Automatic detection of pain in ECG signals

Uri Shaham, Eran Tomer, Zvia Rudich, Shai Tejman-Yarden

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Pain has been increasingly studied as for its importance in patient care and recovery. In the past, several ECG (electrocardiogram) signal processing methods have been studied for the detection and classification of pain without achieving clinical application. In this work we study the ECG signals from healthy adults during acute pain, and perform classification experiments of ECG signals according to existence of pain, as reported by patients. We use the Normalized Compression Distance (NCD), proposed by Cilibrasi and Vitanyi (2005) in order to approximate global similarity between different ECG samples, hence can avoid specifying particular features of the ECG signals to analyze. Our results show detection rate of 93% and above. This work is the first to use this approach for the detection of pain using the ECG signals. The preliminary good results encourage us to continue and study this application.

Original languageEnglish
Title of host publicationInternational Conference on Artificial Intelligence and Pattern Recognition 2009, AIPR 2009
Pages430-434
Number of pages5
StatePublished - 1 Dec 2009
Externally publishedYes
Event2009 International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2009 - Orlando, FL, United States
Duration: 13 Jul 200916 Jul 2009

Publication series

NameInternational Conference on Artificial Intelligence and Pattern Recognition 2009, AIPR 2009

Conference

Conference2009 International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2009
Country/TerritoryUnited States
CityOrlando, FL
Period13/07/0916/07/09

Keywords

  • Classification
  • ECG
  • NCD
  • Pain

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