The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
■ REVIEW : Chaos Theory and Epilepsy
Abstract
Recently, interest has turned to the mathematical concept of chaos as an explanation for a variety of complex processes in nature. Chaotic systems, among other characteristics, can produce what appears to be random output. Another property of chaotic systems is that they may exhibit abrupt intermittent transitions between highly ordered and disordered states. Because of this property, it is hypothesized that epilepsy may be an example of chaos. In this review, some of the basic concepts of nonlinear dynamics and chaos are illustrated. Mathematical techniques developed to study the properties of nonlinear dynamical systems are outlined. Finally, the results of applying these techniques to the study of human epilepsy are discussed. The application of these powerful and novel mathematical techniques to analysis of the electroencephalogram has provided new insights into the epileptogenic process and may have considerable utility in the diagnosis and treatment of epilepsy. The Neuroscientist 2:118-126, 1996
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Cites
Detecting strange attractors in turbulence
Takens, F. (1981) · Lecture Notes in Mathematics · doi:10.1007/bfb0091924 · Cited by 10,459
Simple mathematical models with very complicated dynamics
May, R. M. (1976) · Nature · doi:10.1038/261459a0 · Cited by 7,001
Ott, E., Grebogi, C., & Yorke, J. A. (1990) · Physical Review Letters · doi:10.1103/physrevlett.64.1196 · Cited by 5,431
Neuronal Networks of the Hippocampus
Traub, R. D., & Miles, R. (1991) · doi:10.1017/cbo9780511895401 · Cited by 802
Cited by
Epileptic seizure prediction and control
Iasemidis, L. D. (2003) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2003.810705 · Cited by 363
Self-citationEpilepsy Classification for Mining Deeper Relationships between EEG Channels based on GCN
Chen, X., Zheng, Y., Niu, Y., & Li, C. (2020) · 2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL) · doi:10.1109/cvidl51233.2020.00050 · Cited by 34
Metadata
- Authors
- Iasemidis, L. D., & Sackellares, J. C.
- Venue
- The Neuroscientist
- Publisher
- SAGE Publications
- Published
- 1996-03
- Volume
- 2
- Issue
- 2
- Pages
- 118-126
- ISSN
- 1073-8584 (ISSN-L)
- 1089-4098
- OpenAlex
- W2081617575
- Cited by
- 175
- References
- 4
- FWCI
- 1.50
- Type
- journal article
- Language
- en
- Open access
- closed
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