title: Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines.
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.
  
  
  METHODS
  In this context, we created a database of biopotentials to advance an automated pain recognition system, determine its theoretical testing quality, and optimize its performance. Eighty-five participants were subjected to painful heat stimuli (baseline, pain threshold, two intermediate thresholds, and pain tolerance threshold) under controlled conditions and the signals of electromyography, skin conductance level, and electrocardiography were collected. A total of 159 features were extracted from the mathematical groupings of amplitude, frequency, stationarity, entropy, linearity, variability, and similarity.
  
  
  RESULTS
  We achieved classification rates of 90.94% for baseline vs. pain tolerance threshold and 79.29% for baseline vs. pain threshold. The most selected pain features stemmed from the amplitude and similarity group and were derived from facial electromyography.
  
  
  CONCLUSIONS
  The machine learning measurement of pain in patients could provide valuable information for a clinical team and thus support the treatment assessment.
authors.affiliation: University of Ulm, Medical Psychology, Department of Psychosomatic Medicine and Psychotherapy, Ulm, Germany.
authors.forename: Sascha
authors.initials: S
authors.lastname: Gruss
authors.affiliation: Massachusetts General Hospital & Harvard Medical School, Department of Neurology, Nerve Injury Unit, Boston, Massachusetts, United States of America.
authors.forename: Roi
authors.initials: R
authors.lastname: Treister
authors.affiliation: Otto-von-Guericke-Universität Magdeburg, Institute for Information Technology and Communications, Magdeburg, Germany.
authors.forename: Philipp
authors.initials: P
authors.lastname: Werner
authors.affiliation: University of Ulm, Medical Psychology, Department of Psychosomatic Medicine and Psychotherapy, Ulm, Germany.
authors.forename: Harald C
authors.initials: HC
authors.lastname: Traue
authors.affiliation: University of Technology Dresden, Department of Clinical Psychology and Psychotherapy, Dresden, Germany.
authors.forename: Stephen
authors.initials: S
authors.lastname: Crawcour
authors.affiliation: Federal University of Uberlândia, Biomedical Engineering Laboratory (BioLab), Uberlândia, Brazil.
authors.forename: Adriano
authors.initials: A
authors.lastname: Andrade
authors.affiliation: University of Ulm, Medical Psychology, Department of Psychosomatic Medicine and Psychotherapy, Ulm, Germany.
authors.forename: Steffen
authors.initials: S
authors.lastname: Walter
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D004576:Electromyography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010146:Pain / Q000175:diagnosis / Q000503:physiopathology; D010147:Pain Measurement / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine