Dynamic analysis of heartbeat rate signals of epileptics using multidimensional phase space reconstruction approach

Su, Zhiyuan; Wu, Tso-Ren; Yang, Po-Hua; Wang, Yeng‐Tseng

doi:10.1016/j.physa.2007.12.008

Cited by 23 publications

(18 citation statements)

References 22 publications

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“…The average values of the Lyapunov exponents found here are similar to that of the heartbeat [20]. Future work will involve measuring simultaneously the heartbeat signal alongside the ocular aberrations, and processing both signals using chaos theory.…”

Section: Discussionmentioning

confidence: 53%

Chaos in ocular aberration dynamics of the human eye

Hampson¹,

Mallen²

2012

Biomed. Opt. Express

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Since the characterization of the eye’s monochromatic aberration fluctuations in 2001, the power spectrum has remained the most widely used method for analyzing their dynamics. However, the power spectrum does not capture the complexities of the fluctuations. We measured the monochromatic aberration dynamics of six subjects using a Shack-Hartmann sensor sampling at 21 Hz. We characterized the dynamics using techniques from chaos theory. We found that the attractor embedding dimension for all aberrations, for all subjects, was equal to three. The embedding lag averaged across aberrations and subjects was 0.31 ± 0.07 s. The Lyapunov exponent of the rms wavefront error was positive for each subject, with an average value of 0.44 ± 0.15 µm/s. This indicates that the aberration dynamics are chaotic. Implications for future modeling are discussed.

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Section: Discussionmentioning

confidence: 53%

Chaos in ocular aberration dynamics of the human eye

Hampson¹,

Mallen²

2012

Biomed. Opt. Express

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“…2008) and was set to 5. The embedding dimension was taken as the dimension in which the percentage of FNNs was 5%.…”

Section: Discussionmentioning

confidence: 99%

Sensitivity of Chaos Measures in Detecting Stress in the Focusing Control Mechanism of the Short-Sighted Eye

2017

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When fixating on a stationary object, the power of the eye’s lens fluctuates. Studies have suggested that changes in these so-called microfluctuations in accommodation may be a factor in the onset and progression of short-sightedness. Like many physiological signals, the fluctuations in the power of the lens exhibit chaotic behaviour. A breakdown or reduction in chaos in physiological systems indicates stress to the system or pathology. The purpose of this study was to determine whether the chaos in fluctuations of the power of the lens changes with refractive error, i.e. how short-sighted a subject is, and/or accommodative demand, i.e. the effective distance of the object that is being viewed. Six emmetropes (EMMs, non-short-sighted), six early-onset myopes (EOMs, onset of short-sightedness before the age of 15), and six late-onset myopes (LOMs, onset of short-sightedness after the age of 15) took part in the study. Accommodative microfluctuations were measured at 22 Hz using an SRW-5000 autorefractor at accommodative demands of 1 D (dioptres), 2 D, and 3 D. Chaos theory analysis was used to determine the embedding lag, embedding dimension, limit of predictability, and Lyapunov exponent. Topological transitivity was also tested for. For comparison, the power spectrum and standard deviation were calculated for each time record. The EMMs had a statistically significant higher Lyapunov exponent than the LOMs ( vs. ) and a lower embedding dimension than the LOMs ( vs. ). There was insufficient evidence (non-significant p value) of a difference between EOMs and EMMs or EOMs and LOMs. The majority of time records were topologically transitive. There was insufficient evidence of accommodative demand having an effect. Power spectrum analysis and assessment of the standard deviation of the fluctuations failed to discern differences based on refractive error. Chaos differences in accommodation microfluctuations indicate that the control system for LOMs is under stress in comparison to EMMs. Chaos theory analysis is a more sensitive marker of changes in accommodation microfluctuations than traditional analysis methods.

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“…Phase space reconstruction (PSR) is the basement of researching complex system by observing time series [11][12]. In 1981, Packard advised that the PSR can be formed by the delay coordinate got from a variable in the original system, and Takens has proved a suitable embedding dimension can be used to resume the regular track in the system.…”

Section: Phase Space Reconstructionmentioning

confidence: 99%

Research on the chaotic characteristics for heating load time series

Wang

Wei-gui

2011

2011 6th IEEE Conference on Industrial Electronics and Applications

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In order to observe and study the possibility of the existence of chaotic behavior in the heating load, profound research has been performed at the real-test heating supplying load time series from 2007 to 2010 in this paper. Firstly, the data which were got from the worksite are pre-processed. Secondly, phase space reconstruction is done to the time series which have been processed. Thirdly, a qualitative, quantitative and RP analysis of data are accomplished. Finally, the rigorous chaotic character of the heating supplying load is found. This conclusion can provides evidence on the feasibility of introducing the chaotic control and prediction to the heating supplying system with the properties of large time-lag, nonlinear, time varying etc.

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Dynamic analysis of heartbeat rate signals of epileptics using multidimensional phase space reconstruction approach

Cited by 23 publications

References 22 publications

Chaos in ocular aberration dynamics of the human eye

Chaos in ocular aberration dynamics of the human eye

Sensitivity of Chaos Measures in Detecting Stress in the Focusing Control Mechanism of the Short-Sighted Eye

Research on the chaotic characteristics for heating load time series

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