Detection of Myopathy and ALS Electromyograms Employing Modified Window Stockwell Transform

Soumya Chatterjee, Kaniska Samanta, Niladri Ray Choudhury, Rohit Bose

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

In this article, a novel technique for detection and classification of electromyograms is proposed employing
the modified window Stockwell transform. Instead of using a conventional Gaussian window, a modified signal-dependent
adaptive Gaussian window is proposed for improved analysis of electromyograms in joint-time frequency frame. The
parameters of the proposed modified Gaussian window are optimized using a particle swarm optimization algorithm to
maximize the energy concentration measure in time-frequency plane. The electromyograms of myopathy and amyotrophic
lateral sclerosis disorders are subsequently probed using the proposed modified Gaussian window to obtain their respective time–frequency representations. From the transformed signals in the joint-time frequency domain, several new
features are proposed, and student’s t-test is conducted to examine their statistical significance. Using the selected features, classification of myopathy and amyotrophic lateral sclerosis disorders is done using four benchmark classifiers.
Investigations reveal that the highest mean classification accuracy of 98.58% is achieved in this article, which proves the
efficacy of the proposed method for automated diagnosis of neuromuscular disorders
Original languageEnglish
Article number7001204
Pages (from-to)1-4
Number of pages4
JournalIEEE Sensors Letters
Volume3
Issue number7
Early online date5 Jun 2019
DOIs
Publication statusPublished (in print/issue) - 20 Jun 2019

Keywords

  • Sensor signals processing
  • classification
  • electromyograms (EMG)
  • feature extraction
  • signal processing
  • Stockwell transform

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