Support Vector Machines to improve physiologic hot flash measures: application to the ambulatory setting
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Updated: Jun 6, 2026
A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings
Published on: January 22, 2018
Rebecca C Thurston1, Javier Hernandez, Jose M Del Rio
1Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15213, USA. thurstonrc@upmc.edu
Abstract:
Most midlife women have hot flashes. The conventional criterion (≥2 micromho 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 micromho/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.
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