Title of article :
Overnight features of transcutaneous carbon dioxide measurement as predictors of metabolic status
Author/Authors :
Virkki، نويسنده , , Arho and Polo، نويسنده , , Olli and Saaresranta، نويسنده , , Tarja and Laapotti-Salo، نويسنده , , Anne and Gyllenberg، نويسنده , , Mats and Aittokallio، نويسنده , , Tero، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2008
Abstract :
SummaryObjective
tematically investigate whether overnight features in transcutaneous carbon dioxide ( P TcCO 2 ) measurements can predict metabolic variables in subject with suspected sleep-disordered breathing.
s
atures extracted from the P TcCO 2 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.
s
O 2 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.
sions
atures learned from the P TcCO 2 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.
Keywords :
Pattern classification , Predictive mathematical modeling , Sleep-disordered breathing
Journal title :
Artificial Intelligence In Medicine
Journal title :
Artificial Intelligence In Medicine