Cross-correlation based feature extraction from EMG signals for classification of neuro-muscular diseases

Rohit Bose, Kaniska Samanta, Soumya Chatterjee

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

21 Citations (Scopus)

Abstract

In this contribution, classification of two main neuromuscular diseases namely Myopathy and Neuropathy and Healthy signals is performed using cross-correlation based feature extraction technique. For this purpose, cross-correlation of Healthy, Myopathy and Neuropathy disease EMG signal is done with a reference Healthy signal. Selective features like Hjorth, Adaptive Autoregressive and statistical features comprising mean, standard deviation and power are extracted from the cross-correlated signals. Support Vector Machine(SVM) and k-Nearest Neighbor(kNN) are the two classifiers used for this work. Highest classification accuracy of 100% is obtainedby SVM using Gaussian Radial Basis Function (RBF) as the kernel function with AAR and all combined features as the feature set. For kNN, k=4 yields best result of 100% accuracy using the combined feature set.
Original languageEnglish
Title of host publication2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI)
PublisherIEEE
ISBN (Print)9781509026388
DOIs
Publication statusPublished (in print/issue) - 23 Feb 2017
Event2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI) - Kolkata, India
Duration: 21 Oct 201623 Oct 2016

Conference

Conference2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI)
Abbreviated titleICICPI
Country/TerritoryIndia
CityKolkata
Period21/10/1623/10/16

Keywords

  • Electromyography
  • Neuropathy
  • Myopathy
  • SVM
  • kNN
  • Cross-correlation

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