | 4.71 |