Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines.

Journal: PloS one
Published Date: Oct 16, 2015

Abstract

BACKGROUND: The clinically used methods of pain diagnosis do not allow for objective and robust measurement, and physicians must rely on the patient's report on the pain sensation. Verbal scales, visual analog scales (VAS) or numeric rating scales (NRS) count among the most common tools, which are restricted to patients with normal mental abilities. There also exist instruments for pain assessment in people with verbal and / or cognitive impairments and instruments for pain assessment in people who are sedated and automated ventilated. However, all these diagnostic methods either have limited reliability and validity or are very time-consuming. In contrast, biopotentials can be automatically analyzed with machine learning algorithms to provide a surrogate measure of pain intensity.

Authors

  • Sascha Gruss
    University of Ulm, Medical Psychology, Department of Psychosomatic Medicine and Psychotherapy, Ulm, Germany.
  • Roi Treister
    Massachusetts General Hospital & Harvard Medical School, Department of Neurology, Nerve Injury Unit, Boston, Massachusetts, United States of America.
  • Philipp Werner
    Otto-von-Guericke-Universität Magdeburg, Institute for Information Technology and Communications, Magdeburg, Germany.
  • Harald C Traue
    University of Ulm, Medical Psychology, Department of Psychosomatic Medicine and Psychotherapy, Ulm, Germany.
  • Stephen Crawcour
    University of Technology Dresden, Department of Clinical Psychology and Psychotherapy, Dresden, Germany.
  • Adriano Andrade
    Federal University of Uberlândia, Biomedical Engineering Laboratory (BioLab), Uberlândia, Brazil.
  • Steffen Walter
    University of Ulm, Medical Psychology, Department of Psychosomatic Medicine and Psychotherapy, Ulm, Germany.

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