Conference proceeding
Detection of seizure onset using wavelet analysis
Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, v 2, pp 1220-1221
1994
Abstract
The spectrum of the normal electroencephalogram (EEG) follows an inverse power law attenuation over a band of clinically relevant frequencies. This suggests that EEG exhibits self-similar fluctuations over a multiplicity of scales, hence, can be characterized by measures which capture the scale-invariant nature of the signal. Here, the authors investigate the use of the discrete wavelet transform as a multiscale decomposition tool to monitor the statistical scale invariant properties of the EEG in long-term monitoring aimed to localize epileptic foci. The objective is to detect the onset of seizure marked by the loss of scale-invariance.
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Details
- Title
- Detection of seizure onset using wavelet analysis
- Creators
- S Mehta - Drexel UniversityB OnaralR Koser
- Publication Details
- Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, v 2, pp 1220-1221
- Conference
- 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 16th
- Publisher
- IEEE
- Number of pages
- 1
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:A1994BC56Q00610
- Other Identifier
- 991019182661604721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Engineering, Biomedical