A guide to dynamical analysis

Rapp, Paul E.

doi:10.1007/bf02691335

Cited by 71 publications

(42 citation statements)

References 90 publications

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“…A: The choice of the delays for the embedding Although many authors have warned against inappropriate use of reconstruction parameters for the estimation of D 2 and have even provided recipes of what to chose (nicely reviewed by Rapp, 1994), none have published on the influence of parameter choices on the evolution of D 2 during a process of which the dynamics gradually change. In a pilot study we analyzed the spread of an epileptic seizure in a kindled rat brain.…”

Section: Appendicesmentioning

confidence: 99%

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Nonlinear dynamics of epileptic seizures on basis of intracranial EEG recordings

et al. 1997

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Pijn, J.P.; Velis, D.N.; van der Heyden, M.; de Goede, J.; van Veelen, C.W.M. General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Signal analysis methods derived from the theory of nonlinear dynamics provide new tools to characterize the behavior of such networks, and are particularly relevant for the analysis of epileptiform activity. Methods: We calculated the correlation dimension, tested for irreversibility, and made recurrence plots of EEG signals recorded intracranially both during interictal and ictal states in temporal lobe epilepsy patients who were surgical candidates. Results: Epileptic seizure activity often, but not always, emerges as a low-dimensional oscillation. In general, the seizure behaves as a nonstationary phenomenon during which both phases of low and high complexity may occur. Nevertheless a low dimension may be found mainly in the zone of ietal onset and nearby structures. Both the zone of ictal onset and the pattern of propagation of seizure activity in the brain could be identified using this type of analysis. Furthermore, the results obtained were in close agreement with visual inspection of the EEG records. Conclusions: Application of these mathematical tools provides novel insights into the spatio-temporal dynamics of "epileptic brain states". In this way it may be of practical use in the localization of an epileptogenic region in the brain, and thus be of assistance in the presurgical evaluation of patients with localization-related epilepsy.

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Section: Appendicesmentioning

confidence: 99%

“…This is a kind of autoscaling. Usually the range is related to the autocorrelation function of the signal (or the auto-information function as proposed by Fraser and Swinney 1986; additional alternatives are reviewed in Rapp 1994).…”

Section: Appendicesmentioning

confidence: 99%

Nonlinear dynamics of epileptic seizures on basis of intracranial EEG recordings

et al. 1997

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“…One problem in physiologic research is the signal processing and analysis of nonstationary time series [2,23,[28][29][30][31]. Many physiologic time series are extremely inhomogeneous and nonstationary, fluctuating in an irregular and complex manner.…”

Section: Multifractality and Cardiac Regulationmentioning

confidence: 99%

Multiscale aspects of cardiac control

Ivanov

Chen

et al. 2004

Physica A: Statistical Mechanics and its Applications

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We report some recent attempts to understand the dynamics of complex physiologic fluctuations by adapting and extending concepts and methods developed very recently in statistical physics. We first review recent progress using wavelet-based multifractal analysis, magnitude and sign decomposition analysis and a new segmentation algorithm to quantify multiscale features of heartbeat interval series. We then investigate how heartbeat dynamics change with circadian influences and under pathologic conditions, and we discuss their possible relation to the underlaying cardiac control mechanisms. The analytic tools we discuss may be used on a wider range of physiologic signals. r

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“…aus den PPG-Zeitreihen bestimmt und entfernt. Die so erhaltenen Zeitreihen wurden danach mittels nichtlinearer Analyse [1,9] (Zustandsraumrekonstruktion, «recurrence plots» und deren Masszahlen: Ratio, Divergenz, etc.) auf ihre Ønderung des Informationsgehaltes untersucht [3,17].…”

Section: Ppg-analyseunclassified

Elektromagnetische Bioinformation im Frequenzbereich von 100 Hz bis 100 kHz?

Klima¹,

Lipp²,

Lahrmann³

et al. 1998

Complement Med Res

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Electromagnetic Bioinformation in the Frequency Region between 100 Hz and 100 kHz?A highly sensitive measurement amplifier (BIT device) was constructed in order to detect possible electromagnetic biosignals in the frequency region between 100 Hz and 100 kHz. Even when working with the highest amplification mode of the BIT device, no endogenous electromagnetic biosignals could be detected on the test persons, but only well-known EMG signals. If the BIT device worked in a feedback mode, electromagnetic oscillations beween 1.7 and 2.9 kHz could be generated and oscilloscopically detected; these oscillations are caused by the oscillator system ‘BIT – man’, depending on the impedance of the human body. Biological effects of the impedance-depending oscillations were investigated in a simple randomized double-blind study. Three anamnestically healthy persons were treated 20 times with their specific oscillations. The physiological effects of this treatment were measured by pulse plethysmography. Nonlinear analysis of the time series indicated significant changes in pulse dynamics of one person. Linear analysis of heart rate variability showed no statistical significance. Our device was only designed for the project described below. It is, therefore, evident that the research results presented in this paper cannot be applied to any of the therapy devices at present on the market.

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A guide to dynamical analysis

Cited by 71 publications

References 90 publications

Nonlinear dynamics of epileptic seizures on basis of intracranial EEG recordings

Nonlinear dynamics of epileptic seizures on basis of intracranial EEG recordings

Multiscale aspects of cardiac control

Elektromagnetische Bioinformation im Frequenzbereich von 100 Hz bis 100 kHz?

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