Identification of contaminant type in surface electromyography (EMG) signals

Paul McCool, Graham D. Fraser, Adrian D. C. Chan, Lykourgos Petropoulakis, John J. Soraghan

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)


The ability to recognize various forms of contaminants in surface electromyography (EMG) signals and to ascertain the overall quality of such signals is important in many EMG-enabled rehabilitation systems. In this paper, new methods for the automatic identification of commonly occurring contaminant types in surface EMG signals are presented. Such methods are advantageous because the contaminant type is typically not known in advance. The presented approach uses support vector machines as the main classification systems. Both simulated and real EMG signals are used to assess the performance of the methods. The contaminants considered include: 1) electrocardiogram interference, 2) motion artifact, 3) power line interference, 4) amplifier saturation and 5) additive white Gaussian noise. Results show that the contaminants can readily be distinguished at lower signal to noise ratios, with a growing degree of confusion at higher signal to noise ratios, where their effects on signal quality are less significant.
Original languageEnglish
Pages (from-to)774-783
Number of pages10
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number4
Early online date21 Jan 2014
Publication statusPublished - 31 Jul 2014


  • signal to noise ratio
  • support vector machines
  • contamination
  • electrocardiography
  • electromyography
  • interference
  • muscles


Dive into the research topics of 'Identification of contaminant type in surface electromyography (EMG) signals'. Together they form a unique fingerprint.

Cite this