A computationally simple and robust method to detect determinism in a time series

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Standard

A computationally simple and robust method to detect determinism in a time series. / Lu, Sheng; Ju, Ki Hwan; Kanters, Jørgen K.; Chon, Ki H.

I: I E E E Engineering in Medicine and Biology Society. Conference Proceedings, Bind 1, 01.01.2006, s. 763-6.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Lu, S, Ju, KH, Kanters, JK & Chon, KH 2006, 'A computationally simple and robust method to detect determinism in a time series', I E E E Engineering in Medicine and Biology Society. Conference Proceedings, bind 1, s. 763-6. https://doi.org/10.1109/IEMBS.2006.260508

APA

Lu, S., Ju, K. H., Kanters, J. K., & Chon, K. H. (2006). A computationally simple and robust method to detect determinism in a time series. I E E E Engineering in Medicine and Biology Society. Conference Proceedings, 1, 763-6. https://doi.org/10.1109/IEMBS.2006.260508

Vancouver

Lu S, Ju KH, Kanters JK, Chon KH. A computationally simple and robust method to detect determinism in a time series. I E E E Engineering in Medicine and Biology Society. Conference Proceedings. 2006 jan. 1;1:763-6. https://doi.org/10.1109/IEMBS.2006.260508

Author

Lu, Sheng ; Ju, Ki Hwan ; Kanters, Jørgen K. ; Chon, Ki H. / A computationally simple and robust method to detect determinism in a time series. I: I E E E Engineering in Medicine and Biology Society. Conference Proceedings. 2006 ; Bind 1. s. 763-6.

Bibtex

@article{f4160b784cdb46d4b415cb85ee608961,

title = "A computationally simple and robust method to detect determinism in a time series",

abstract = "We present a new, simple, and fast computational technique, termed the incremental slope (IS), that can accurately distinguish between deterministic from stochastic systems even when the variance of noise is as large or greater than the signal, and remains robust for time-varying signals. The IS method is more accurate than the widely utilized Poincare plot analysis especially when the data are severely contaminated by noise. The efficacy of the IS is demonstrated with several simulated deterministic and stochastic signals.",

keywords = "Algorithms, Computer Simulation, Diagnosis, Computer-Assisted, Models, Biological, Numerical Analysis, Computer-Assisted, Signal Processing, Computer-Assisted, Time Factors",

author = "Sheng Lu and Ju, {Ki Hwan} and Kanters, {J{\o}rgen K.} and Chon, {Ki H}",

year = "2006",

month = jan,

day = "1",

doi = "10.1109/IEMBS.2006.260508",

language = "English",

volume = "1",

pages = "763--6",

journal = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",

issn = "0589-1019",

publisher = "IEEE Signal Processing Society",

}

RIS

TY - JOUR

T1 - A computationally simple and robust method to detect determinism in a time series

AU - Lu, Sheng

AU - Ju, Ki Hwan

AU - Kanters, Jørgen K.

AU - Chon, Ki H

PY - 2006/1/1

Y1 - 2006/1/1

N2 - We present a new, simple, and fast computational technique, termed the incremental slope (IS), that can accurately distinguish between deterministic from stochastic systems even when the variance of noise is as large or greater than the signal, and remains robust for time-varying signals. The IS method is more accurate than the widely utilized Poincare plot analysis especially when the data are severely contaminated by noise. The efficacy of the IS is demonstrated with several simulated deterministic and stochastic signals.

AB - We present a new, simple, and fast computational technique, termed the incremental slope (IS), that can accurately distinguish between deterministic from stochastic systems even when the variance of noise is as large or greater than the signal, and remains robust for time-varying signals. The IS method is more accurate than the widely utilized Poincare plot analysis especially when the data are severely contaminated by noise. The efficacy of the IS is demonstrated with several simulated deterministic and stochastic signals.

KW - Algorithms

KW - Computer Simulation

KW - Diagnosis, Computer-Assisted

KW - Models, Biological

KW - Numerical Analysis, Computer-Assisted

KW - Signal Processing, Computer-Assisted

KW - Time Factors

U2 - 10.1109/IEMBS.2006.260508

DO - 10.1109/IEMBS.2006.260508

M3 - Journal article

C2 - 17946422

VL - 1

SP - 763

EP - 766

JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

SN - 0589-1019

ER -

ID: 33911556