Abstract
Dynamics of the spike-wave paroxysms in Childhood Absence Epilepsy (CAE) are automatically characterized using novel approaches. Features are extracted from scalograms formed by Continuous Wavelet Transform (CWT). Detection algorithms are designed to identify an estimate of the temporal development of frequencies in the paroxysms. A database of 106 paroxysms from 26 patients was analyzed. The database is large compared to other known studies in the field of dynamics in CAE. CWT is more efficient than the widely used Fourier transform due to CWTs ability to recognize smaller discontinuities and variations. The use of scalograms and the detection algorithms result in a potentially usable clinical tool for dividing CAE patients into subsets. Differences between the grouped paroxysms may turn out to be useful from a clinical perspective as a prognostic indicator or when adjusting drug treatment.
| Original language | English |
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| Title of host publication | 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 |
| Number of pages | 4 |
| Publisher | IEEE |
| Publication date | 2013 |
| Pages | 4283-4286 |
| Article number | FrD01.23 |
| DOIs | |
| Publication status | Published - 2013 |
| Event | Annual International Conference of the IEEE 2013: Engineering in Medicine and Biology Society (EMBC) - Osaka, Japan Duration: 3 Jul 2013 → 7 Jul 2013 Conference number: 35 |
Conference
| Conference | Annual International Conference of the IEEE 2013 |
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| Number | 35 |
| Country/Territory | Japan |
| City | Osaka |
| Period | 03/07/2013 → 07/07/2013 |