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195 Views | 58 Downloads

Author Notes:

Correspondence: er@gatech.edu

Subjects:

Research Funding:

This study was funded in part by NIH grants UL1TR000454, K24HL077506, R01HL68630, R01AG026255, R01HL125246, R01HL109413, P01HL101398, and K23HL127251; AHA grants 0245115N and 15SDG25310017; and Emory University GCRC MO1-RR00039 and KL2TR000455.

A.N.V., A.S, and G.D.C also thank Medibio Limited for support.

Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Technology
  • Biophysics
  • Engineering, Biomedical
  • Physiology
  • Engineering
  • post-traumatic stress disorder
  • heart rate variability
  • machine learning
  • segmentation
  • electrocardiogram
  • MYOCARDIAL-INFARCTION
  • SLEEP STAGES
  • PTSD
  • TRAUMA
  • PREVALENCE
  • PREDICTORS
  • MORTALITY
  • SYMPTOMS
  • VETERANS
  • IRAQ

Heart rate-based window segmentation improves accuracy of classifying posttraumatic stress disorder using heart rate variability measures

Tools:

Journal Title:

Physiological Measurement

Volume:

Volume 38, Number 6

Publisher:

, Pages 1061-1076

Type of Work:

Article | Post-print: After Peer Review

Abstract:

Objective. Heart rate variability (HRV) characterizes changes in autonomic nervous system function and varies with posttraumatic stress disorder (PTSD). In this study we developed a classifier based on heart rate (HR) and HRV measures, and improved classifier performance using a novel HR-based window segmentation. Approach. Single-channel ECG data were collected from 23 subjects with current PTSD, and 25 control subjects with no history of PTSD over 24 h. RR intervals were derived from these data, cleaned, and used to calculate HR and HRV metrics. These metrics were used as features in a logistic regression classifier. Performance was assessed via repeated random sub-sampling validation. To reduce noise and activity-related effects, we calculated features from five non-overlapping ten-minute quiescent segments of RR intervals defined by lowest HR, as well as random ten-minute segments as a control. Main Results. Using a combination of the four most predictive features derived from quiescent segments we achieved a median area under the receiver operating curve (AUC) of 0.86 on out-of-sample test set data. This was significantly higher than the AUC using 24 h of data (0.72) or random segments (0.67). Significance. These results demonstrate our segmentation approach improves the classification of PTSD from HR and HRV measures, and suggest the potential for tracking PTSD illness severity via objective physiological monitoring. Future studies should prospectively evaluate if classifier output changes significantly with worsening or effective treatment of PTSD.

Copyright information:

© 2017 Institute of Physics and Engineering in Medicine.

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