Journal: Journal of Clinical Monitoring and Computing
Article Title: An in-depth analysis of parameter settings and probability distributions of specific ordinal patterns in the Shannon permutation entropy during different states of consciousness in humans
doi: 10.1007/s10877-023-01051-z
Figure Lengend Snippet: Analysis of PeEn, probability of monotonous and non-occurring patterns in clinical EEG signals of 120 s and 20 s segments. In both segment lengths, the PeEn as well as the probability of monotonous patterns decreases with higher embedding dimension, with wake signals generally having higher values than the two anaesthesia signals. As for the probability of non-occurring pattern an increasing embedding dimension causes a higher proportion of non-occurring patterns. Here, wake signals generally have the lowest values, compared to the anaesthesia signals. The comparison of the two anaesthesia signals shows slightly higher PeEn values and less non-occurring patterns for the deeper-level anaesthesia signals (inter2), compared to the lighter-level ones (inter1). For the most part, the 120 s and the 20 s segments do not differ much from each other, except for the fact, that the variability in values is higher in the shorter segment, compared to the longer ones and that the start of patterns not occurring is at an embedding dimension of m = 5 for the longer and at m = 4 for the shorter segments. In the boxplots, one whisker connects the upper quartile to the nonoutlier maximum (the maximum data value that is not an outlier), and the other connects the lower quartile to the nonoutlier minimum (the minimum data value that is not an outlier). Outliers are values that are more than 1.5 interquartile range away from the top or bottom of the box as defined as default setting in the MATLAB boxchart function
Article Snippet: For the analysis, we used the MES-toolbox for MATLAB [ ].
Techniques: Comparison, Whisker Assay
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