Biomedical subjects

Tomomichi Nakamura

Publications and source records attributed to Tomomichi Nakamura.

6 recordsLinked to original sources

PubMed2006

Testing for correlation structures in short-term variabilities with long-term trends of multivariate time series.

We describe a method for identifying correlation structures in irregular fluctuations (short-term variabilities) of multivariate time series, even if they exhibit long-term trends. This method is based on the previously proposed small shuffle surrogate method. The null hypothesis addressed by this method is that there is no short-term correlation structure among data or that the irregular fluctuations are independent. The method is demonstrated for numerical data generated by known systems and applied to several experimental time series.

Journal Article↗

PubMed2006

Testing for nonlinearity in irregular fluctuations with long-term trends.

We describe a method for investigating nonlinearity in irregular fluctuations (short-term variability) of time series even if the data exhibit long-term trends (periodicities). Such situations are theoretically incompatible with the assumption of previously proposed methods. The null hypothesis addressed by our algorithm is that irregular fluctuations are generated by a stationary linear system. The method is demonstrated for numerical data generated by known systems and applied to several actual time series.

Journal Article↗

PubMed2006

Degeneracy of time series models: the best model is not always the correct model.

There are a number of good techniques for finding, in some sense, the best model of a deterministic system given a time series of observations. We examine a problem called model degeneracy, which has the consequence that even when a perfect model of a system exists, one does not find it using the best techniques currently available. The problem is illustrated using global polynomial models and the theory of Grobner bases.

Algorithms↗

PubMed2005

Small-shuffle surrogate data: testing for dynamics in fluctuating data with trends.

We describe a method for identifying dynamics in irregular time series (short term variability). The method we propose focuses attention on the flow of information in the data. We can apply the method even for irregular fluctuations which exhibit long term trends (periodicities): situations in which previously proposed surrogate methods would give erroneous results. The null hypothesis addressed by our algorithm is that irregular fluctuations are independently distributed random variables (in other words, there is no short term dynamics). The method is demonstrated for numerical data generated by known systems, and applied to several actual time series.

Journal Article↗

PubMed2005

Testing for nonlinearity in time series without the Fourier transform.

A method to test for nonlinearity in time series, without the need to apply the Fourier transform, is proposed. This method therefore avoids the drawbacks of previously proposed surrogate techniques associated with the estimation of the signal's power spectrum. The test addressed by this algorithm is that the data are generated by a stationary linear system. To achieve this, the algorithm takes advantage of the fundamentally different structure of linear and nonlinear systems.

Journal Article↗

PubMed2005

Surrogate test to distinguish between chaotic and pseudoperiodic time series.

In this paper a different algorithm is proposed to produce surrogates for pseudoperiodic time series. By imposing a few constraints on the noise components of pseudoperiodic data sets, we devise an effective method to generate surrogates. Unlike other algorithms, this method properly copes with pseudoperiodic orbits contaminated with linear colored observational noise. We will demonstrate the ability of this algorithm to distinguish chaotic orbits from pseudoperiodic orbits through simulation data sets from the Rössler system. As an example of application of this algorithm, we will also employ it to investigate a human electrocardiogram record.

Journal Article↗