Time dependencies in the occurrences of epileptic seizures
dc.contributor.author | Iasemidis, Leonidas D. | en_US |
dc.contributor.author | Olson, Larry D. | en_US |
dc.contributor.author | Savit, Robert S. | en_US |
dc.contributor.author | Sackellares, J. Chris | en_US |
dc.date.accessioned | 2006-04-10T18:26:20Z | |
dc.date.available | 2006-04-10T18:26:20Z | |
dc.date.issued | 1994-01 | en_US |
dc.identifier.citation | Iasemidis, Leonidas D., Olson, Larry D., Savit, Robert S., Sackellares, J. Chris (1994/01)."Time dependencies in the occurrences of epileptic seizures." Epilepsy Research 17(1): 81-94. <http://hdl.handle.net/2027.42/31879> | en_US |
dc.identifier.uri | http://www.sciencedirect.com/science/article/B6T34-4859N24-1W/2/5d3dcd97ed55bb4d523d3ac42f430b6e | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/31879 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=8174527&dopt=citation | en_US |
dc.description.abstract | A new method of analysis, developed within the framework of nonlinear dynamics, is applied to patient recorded time series of the occurrence of epileptic seizures. These data exhibit broad band spectra and generally have no obvious structure. The goal is to detect hidden internal dependencies in the data without making any restrictive assumptions, such as linearity, about the structure of the underlying system. The basis of our approach is a conditional probabilistic analysis in a phase space reconstructed from the original data. The data, recorded from patients with intractable epilepsy over a period of 1-3 years, consist of the times of occurrences of hundreds of partial complex seizures. Although the epileptic events appear to occur independently, we show that the epileptic process is not consistent with the rules of a homogeneous Poisson process or generally with a random (IID) process. More specifically, our analysis reveals dependencies of the occurrence of seizures on the occurrence of preceding seizures. These dependencies can be detected in the interseizure interval data sets as well as in the rate of seizures per time period. We modeled patient's inaccuracy in recording seizure events by the addition of uniform white noise and found that the detected dependencies are persistent after addition of noise with standard deviation as great as 1/3 of the standard deviation of the original data set. A linear autoregressive analysis fails to capture these dependencies or produces spurious ones in most of the cases. | en_US |
dc.format.extent | 1404810 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Elsevier | en_US |
dc.title | Time dependencies in the occurrences of epileptic seizures | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Kinesiology and Sports | en_US |
dc.subject.hlbsecondlevel | Neurosciences | en_US |
dc.subject.hlbtoplevel | Health Sciences | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Electrical Engineering, Bioengineering Program, Department of Neurology, Epilepsy Program, University of Michigan, Ann Arbor, MI 48109-2122, USA | en_US |
dc.contributor.affiliationum | Department of Neurology, University of Michigan, Ann Arbor, MI 48109-0316, USA | en_US |
dc.contributor.affiliationum | Physics Department, University of Michigan, Ann Arbor, MI 48109, USA | en_US |
dc.contributor.affiliationum | Department of Neurology, University of Michigan, Ann Arbor, MI 48109-0316, USA | en_US |
dc.identifier.pmid | 8174527 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/31879/1/0000830.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1016/0920-1211(94)90081-7 | en_US |
dc.identifier.source | Epilepsy Research | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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