Neuromuscular disease detection based on feature extraction from time–frequency images of EMG signals employing robust hyperbolic Stockwell transform

Kaniska Samanta, Soumya Chatterjee, Rohit Bose

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
11 Downloads (Pure)

Abstract

In this paper, a novel technique for detection of healthy (H), myopathy, (M) and amyotrophic lateral sclerosis (ALS) electromyography (EMG) signals is proposed employing robust hyperbolic Stockwell transform (HST). HST is an efficient signal processing technique to analyze any nonstationary signal in joint time–frequency (T–F) plane. However, a major issue with HST is the optimum selection of Gaussian window parameters since the resolution in the T–F plane depends on the shape of the window. Considering the aforesaid fact, in this article, a genetic algorithm (GA) based optimized HST is proposed for improved EMG signal analysis in T–F plane. Several novel features were extracted from HST spectrum and features with high statistical significance were selected for classification using several benchmark classifiers. It was observed that optimized HST resulted in better classification accuracy of EMG signals which indicates its potential for clinical applications.

Original languageEnglish
Pages (from-to)1251-1262
Number of pages12
JournalInternational Journal of Imaging Systems and Technology
Volume32
Issue number4
Early online date25 Jan 2022
DOIs
Publication statusPublished (in print/issue) - 5 Jul 2022

Bibliographical note

Publisher Copyright:
© 2022 The Authors. International Journal of Imaging Systems and Technology published by Wiley Periodicals LLC.

Data Access Statement

The data that support the findings of this study areopenly available in ‘EMGLAB’ at http://www.emglab.net/emglab/Signals/N2001/index.html and also at Reference 42 of the manuscript

Keywords

  • classification
  • EMG signals
  • feature selection and genetic algorithm
  • Stockwell transform

Fingerprint

Dive into the research topics of 'Neuromuscular disease detection based on feature extraction from time–frequency images of EMG signals employing robust hyperbolic Stockwell transform'. Together they form a unique fingerprint.

Cite this