Abstract:
In this paper we consider the self-consistent stationary level of electroencephalogram time series. The practical purpose of this statistics is to construct the disorder indicator. Unlike the classical problem of stationary test of two samples, in our case one should construct an indicator to predict the change in the nonstationary regime. For example, we consider special predictor of an attack of epilepsy.
Citation:
A. A. Kislitsyn, A. B. Kozlova, E. L. Masherov, Yu. N. Orlov, “Numerical algorithm for self-consistent stationary level for multidimensional non-stationary time-series”, Keldysh Institute preprints, 2017, 124, 14 pp.
\Bibitem{KisKozMas17}
\by A.~A.~Kislitsyn, A.~B.~Kozlova, E.~L.~Masherov, Yu.~N.~Orlov
\paper Numerical algorithm for self-consistent stationary level for multidimensional non-stationary time-series
\jour Keldysh Institute preprints
\yr 2017
\papernumber 124
\totalpages 14
\mathnet{http://mi.mathnet.ru/ipmp2340}
\crossref{https://doi.org/10.20948/prepr-2017-124-e}
Linking options:
https://www.mathnet.ru/eng/ipmp2340
https://www.mathnet.ru/eng/ipmp/y2017/p124
This publication is cited in the following 2 articles:
Alexey A. Kislitsyn, Yurii N. Orlov, 2021 International Joint Conference on Neural Networks (IJCNN), 2021, 1
A. A. Kislitsyn, A. B. Kozlova, M. B. Korsakova, E. L. Masherov, Yu. N. Orlov, “Statsionarnaya tochka urovnya znachimosti dlya nestatsionarnykh funktsii raspredeleniya”, Preprinty IPM im. M. V. Keldysha, 2018, 113, 20 pp.
Citation in format AMSBIB
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