scholarly journals Analysing nystagmus waveforms: a computational framework

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Richard V. Abadi ◽  
Ozgur E. Akman ◽  
Gemma E. Arblaster ◽  
Richard A. Clement
Keyword(s):  
Time Series ◽  
Periodic Orbit ◽  
Late Onset ◽  
Periodic Oscillation ◽  
Permutation Entropy ◽  
Entropy Measure ◽  
Unstable Periodic Orbit ◽  
Additional Tool ◽  
Orbit Analysis ◽  
Over Time

AbstractWe present a new computational approach to analyse nystagmus waveforms. Our framework is designed to fully characterise the state of the nystagmus, aid clinical diagnosis and to quantify the dynamical changes in the oscillations over time. Both linear and nonlinear analyses of time series were used to determine the regularity and complexity of a specific homogenous phenotype of nystagmus. Two-dimensional binocular eye movement recordings were carried out on 5 adult subjects who exhibited a unilateral, uniplanar, vertical nystagmus secondary to a monocular late-onset severe visual loss in the oscillating eye (the Heimann-Bielschowsky Phenomenon). The non-affected eye held a central gaze in both horizontal and vertical planes (± 10 min. of arc). All affected eyes exhibited vertical oscillations, with mean amplitudes and frequencies ranging from 2.0°–4.0° to 0.25–1.5 Hz, respectively. Unstable periodic orbit analysis revealed only 1 subject exhibited a periodic oscillation. The remaining subjects were found to display quasiperiodic (n = 1) and nonperiodic (n = 3) oscillations. Phase space reconstruction allowed attractor identification and the computation of a time series complexity measure—the permutation entropy. The entropy measure was found to be able to distinguish between a periodic oscillation associated with a limit cycle attractor, a quasiperiodic oscillation associated with a torus attractor and nonperiodic oscillations associated with higher-dimensional attractors. Importantly, the permutation entropy was able to rank the oscillations, thereby providing an objective index of nystagmus complexity (range 0.15–0.21) that could not be obtained via unstable periodic orbit analysis or attractor identification alone. These results suggest that our framework provides a comprehensive methodology for characterising nystagmus, aiding differential diagnosis and also permitting investigation of the waveforms over time, thereby facilitating the quantification of future therapeutic managements. In addition, permutation entropy could provide an additional tool for future oculomotor modelling.

scholarly journals Permutation Entropy Based on Non-Uniform Embedding

Entropy ◽  
2018 ◽  
Vol 20 (8) ◽  
pp. 612 ◽  
Author(s):  
Mei Tao ◽  
Kristina Poskuviene ◽  
Nizar Alkayem ◽  
Maosen Cao ◽  
Minvydas Ragulskis
Keyword(s):  
Time Series ◽  
Digital Image ◽  
Real World ◽  
Computational Experiments ◽  
Permutation Entropy ◽  
Coordinate Space ◽  
Entropy Measure ◽  
Uniform Attractor ◽  
Uniform Embedding

A novel visualization scheme for permutation entropy is presented in this paper. The proposed scheme is based on non-uniform attractor embedding of the investigated time series. A single digital image of permutation entropy is produced by averaging all possible plain projections of the permutation entropy measure in the multi-dimensional delay coordinate space. Computational experiments with artificially-generated and real-world time series are used to demonstrate the advantages of the proposed visualization scheme.

Comparing different approaches to compute Permutation Entropy with coarse time series

2019 ◽  
Vol 513 ◽  
pp. 635-643 ◽  
Author(s):  
Francisco Traversaro ◽  
Nicolás Ciarrocchi ◽  
Florencia Pollo Cattaneo ◽  
Francisco Redelico
Keyword(s):  
Time Series ◽  
Permutation Entropy

scholarly journals Stock assessment and projections incorporating discard estimates in some years: an application to the hake stock in ICES Divisions VIIIc and IXa

2010 ◽  
Vol 67 (6) ◽  
pp. 1185-1197 ◽  
Author(s):  
C. Fernández ◽  
S. Cerviño ◽  
N. Pérez ◽  
E. Jardim
Keyword(s):  
Time Series ◽  
Stock Assessment ◽  
Reference Points ◽  
Assessment Model ◽  
Marine Science ◽  
Fishing Mortality ◽  
Change Over Time ◽  
Age Structured ◽  
Available Information ◽  
Over Time

Abstract Fernández, C., Cerviño, S., Pérez, N., and Jardim, E. 2010. Stock assessment and projections incorporating discard estimates in some years: an application to the hake stock in ICES Divisions VIIIc and IXa. – ICES Journal of Marine Science, 67: 1185–1197. A Bayesian age-structured stock assessment model is developed to take into account available information on discards and to handle gaps in the time-series of discard estimates. The model incorporates mortality attributable to discarding, and appropriate assumptions about how this mortality may change over time are made. The result is a stock assessment that accounts for information on discards while, at the same time, producing a complete time-series of discard estimates. The method is applied to the hake stock in ICES Divisions VIIIc and IXa, for which the available data indicate that some 60% of the individuals caught are discarded. The stock is fished by Spain and Portugal, and for each country, there are discard estimates for recent years only. Moreover, the years for which Portuguese estimates are available are only a subset of those with Spanish estimates. Two runs of the model are performed; one assuming zero discards and another incorporating discards. When discards are incorporated, estimated recruitment and fishing mortality for young (discarded) ages increase, resulting in lower values of the biological reference points Fmax and F0.1 and, generally, more optimistic future stock trajectories under F-reduction scenarios.

Following the infection dynamics of the tropical trematode Oligogonotylus mayae in its intermediate and definitive hosts for 13 years

2020 ◽  
Vol 94 ◽  
Author(s):  
A.L. May-Tec ◽  
N.A. Herrera-Castillo ◽  
V.M. Vidal-Martínez ◽  
M.L. Aguirre-Macedo
Keyword(s):  
Time Series ◽  
Cross Correlation ◽  
Southern Oscillation ◽  
Fish Host ◽  
Snail Host ◽  
Monthly Data ◽  
Infection Dynamics ◽  
Infection Parameters ◽  
Over Time

Abstract We present a time series of 13 years (2003–2016) of continuous monthly data on the prevalence and mean abundance of the trematode Oligogonotylus mayae for all the hosts involved in its life cycle. We aimed to determine whether annual (or longer than annual) environmental fluctuations affect these infection parameters of O. mayae in its intermediate snail host Pyrgophorus coronatus, and its second and definitive fish host Mayaheros urophthalmus from the Celestun tropical coastal lagoon, Yucatan, Mexico. Fourier time series analysis was used to identify infection peaks over time, and cross-correlation among environmental forcings and infection parameters. Our results suggest that the transmission of O. mayae in all its hosts was influenced by the annual patterns of temperature, salinity and rainfall. However, there was a biannual accumulation of metacercarial stages of O. mayae in M. urophthalmus, apparently associated with the temporal range of the El Niño-Southern Oscillation (five years) and the recovery of the trematode population after a devasting hurricane. Taking O. mayae as an example of what could be happening to other trematodes, it is becoming clear that environmental forcings acting at long-term temporal scales affect the population dynamics of these parasites.

scholarly journals Jonckheere–Terpstra–Kendall-based non-parametric analysis of temporal differential gene expression

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Hitoshi Iuchi ◽  
Michiaki Hamada
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Time Course ◽  
Time Series Data ◽  
Expression Patterns ◽  
Detection Methods ◽  
Series Data ◽  
Expression Levels ◽  
Over Time ◽  
Non Parametric

Abstract Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks modeling of linearly increasing genes with higher-order functions, or fitting of cyclic gene expression with linear functions, thereby leading to false positives/negatives. Here, we present a Jonckheere–Terpstra–Kendall (JTK)-based non-parametric algorithm for TEG detection. Benchmarks, using simulation data, show that the JTK-based approach outperforms existing methods, especially in long time-series experiments. Additionally, application of JTK in the analysis of time-series RNA-seq data from seven tissue types, across developmental stages in mouse and rat, suggested that the wave pattern contributes to the TEG identification of JTK, not the difference in expression levels. This result suggests that JTK is a suitable algorithm when focusing on expression patterns over time rather than expression levels, such as comparisons between different species. These results show that JTK is an excellent candidate for TEG detection.

Income Elasticities for State Tax Revenues: Techniques of Estimation and Their Usefulness for Forecasting

1973 ◽  
Vol 1 (4) ◽  
pp. 409-425 ◽  
Author(s):  
Robert E. Berney ◽  
Bernard H. Frerichs
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Income Elasticity ◽  
Income Elasticities ◽  
Tax Revenues ◽  
State Tax ◽  
The Stability ◽  
Revenue Estimation ◽  
Theoretical Foundations ◽  
Over Time

The concept of income elasticity of tax revenues has been used in numerous studies with little concern about its theoretical foundations. Income elasticities have also been used for revenue estimation with limited concern about stability over time or about the accuracy of the forecasts. This paper explores the development of the tax elasticity measure and, using revenue data from Washington, compares year-to-year elasticity measures with those established by regression analysis. The length of the time series is varied to check on the stability of the coefficients. Finally, the elasticities are used to predict revenues for three years to check on their accuracy for revenue estimation.

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