glucose data analysis toolbox (MathWorks Inc)

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MathWorks Inc glucose data analysis toolbox

Glucose Data Analysis Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 2340 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/Databases/Database+Toolbox/pm36602030-2-22-28
Average 96 stars, based on 2340 article reviews

glucose data analysis toolbox - by Bioz Stars, 2026-09

96/100 stars

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1) Product Images from "AGATA: A Toolbox for Automated Glucose Data Analysis."

Article Title: AGATA: A Toolbox for Automated Glucose Data Analysis.

Journal: Journal of diabetes science and technology

doi: 10.1177/19322968221147570

Figure Legend Snippet: Figure 1. On the top, the processing pipeline that can be implemented with AGATA thanks to its functionalities. On the bottom left, the home screen of AGATA’s standalone version, which allows to easily access specific interfaces to either analyze a single/multiple glucose profiles or compare two groups of glucose profiles. On the bottom right, as representative use case, the dedicated GUI that allows to analyze a single glucose profile. Abbreviations: AGATA, Automated Glucose dATa Analysis; GUI, graphical user interface.

Techniques Used:

Figure 2. Detailed visualization of a week of CGM data extracted from a representative subject (Ohio544) obtained using AGATA’s utilities. Panel (a) (plot I) shows with a black solid line the CGM trace and highlights the hyperglycemic events with yellow areas, the hypoglycemic events with red areas, and the missing portion of data with shaded red areas. It also marks the target glycemic range with a shaded gray shaded area delimited by two green dashed lines. Panel (b) (plot II) shows with a black solid line the CGM trace and highlights, with superimposed dots, aggregated glucose values in hyperglycemia (in yellow), hypoglycemia (in red), and target (in green). It also highlights missing portion of data with shaded red areas and marks the target glycemic range with a shaded gray area delimited by two green dashed lines. Panel (c) (plot VII) shows with a black solid line the CGM rate of change. It also highlights positive values with a gray shaded area and the missing portion of data with red shaded areas. Abbreviations: CGM, continuous glucose monitoring; AGATA, Automated Glucose dATa Analysis.

Figure Legend Snippet: Figure 2. Detailed visualization of a week of CGM data extracted from a representative subject (Ohio544) obtained using AGATA’s utilities. Panel (a) (plot I) shows with a black solid line the CGM trace and highlights the hyperglycemic events with yellow areas, the hypoglycemic events with red areas, and the missing portion of data with shaded red areas. It also marks the target glycemic range with a shaded gray shaded area delimited by two green dashed lines. Panel (b) (plot II) shows with a black solid line the CGM trace and highlights, with superimposed dots, aggregated glucose values in hyperglycemia (in yellow), hypoglycemia (in red), and target (in green). It also highlights missing portion of data with shaded red areas and marks the target glycemic range with a shaded gray area delimited by two green dashed lines. Panel (c) (plot VII) shows with a black solid line the CGM rate of change. It also highlights positive values with a gray shaded area and the missing portion of data with red shaded areas. Abbreviations: CGM, continuous glucose monitoring; AGATA, Automated Glucose dATa Analysis.

Techniques Used:

Figure Legend Snippet: Figure 3. Summary visualization of a week of CGM data extracted from a retrospective subject (Ohio544) obtained using AGATA’s utilities. Panel (a) (plot III) shows the daily glucose profile distribution resulting from the combination of every single daily profile. The median daily profile is represented with a black bold line, while the shaded dark blue and the shaded light blue areas highlight the 25th to 75th and the 5th to 95th percentiles, respectively. Panel (b) (plot IV) shows the distribution of CGM rate of change as an histogram (in blue) and the respective fitted Gaussian distribution (in red). Panel (c) (plot V) shows the CVGA plot. Panel (d) (plot VI) shows the GRI plot. Abbreviations: CGM, continuous glucose monitoring; AGATA, Automated Glucose dATa Analysis; CVGA, control variability glucose analysis; GRI, glycemic risk index; BG, blood glucose.

Techniques Used: Control

Figure Legend Snippet: Figure 5. Comparison between CGM data of two representative subjects (Ohio544 and Ohio596) obtained using AGATA’s utilities. Panel (a) (plot III) compares the daily glucose profile distributions highlighted in blue for Ohio544 and in red for Ohio596, in terms of median, 25th to 75th percentile, and 5th to 95th percentile. Panels (b and c) compare the CVGA and GRI of the two subjects where data of Ohio544 and Ohio596 are reported as black circles and gray diamonds, respectively. Abbreviations: CGM, continuous glucose monitoring; AGATA, Automated Glucose dATa Analysis; CVGA, control variability glucose analysis; GRI, glycemic risk index; BG, blood glucose.

Techniques Used: Comparison, Control

other:

Article Title: During natural vision, semantic novelty modulates fixation-related processing in primate cortex
Article Snippet: In the human intracranial dataset, the electrode arrays each contained multiple contacts, which were identified using the iELVis MATLAB toolbox ( ).

Article Title: From statistics to deep learning in single-molecule fluorescence resonance energy transfer analysis.
Article Snippet: MASH-FRET [9,26], a MATLAB-based toolbox, uses the BOBA-FRET’s analysis and simulation framework, while adding workflows for molecule localisation and dynamic characterisation. iSMS [27] and SMART [28] offer graphical interfaces for data analysis and visualisation, and enhance standard HMM by incorporating VB and the Bayesian Information Criterion (BIC) while estimating the number of states.

Software:

Article Title: Alpha‐adrenergic mediated changes in blood pressure variability after hypoxia‐ischaemia in preterm fetal sheep
Article Snippet: .. For beat-to-beat BPV analysis, systolic arterial blood pressure peak amplitudes were extracted using LabVIEW software and imported into a customised physiological data analysis toolbox using MATLAB (MATLAB 2023A; The MathWorks, Inc., Natick, MA, USA) to calculate features of beat-to-beat variability. ..

Clinical Proteomics:

Article Title: Parvalbumin-positive neurons in the medial septum participate in the formation of hippocampal-dependent spatial memory.
Article Snippet: .. The EDF files were exported to MATLAB and then analysis sleep state using an open source MATLAB toolbox (https://data.mendeley.com/datasets/c872f83pdz/4)58 Plasma corticosterone measures Plasma samples were collected approximately 12-15 hours after SD, including from the gentle handling SD group. ..

Article Title: Parvalbumin-positive neurons in the medial septum participate in the formation of hippocampal-dependent spatial memory.
Article Snippet: .. The EDF files were exported to MATLAB and then analysis sleep state using an open source MATLAB toolbox (https://data.mendeley.com/datasets/c872f83pdz/4) Plasma corticosterone measures Plasma samples were collected approximately 12-15 hours after SD, including from the gentle handling SD group. ..

Gentle:

Article Title: Parvalbumin-positive neurons in the medial septum participate in the formation of hippocampal-dependent spatial memory.
Article Snippet: .. The EDF files were exported to MATLAB and then analysis sleep state using an open source MATLAB toolbox (https://data.mendeley.com/datasets/c872f83pdz/4) Plasma corticosterone measures Plasma samples were collected approximately 12-15 hours after SD, including from the gentle handling SD group. ..

Generated:

Article Title: Opposing cortical forces: Alpha slowing and sensorimotor mu acceleration during motor-related BCI training.
Article Snippet: .. Topoplots were generated using the MATLAB toolbox from Víctor Martínez-Cagigal (2025): Topographic EEG/MEG plot (https://www.mathworks.com/matlabcentral/fileexchange/72729-topographic-eeg-meg-plot). https://doi.org/10.1371/journal.pcbi.1014112.g003 PLOS Computational Biology | https://doi.org/10.1371/journal.pcbi.1014112 April 1, 2026 8 / 22 showing a positive association with posterior alpha frequency acceleration with effects ranging from negligible to moderate depending on dataset. ..

Functional Assay:

Article Title: Fronto-Cerebellar Connectivity Disruptions and Functional Reorganization in Friedreich's Ataxia: A Structural and Resting-State fMRI Study.
Article Snippet: .. For functional data, grouplevel analyses were performed using the CONN toolbox in MATLAB (v.R2022a) (Nieto-Castanon and Whitfield-Gabrieli, 2022). ..

Review