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Overnight features of transcutaneous carbon dioxide measurement as predictors of metabolic status.

Summary: Objective: To systematically investigate whether overnight features in transcutaneous carbon dioxide () measurements can predict metabolic variables in subject with suspected sleep-disordered breathing. Methods: The features extracted from the signal included the number of abrupt descents p...

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Published in:Artificial Intelligence in Medicine Vol. 42; no. 1; pp. 55 - 66
Main Authors: Virkki A, Polo O, Saaresranta T, Laapotti-Salo A, Gyllenberg M, Aittokallio T
Format: research Journal Article
Published: Elsevier B.V. Jan2008
Online Access:View this record in EBSCOhost
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      jtl: Artificial Intelligence in Medicine
      issn: 09333657
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      dt: Jan2008
      vid: 42
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      pub: Elsevier B.V.
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        105746423
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        2009772166
        10.1016/j.artmed.2007.09.001
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        atl: Overnight features of transcutaneous carbon dioxide measurement as predictors of metabolic status.
      aug:
        au:
          Virkki A
          Polo O
          Saaresranta T
          Laapotti-Salo A
          Gyllenberg M
          Aittokallio T
        affil: Biomathematics Research Group, Department of Mathematics, University of Turku, FIN-20014 Turku, Finland; Turku Centre for Computer Science, Joukahaisenkatu 3-5 B, 6th Floor, FIN-20520 Turku, Finland.
      sug:
        subj:
          Carbon Dioxide Blood
          Sleep Apnea Syndromes Metabolism
          Algorithms
          Blood Gas Monitoring, Transcutaneous
          Blood Pressure
          Female
          Glycated Hemoglobin Analysis
          Lipids Blood
          Male
          Middle Age
          Predictive Value of Tests
          ROC Curve
          Sleep Apnea Syndromes Blood
          Thyrotropin Blood
          Human
          Middle Aged: 45-64 years
          Female
          Male
      ab: Summary: Objective: To systematically investigate whether overnight features in transcutaneous carbon dioxide () measurements can predict metabolic variables in subject with suspected sleep-disordered breathing. Methods: The features extracted from the signal included the number of abrupt descents per hour and attributes that characterize the recovery after such an event. For each outcome variable, the subgroup of the 108 study subjects with the particular variable present was divided into two representative classes, and the optimal features that can predict the classes were learned. Overfitting was avoided by evaluating the classification algorithms using 10-fold cross-validation. Results: signal has a key role in determining the classes of high-density lipoprotein cholesterol and thyroid-stimulating hormone concentrations, and it improves the classification accuracy of glycosylated hemoglobin A1c and fasting plasma glucose values. Conclusions: The features learned from the signal reflected the state of the selected metabolic variables in a subtle, but systematic, way. These findings provide a step towards understanding how metabolic disturbances are connected to carbon dioxide exchange during sleep.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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