Abstract
The algorithm for the detection of lag synchronization from time series data is presented in this paper. Multi-variate time series data are re-organized into a sequence of perfect matrices of delayed Lagrange differences and mapped into a two-dimensional pattern of discriminants. The minimization of this pattern yields a discrete sequence of time lags with a high resolution in time. The proposed technique is capable to detect lag synchronization between chaotic signals contaminated by noise. The proposed technique is also exploited as the feature extraction algorithm for the detection of cyclic alternating patterns in sleep. Computational experiments are used to demonstrate the efficacy of the proposed algorithm.
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