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matlab-based mes toolbox (MathWorks Inc)

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MathWorks Inc matlab-based mes toolbox
Matlab Based Mes Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/matlab+mes+toolbox/pm39999112-142-1-1
Average 90 stars, based on 1 article reviews

matlab-based mes toolbox - by Bioz Stars, 2026-09

90/100 stars

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Article Title: Age-related preference for geometric spatial cues during real-world navigation.
Article Snippet: 1Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France.. 2Institute of Biophysics, National Research Council, Palermo, Italy.. 3CHNO des Quinze-Vingts, INSERM-DGOS CIC 1423, Paris, France.

Article Title: The Strength of Alpha Oscillations in the Electroencephalogram Differently Affects Algorithms Used for Anesthesia Monitoring
Article Snippet: BACKGROUND: Intraoperative patient monitoring using the electroencephalogram (EEG) can help to adequately adjust the anesthetic level.. Therefore, the processed EEG (pEEG) provides the anesthesiologist with the estimated anesthesia level.. The commonly used approaches track the changes from a fastand a low-amplitude EEG during wakefulness to a slowand a high-amplitude EEG under general anesthesia.

Article Title: Electroencephalography-Based Effects of Acute Alcohol Intake on the Pain Matrix.
Article Snippet: To test for statistical differences in the EEG, we calculated the area under the receiver operating characteristics (AUROC) with the 1000-fold bootstrapped 95% confidence intervals using the MES toolbox for MATLAB [41].

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Changes in PeEn with increasing embedding dimension for window lengths of 500, 5000 and 50,000 data points. White noise signals have the highest PeEn values compared to pink and brown noise signals in any given setting. PeEn generally decreases with increasing m , especially for short window lengths. An increasing window length led to increased PeEn values in higher embedding dimensions in all three simulated signals. The variability of PeEn within each of the simulated signals decreases with higher window lengths and depends on the properties of the signal. 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

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: Changes in PeEn with increasing embedding dimension for window lengths of 500, 5000 and 50,000 data points. White noise signals have the highest PeEn values compared to pink and brown noise signals in any given setting. PeEn generally decreases with increasing m , especially for short window lengths. An increasing window length led to increased PeEn values in higher embedding dimensions in all three simulated signals. The variability of PeEn within each of the simulated signals decreases with higher window lengths and depends on the properties of the signal. 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: Whisker Assay

Probability of monotonous and non-occurring patterns for various embedding dimensions and window lengths. With increasing embedding dimensions, the probability of monotonous patterns decreases across all window lengths (4A). All ordinal patterns occur in embedding dimensions m = 3 and m = 4 but with higher embedding dimensions, the probability of non-occurring patterns increases (4B). Window length, however, weakens this trend. Monotonous patterns as well as non-occurring patterns are most prominent in brown noise, followed by pink noise and lastly by white noise. 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

Journal: Journal of Clinical Monitoring and Computing

doi: 10.1007/s10877-023-01051-z

Figure Lengend Snippet: Probability of monotonous and non-occurring patterns for various embedding dimensions and window lengths. With increasing embedding dimensions, the probability of monotonous patterns decreases across all window lengths (4A). All ordinal patterns occur in embedding dimensions m = 3 and m = 4 but with higher embedding dimensions, the probability of non-occurring patterns increases (4B). Window length, however, weakens this trend. Monotonous patterns as well as non-occurring patterns are most prominent in brown noise, followed by pink noise and lastly by white noise. 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: Whisker Assay

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

Journal: Journal of Clinical Monitoring and Computing

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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