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. Author manuscript; available in PMC: 2021 May 21.
Published in final edited form as: Neuroimage. 2021 Feb 20;232:117893. doi: 10.1016/j.neuroimage.2021.117893

Fig. 1.

Fig. 1.

A scheme of the sliding window-based dynamic entropy calculation. A) A large time window is used to extract a sub-time series at N successive timepoints (N=8 here) from the original time series. The green box indicates the window slid to the n-th timepoint. B) The standard sample entropy formula is used to calculate entropy for the sub-time series extracted from A. B.1 and B.2 illustrate the embedding vector matching process for the embedding window length of m and m+1, respectively. The boxes in different color indicate the locations of the embedding vectors in the input time series—the sub-series from A).