AGATA

# Inspection

# findNanIslands

function [shortNan, longNan, nanStart, nanEnd] = findNanIslands(data,TH)

Function that locates nan sequences in vector data, and classifies them based on their length (longer or not than the specified threshold TH).

# Inputs

  • data: vector of double (required)
    A vector of double of equally spaced (in time) values;
  • TH: integer (required) A integer defining the threshold, expressed in number of samples, to distinguish between long and short nan sequences.

# Outputs

  • shortNan: vector of integer
    Vector of integer that contains the indices of "short nan" sequences (i.e., sequences shorter than TH consecutive nan samples);
  • longNan: vector of integer
    Vector of integer that contains the indices of "long nan" sequences (i.e., sequences having TH consecutive nan samples or more);
  • nanStart: vector of integer
    Vector of integer containing the start indices of the nan sequences;
  • nanEnd: vector of integer
    Vector of integer containing the last indices of the nan sequences.

# Preconditions

  • data must be a timetable having an homogeneous time grid;
  • data must contain a column named Time and another named glucose;
  • TH must be an integer.

# Reference

  • None

# findHypoglycemicEvents

function hypoglycemicEvents = findHypoglycemicEvents(data, varargin)

Function that finds the hypoglycemic events in a given glucose trace. The definition of hypoglycemic event can be found in Battellino et al. (event begins: at least consecutive 15 minutes < threshold, event ends: at least 15 consecutive minutes > threshold).

# Input

  • data: timetable (required)
    A timetable with columns Time and glucose containing the glucose data to analyze (mg/dl);
  • th: integer (optional)
    An integer with the selected hypoglycemia threshold (in mg/dl) the default value is 70 mg/dl.

# Output

  • hypoglycemicEvents: structure
    A structure containing the information on the hypoglycemic events found by the function with fields:
    • timeStart: vector of datetime
      A vector of datetime containing the starting timestamps of each found hypoglycemic event;
    • timeEnd: vector of datetime
      A vector of datetime containing the ending timestamps of each found hypoglycemic event;
    • duration: vector of integer
      A vector of integer containing the duration (min) of each found hypoglycemic event;
    • meanDuration: double
      Metric, the average duration of the events;
    • duration: double
      Metric, the number of events per week.

# Preconditions

  • data must be a timetable having an homogeneous time grid;
  • data must contain a column named Time and another named glucose.

# Reference

  • Battelino et al., "Continuous glucose monitoring and merics for clinical trials: An international consensus statement", The Lancet Diabetes & Endocrinology, 2022, pp. 1-16. DOI: https://doi.org/10.1016/S2213-8587(22)00319-9.

# findHypoglycemicEventsByLevel

function hypoglycemicEvents = findHypoglycemicEventsByLevel(data)

Function that finds the hypoglycemic events in a given glucose trace classifying them by level, i.e., hypo, level 1 hypo or level 2 hypo. The definition of hypoglycemic event can be found in Battellino et al.

# Input

  • data: timetable (required)
    A timetable with columns Time and glucose containing the glucose data to analyze (mg/dl).

# Output

  • hypoglycemicEvents: structure
    A structure containing the information on the hypoglycemic events found by the function with fields:
    • hypo: structure
      A structure containing the information on the hypo events with fields:
      • timeStart: vector of datetime
        A vector of datetime containing the starting timestamps of each found hypoglycemic event;
      • timeEnd: vector of datetime
        A vector of datetime containing the ending timestamps of each found hypoglycemic event;
      • duration: vector of integer
        A vector of integer containing the duration (min) of each found hypoglycemic event;
      • meanDuration: double
        Metric, the average duration of the events;
      • duration: double
        Metric, the number of events per week.
    • l1: structure
      A structure containing the information on the L1 hypo events with fields:
      • timeStart: vector of datetime
        A vector of datetime containing the starting timestamps of each found L1 hypoglycemic event;
      • timeEnd: vector of datetime
        A vector of datetime containing the ending timestamps of each found L1 hypoglycemic event;
      • duration: vector of integer
        A vector of integer containing the duration (min) of each found L1 hypoglycemic event;
      • meanDuration: double
        Metric, the average duration of the events;
      • duration: double
        Metric, the number of events per week.
    • l2: structure
      A structure containing the information on the L2 hypo events with fields:
      • timeStart: vector of datetime
        A vector of datetime containing the starting timestamps of each found L2 hypoglycemic event;
      • timeEnd: vector of datetime
        A vector of datetime containing the ending timestamps of each found L2 hypoglycemic event;
      • duration: vector of integer
        A vector of integer containing the duration (min) of each found L2 hypoglycemic event;
      • meanDuration: double
        Metric, the average duration of the events;
      • duration: double
        Metric, the number of events per week.

# Preconditions

  • data must be a timetable having an homogeneous time grid;
  • data must contain a column named Time and another named glucose.

# Reference

  • Battelino et al., "Continuous glucose monitoring and merics for clinical trials: An international consensus statement", The Lancet Diabetes & Endocrinology, 2022, pp. 1-16. DOI: https://doi.org/10.1016/S2213-8587(22)00319-9.

# findHyperglycemicEvents

function hyperglycemicEvents = findHyperglycemicEvents(data, varargin)

Function that finds the hyperglycemic events in a given glucose trace. The definition of hyperglycemic event can be found in Battellino et al. (event begins: at least consecutive 15 minutes > threshold, event ends: at least 15 consecutive minutes < threshold).

# Input

  • data: timetable (required)
    A timetable with columns Time and glucose containing the glucose data to analyze (mg/dl);
  • th: integer (optional)
    An integer with the selected hyperglycemia threshold (in mg/dl) the default value is 70 mg/dl.

# Output

  • hyperglycemicEvents: structure
    A structure containing the information on the hyperglycemic events found by the function with fields:
    • timeStart: vector of datetime
      A vector of datetime containing the starting timestamps of each found hyperglycemic event;
    • timeEnd: vector of datetime
      A vector of datetime containing the ending timestamps of each found hyperglycemic event;
    • duration: vector of integer
      A vector of integer containing the duration (min) of each found hyperglycemic event;
    • meanDuration: double
      Metric, the average duration of the events;
    • duration: double
      Metric, the number of events per week.

# Preconditions

  • data must be a timetable having an homogeneous time grid;
  • data must contain a column named Time and another named glucose.

# Reference

  • Battelino et al., "Continuous glucose monitoring and merics for clinical trials: An international consensus statement", The Lancet Diabetes & Endocrinology, 2022, pp. 1-16. DOI: https://doi.org/10.1016/S2213-8587(22)00319-9.

# findHyperglycemicEventsByLevel

function hyperglycemicEvents = findHyperglycemicEventsByLevel(data)

Function that finds the hyperglycemic events in a given glucose trace classifying them by level, i.e., hyper, level 1 hyper or level 2 hyper. The definition of hyperglycemic event can be found in Battellino et al.

# Input

  • data: timetable (required)
    A timetable with columns Time and glucose containing the glucose data to analyze (mg/dl).

# Output

  • hyperglycemicEvents: structure
    A structure containing the information on the hyperglycemic events found by the function with fields:
    • hyper: structure
      A structure containing the information on the hyper events with fields:
      • timeStart: vector of datetime
        A vector of datetime containing the starting timestamps of each found hyperglycemic event;
      • timeEnd: vector of datetime
        A vector of datetime containing the ending timestamps of each found hyperglycemic event;
      • duration: vector of integer
        A vector of integer containing the duration (min) of each found hyperglycemic event;
      • meanDuration: double
        Metric, the average duration of the events;
      • duration: double
        Metric, the number of events per week.
    • l1: structure
      A structure containing the information on the L1 hyper events with fields:
      • timeStart: vector of datetime
        A vector of datetime containing the starting timestamps of each found L1 hyperglycemic event;
      • timeEnd: vector of datetime
        A vector of datetime containing the ending timestamps of each found L1 hyperglycemic event;
      • duration: vector of integer
        A vector of integer containing the duration (min) of each found L1 hyperglycemic event;
      • meanDuration: double
        Metric, the average duration of the events;
      • duration: double
        Metric, the number of events per week.
    • l2: structure
      A structure containing the information on the L2 hypo events with fields:
      • timeStart: vector of datetime
        A vector of datetime containing the starting timestamps of each found L2 hyperglycemic event;
      • timeEnd: vector of datetime
        A vector of datetime containing the ending timestamps of each found L2 hyperglycemic event;
      • duration: vector of integer
        A vector of integer containing the duration (min) of each found L2 hyperglycemic event;
      • meanDuration: double
        Metric, the average duration of the events;
      • duration: double
        Metric, the number of events per week.

# Preconditions

  • data must be a timetable having an homogeneous time grid;
  • data must contain a column named Time and another named glucose.

# Reference

  • Battelino et al., "Continuous glucose monitoring and merics for clinical trials: An international consensus statement", The Lancet Diabetes & Endocrinology, 2022, pp. 1-16. DOI: https://doi.org/10.1016/S2213-8587(22)00319-9.

# findExtendedHypoglycemicEvents

function extendedHypoglycemicEvents = findExtendedHypoglycemicEvents(data)

Function that finds the prolonged hypoglycemic events in a given glucose trace. The definition of extended hypoglycemic event can be found in Battellino et al. (event begins: at least consecutive 120 minutes < threshold mg/dl, event ends: at least 15 consecutive minutes > threshold mg/dl)

# Input

  • data: timetable (required)
    A timetable with columns Time and glucose containing the glucose data to analyze (mg/dl);
  • th: integer (optional)
    An integer with the selected extended hypoglycemia threshold (in mg/dl) the default value is 70 mg/dl.

# Output

  • extendedHypoglycemicEvents: structure
    A structure containing the information on the extended hypoglycemic events found by the function with fields:
    • timeStart: vector of datetime
      A vector of datetime containing the starting timestamps of each found extended hypoglycemic event;
    • timeEnd: vector of datetime
      A vector of datetime containing the ending timestamps of each found extended hypoglycemic event;
    • duration: vector of integer
      A vector of integer containing the duration (min) of each found extended hypoglycemic event;
    • meanDuration: double
      Metric, the average duration of the events;
    • duration: double
      Metric, the number of events per week.

# Preconditions

  • data must be a timetable having an homogeneous time grid;
  • data must contain a column named Time and another named glucose.

# Reference

  • Battelino et al., "Continuous glucose monitoring and merics for clinical trials: An international consensus statement", The Lancet Diabetes & Endocrinology, 2022, pp. 1-16. DOI: https://doi.org/10.1016/S2213-8587(22)00319-9.

# missingGlucosePercentage

function missingGlucosePercentage = missingGlucosePercentage(data)

Function that computes the percentage of missing values in the given glucose trace.

# Input

  • data: timetable (required)
    A timetable with columns Time and glucose containing the glucose data to analyze (mg/dl).

# Output

  • missingGlucosePercentage: double
    Percentage of missing glucose values.

# Preconditions

  • data must be a timetable having an homogeneous time grid;
  • data must contain a column named Time and another named glucose.

# Reference

  • None

# numberDaysOfObservation

function numberDaysOfObservation = numberDaysOfObservation(data)

Function that computes the number of days of observation of the given glucose trace.

# Input

  • data: timetable (required)
    A timetable with columns Time and glucose containing the glucose data to analyze (mg/dl).

# Output

  • numberDaysOfObservation: double
    Number of days of observation.

# Preconditions

  • data must be a timetable having an homogeneous time grid;
  • data must contain a column named Time and another named glucose.

# Reference

  • None

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