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• W2102219141 abstract "The stability of a dynamical system can be indicated by eigenvalues of its underlying mathematical model. However, eigenvalue analysis of a complicated system (e.g. the heart) may be extremely difficult because full models may be intractable or unavailable. We develop data-driven statistical techniques, which are independent of any underlying dynamical model, that use principal components and maximum-likelihood methods to estimate the dominant eigenvalues and their standard errors from the time series of one or a few measurable quantities, e.g. transmembrane voltages in cardiac experiments. The techniques are applied to predicting cardiac alternans that is characterized by an eigenvalue approaching −1. Cardiac alternans signals a vulnerability to ventricular fibrillation, the leading cause of death in the USA." @default.
• W2102219141 created "2016-06-24" @default.
• W2102219141 creator A5044244037 @default.
• W2102219141 creator A5056442662 @default.
• W2102219141 date "2012-07-04" @default.
• W2102219141 modified "2023-09-30" @default.
• W2102219141 title "Estimating eigenvalues of dynamical systems from time series with applications to predicting cardiac alternans" @default.
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• W2102219141 doi "https://doi.org/10.1098/rspa.2012.0098" @default.
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