Manifestation of Chaos in Real Complex Systems: Case of Parkinson’s Disease

Yulmetyev, Renat M.; Demin, Sergey; Hänggi, Peter

doi:10.1007/3-540-32023-7_11

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…It is known that the signals produced in vivo by a human organism contain both a collection of low-frequency ("resonance") components and higherfrequency stochastic variability [47,48,49,50,51,52,53]. We believe that the random components are responsible for the adjustment of the organism to constantly changing external conditions, which also manifests itself as the rearrangement of resonance components.…”

Section: Classification Of Eeg Signals Based On Degree Of Frequency-pmentioning

confidence: 99%

Analysis of cross-correlations in electroencephalogram signals as an approach to proactive diagnosis of schizophrenia

Timashev

Panischev

Polyakov

et al. 2012

Physica A: Statistical Mechanics and its Applications

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We apply flicker-noise spectroscopy (FNS), a time series analysis method operating on structure functions and power spectum estimates, to study the clinical electroencephalogram (EEG) signals recorded in children/adolescents (11 to 14 years of age) with diagnosed schizophrenia-spectrum symptoms at the National Center for Psychiatric Health (NCPH) of the Russian Academy of Medical Sciences. The EEG signals for these subjects were compared with the signals for a control sample of chronically depressed children/adolescents. The purpose of the study is to look for diagnostic signs of subjects' susceptibility to schizophrenia in the FNS parameters for specific electrodes and cross-correlations between the signals simultaneously measured at different points on the scalp. Our analysis of EEG signals from scalp-mounted electrodes at locations F 3 and F 4 , which are symmetrically positioned in the left and right frontal areas of cerebral cortex, respectively, demonstrates an essential role of frequency-phase synchronization, a phenomenon represent- * ing specific correlations between the characteristic frequencies and phases of excitations in the brain. We introduce quantitative measures of frequencyphase synchronization and systematize the values of FNS parameters for the EEG data. The comparison of our results with the medical diagnoses for 84 subjects performed at NCPH makes it possible to group the EEG signals into 4 categories corresponding to different risk levels of subjects' susceptibility to schizophrenia. We suggest that the introduced quantitative characteristics and classification of cross-correlations may be used for the diagnosis of schizophrenia at the early stages of its development.

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Section: Classification Of Eeg Signals Based On Degree Of Frequency-pmentioning

confidence: 99%

Analysis of cross-correlations in electroencephalogram signals as an approach to proactive diagnosis of schizophrenia

Timashev

Panischev

Polyakov

et al. 2012

Physica A: Statistical Mechanics and its Applications

Self Cite

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“…In [14,15] correlations and statistical memory effects were studied in the sequence of Wolf numbers as one of the quantitative characteristics of solar activity, as well as the X-ray intensity of the GRS 1915+105 microquasar. The results obtained reveal individual physical mechanisms of X-ray generation, the energy released during accretion in the GRS 1915+105 binary system, and the formation of sunspots in the solar photosphere.…”

Section: Memory Functions Formalismmentioning

confidence: 99%

The development of statistical analysis methods for the study of correlations and statistical memory effects in the recorded data of physical experiments

Demin

Yunusov

2022

J. Phys.: Conf. Ser.

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In this paper, we discuss the prospects for the use of statistical analysis methods in the study of correlations and statistical memory effects in the experimental data of physical experiments. Developed methods under consideration: Memory Functions Formalism and Flicker-Noise Spectroscopy allow obtaining a large set of quantitative parameters and qualitative characteristics directly from temporal signals generated by complex physical systems. Additionally, for the study of collective phenomena and effects, one- and two-parameter cross-correlation functions are proposed, which allow studying cross-correlations between simultaneously recorded signals in spatially separated areas of the object under study. The introduced analytical relations and numerical algorithms can be applied to solve the problems of metrology of surface structures at the nano- and microlevels, to study collective phenomena in the plasma of astrophysical objects, the structure of molecules, and their complexes.

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“…Many models have been developed for viral infectious diseases such as Influenza and Ebola [9]- [14] and nearly all of them show chaotic dynamics even after vaccination is administrated [15]. The emergence of chaotic behavior can be attributed mostly to predator-prey and competition dynamics [16]- [18] as well as nonlinear interactions between cell populations such as in cancer models [19]- [22] and Parkinson's disease [23], [24].…”

Section: Introductionmentioning

confidence: 99%

FPGA Realizations of Chaotic Epidemic and Disease Models Including Covid-19

et al. 2021

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The spread of epidemics and diseases is known to exhibit chaotic dynamics; a fact confirmed by many developed mathematical models. However, to the best of our knowledge, no attempt to realize any of these chaotic models in analog or digital electronic form has been reported in the literature. In this work, we report on the efficient FPGA implementations of three different virus spreading models and one disease progress model. In particular, the Ebola, Influenza, and COVID-19 virus spreading models in addition to a Cancer disease progress model are first numerically analyzed for parameter sensitivity via bifurcation diagrams. Subsequently and despite the large number of parameters and large number of multiplication (or division) operations, these models are efficiently implemented on FPGA platforms using fixed-point architectures. Detailed FPGA design process, hardware architecture and timing analysis are provided for three of the studied models (Ebola, Influenza, and Cancer) on an Altera Cyclone IV EP4CE115F29C7 FPGA chip. All models are also implemented on a high performance Xilinx Artix-7 XC7A100TCSG324 FPGA for comparison of the needed hardware resources. Experimental results showing real-time control of the chaotic dynamics are presented.

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Manifestation of Chaos in Real Complex Systems: Case of Parkinson’s Disease

Cited by 8 publications

References 18 publications

Analysis of cross-correlations in electroencephalogram signals as an approach to proactive diagnosis of schizophrenia

Analysis of cross-correlations in electroencephalogram signals as an approach to proactive diagnosis of schizophrenia

The development of statistical analysis methods for the study of correlations and statistical memory effects in the recorded data of physical experiments

FPGA Realizations of Chaotic Epidemic and Disease Models Including Covid-19

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