Pain intensity recognition rates via biopotential feature patterns with support vector machines

Sascha Gruss, Roi Treister, Philipp Werner, Harald C. Traue, Stephen Crawcour, Adriano Andrade, Steffen Walter

Research output: Contribution to journalArticlepeer-review

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. Eightyfive 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. Conclusion The machine learning measurement of pain in patients could provide valuable information for a clinical team and thus support the treatment assessment.

Original languageEnglish
Article numbere0140330
JournalPLoS ONE
Volume10
Issue number10
DOIs
StatePublished - 16 Oct 2015
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2015 Gruss et al This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ASJC Scopus subject areas

  • General Agricultural and Biological Sciences
  • General
  • General Biochemistry, Genetics and Molecular Biology

Fingerprint

Dive into the research topics of 'Pain intensity recognition rates via biopotential feature patterns with support vector machines'. Together they form a unique fingerprint.

Cite this