Literature DB >> 16252980

Correlation dimension and integral do not predict epileptic seizures.

Mary Ann F Harrison1, Ivan Osorio, Mark G Frei, Srividhya Asuri, Ying-Cheng Lai.   

Abstract

Reports in the literature have indicated potential value of the correlation integral and dimension for prediction of epileptic seizures up to several minutes before electrographic onset. We apply these measures to over 2000 total hours of continuous electrocortiogram, taken from 20 patients with epilepsy, examine their sensitivity to quantifiable properties such as the signal amplitude and autocorrelation, and investigate the influence of embedding and filtering strategies on their performance. The results are compared against those obtained from surrogate time series. Our conclusion is that neither the correlation dimension nor the correlation integral has predictive power for seizures.

Entities:  

Mesh:

Year:  2005        PMID: 16252980     DOI: 10.1063/1.1935138

Source DB:  PubMed          Journal:  Chaos        ISSN: 1054-1500            Impact factor:   3.642


  8 in total

1.  Seizure prediction: methods.

Authors:  Paul R Carney; Stephen Myers; James D Geyer
Journal:  Epilepsy Behav       Date:  2011-12       Impact factor: 2.937

2.  A rule-based seizure prediction method for focal neocortical epilepsy.

Authors:  Ardalan Aarabi; Bin He
Journal:  Clin Neurophysiol       Date:  2012-02-22       Impact factor: 3.708

3.  Detection of seizure rhythmicity by recurrences.

Authors:  Mary Ann F Harrison; Mark G Frei; Ivan Osorio
Journal:  Chaos       Date:  2008-09       Impact factor: 3.642

4.  Seizure prediction.

Authors:  J Chris Sackellares
Journal:  Epilepsy Curr       Date:  2008 May-Jun       Impact factor: 7.500

5.  An investigation of EEG dynamics in an animal model of temporal lobe epilepsy using the maximum Lyapunov exponent.

Authors:  Sandeep P Nair; Deng-Shan Shiau; Jose C Principe; Leonidas D Iasemidis; Panos M Pardalos; Wendy M Norman; Paul R Carney; Kevin M Kelly; J Chris Sackellares
Journal:  Exp Neurol       Date:  2008-11-27       Impact factor: 5.330

6.  Detecting and characterizing high-frequency oscillations in epilepsy: a case study of big data analysis.

Authors:  Liang Huang; Xuan Ni; William L Ditto; Mark Spano; Paul R Carney; Ying-Cheng Lai
Journal:  R Soc Open Sci       Date:  2017-01-18       Impact factor: 2.963

7.  Comparative study of nonlinear properties of EEG signals of normal persons and epileptic patients.

Authors:  Md Nurujjaman; Ramesh Narayanan; An Sekar Iyengar
Journal:  Nonlinear Biomed Phys       Date:  2009-07-20

8.  Directed dynamical influence is more detectable with noise.

Authors:  Jun-Jie Jiang; Zi-Gang Huang; Liang Huang; Huan Liu; Ying-Cheng Lai
Journal:  Sci Rep       Date:  2016-04-12       Impact factor: 4.379

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.