NDLI: Feature extraction from multifractal spectrum of electromyograms for diagnosis of neuromuscular disorders

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IET Signal Processing

IET Wireless Sensor Systems

Micro & Nano Letters

The Journal of Engineering

Feature extraction from multifractal spectrum of electromyograms for diagnosis of neuromuscular disorders

Content Provider	IET Digital Library
Author	Chatterjee, Soumya Roy, Sayanjit Singha Bose, Rohit Pratiher, Sawon
Abstract	In this contribution, a novel technique for discrimination of myopathy, amyotrophic lateral sclerosis (ALS) and healthy electromyograms is proposed using multifractal detrended fluctuation analysis (DFA). Electromyograms are aperiodic and non-stationary electrical signals which represent the complex dynamics of skeletal muscle tissues and nerve cells activities within the human body. In this study, non-linear and dynamic fluctuations of noisy and chaotic electromyograms are analysed using fractal geometry. Electromyography (EMG) signals of myopathy, ALS and healthy disorders were collected from an online existing database and the non-linear dynamics were initially characterised using multifractal DFA. Following this, five novel feature parameters were extracted from the multifractal spectrum of respective EMG signals. Analysis of variance test was conducted on the selected features to examine their statistical significance. Finally, classification of myopathy, ALS and healthy electromyograms was done using support vector machine and k-nearest neighbour classifiers. In this study, four classification tasks are reported and it was observed that the performance of the proposed method is reasonably satisfactory in discriminating different categories of electromyograms. In addition, the proposed method is also found to deliver comparable and even better results in comparison with the existing methods, studied on the same data set.
Starting Page	817
Ending Page	824
Page Count	8
ISSN	17518822
Volume Number	14
e-ISSN	17518830
Issue Number	Issue 7, Sep (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-smt/14/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-smt.2019.0132
Journal	IET Science, Measurement & Technology
Publisher Date	2020-04-22
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	ALS Amyotrophic Lateral Sclerosis Analysis-of-variance Test Bioelectric Signal Biology And Medical Computing Chaotic Electromyograms Complex Dynamics Digital Signal Processing Dynamic Fluctuations Electrical Activity in Neurophysiological Processes Electrodiagnostics And Other Electrical Measurement Technique Electromyography Electromyography Signal EMG Signal Feature Extraction Feature Parameter Feature Selection Fluctuations Fractal Geometry Fractals Healthy Disorders Healthy Electromyograms Human Body K-nearest Neighbour Classifier Knowledge Engineering Technique Medical Disorders Medical Signal Processing Multifractal Detrended Fluctuation Analysis Multifractal DFA Multifractal Spectrum Myopathy Classification Nearest Neighbour Method Nerve Cell Activity Neuromuscular Disorder Diagnosis Neurophysiology Noisy Electromyograms Nonlinear Dynamics Nonlinear Fluctuations Nonstationary Electrical Signal Online Existing Database Probability Theory Selected Feature Signal Classification Signal Processing And Detection Skeletal Muscle Tissues Statistical Significance Statistical Testing Statistics Stochastic Linearised SCUC Support Vector Machine
Content Type	Text
Resource Type	Article
Subject	Atomic and Molecular Physics, and Optics Electrical and Electronic Engineering

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