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Titolo:
NONLINEAR-ANALYSIS OF CHAOTIC DYNAMICS UNDERLYING THE ELECTROENCEPHALOGRAM IN PATIENTS WITH ALZHEIMERS-DISEASE
Autore:
JEONG J; KIM SY;
Indirizzi:
KOREA ADV INST SCI & TECHNOL,DEPT PHYS TAEJON 305701 SOUTH KOREA
Titolo Testata:
Journal of the Korean Physical Society
fascicolo: 2, volume: 30, anno: 1997,
parte:, 1
pagine: 320 - 327
SICI:
0374-4884(1997)30:2<320:NOCDUT>2.0.ZU;2-I
Fonte:
ISI
Lingua:
ENG
Soggetto:
STRANGE ATTRACTORS; TIME-SERIES; EMBEDDING DIMENSION; SLEEP; EXPONENT;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Citazioni:
27
Recensione:
Indirizzi per estratti:
Citazione:
J. Jeong e S.Y. Kim, "NONLINEAR-ANALYSIS OF CHAOTIC DYNAMICS UNDERLYING THE ELECTROENCEPHALOGRAM IN PATIENTS WITH ALZHEIMERS-DISEASE", Journal of the Korean Physical Society, 30(2), 1997, pp. 320-327

Abstract

We investigated the chaotic dynamics underlying the electroencephalogram (EEG) in patients with Alzheimer's disease by nonlinear methods tounderstand the role of chaos in brain function. In the analysis, we calculated the correlation dimension D-2 and the largest Lyapunov exponent L-1. A new method, proposed by Kennel et al., for calculating nonlinear invariant measures was used. The method determines the proper minimum embedding dimension by looking at the behavior of nearest neighbors under changes in the embedding dimension d from d to d + 1. We showed that it is strikingly faster and more accurate than other algorithms for limited noisy data. We found that, in almost all channels, patients with Alzheimer's disease have significantly lower D-2 and L-1 than age-approximated non-demented controls. It is, therefore, inferred that brains injured by Alzheimer's disease have electrophysiologically inactive elements (i.e., neurons and/or synapses) and thus show decreased chaotic behavior. These results support the assumption that chaos plays an important role in brain function, for instance, learning and memory. We suggest that brains can be described by deterministic models. In this paper we show that nonlinear analysis can provide a promising tool for detecting relative changes in the complexity of brain dynamics, which cannot be detected by conventional linear analysis. We propose a nonlinear analysis of the EEG in Alzheimer's disease for diagnosis as a clinical application.

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Documento generato il 09/04/20 alle ore 19:56:09