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- Title
Support Vector Machines to improve physiologic hot flash measures: Application to the ambulatory setting.
- Authors
Thurston, Rebecca C.; Hernandez, Javier; Del Rio, Jose M.; De La Torre, Fernando
- Abstract
Most midlife women have hot flashes. The conventional criterion (≥2 μmho rise/30 s) for classifying hot flashes physiologically has shown poor performance. We improved this performance in the laboratory with Support Vector Machines (SVMs), a pattern classification method. We aimed to compare conventional to SVM methods to classify hot flashes in the ambulatory setting. Thirty-one women with hot flashes underwent 24 h of ambulatory sternal skin conductance monitoring. Hot flashes were quantified with conventional (≥2 μmho/30 s) and SVM methods. Conventional methods had low sensitivity (sensitivity=.57, specificity=.98, positive predictive value (PPV)=.91, negative predictive value (NPV)=.90, F1=.60), with performance lower with higher body mass index (BMI). SVMs improved this performance (sensitivity=.87, specificity=.97, PPV=.90, NPV=.96, F1=.88) and reduced BMI variation. SVMs can improve ambulatory physiologic hot flash measures.
- Publication
Psychophysiology, 2011, Vol 48, Issue 7, p1015
- ISSN
0048-5772
- Publication type
Article
- DOI
10.1111/j.1469-8986.2010.01155.x