Analysis of ALS and normal EMG signals based on empirical mode decomposition. Issue 8 (1st November 2016)

• Record Type: Journal Article
• Authors: Mishra, Vipin K.; Bajaj, Varun; Kumar, Anil; Singh, Girish Kumar
• Is Part Of: IET science, measurement & technology. Volume 10:Issue 8(2016)
• Page Start: 963
• Page End: 971
• Publication Date: 2016-11-01
• Subjects: electromyography -- decomposition -- medical signal processing -- diseases -- neurophysiology -- amplitude modulation -- frequency modulation -- least squares approximations -- support vector machines -- signal classification; ALS analysis -- normal EMG signal analysis -- empirical mode decomposition -- electromyogram signal -- neuromuscular disease -- amyotrophic lateral sclerosis -- motor neuron degeneration -- spinal cord -- narrow band intrinsic mode function -- IMF -- EMD technique -- amplitude modulation bandwidth -- frequency modulation bandwidth -- normalised instantaneous frequency -- spectral momentum -- power spectral density -- least square support vector machine classifier; Measurement -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Periodicals; Nanotechnology -- Periodicals; Electromagnetism -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3
• DOI: 10.1049/iet-smt.2016.0208
• Languages: English
• ISSNs: 1751-8822

Abstract

Abstract : Electromyogram (EMG) signals contain a lot of information about the neuromuscular diseases like amyotrophic lateral sclerosis (ALS). ALS progressively degenerates the motor neurons in spinal cord. In this study, a new technique for the analysis of normal and ALS EMG signals is proposed. EMG signals are decomposed into narrow band intrinsic mode functions (IMFs) by using empirical mode decomposition (EMD) technique. The area of complex plot, two bandwidths namely amplitude modulation bandwidth ( B AM ) and frequency modulation bandwidth ( B FM ), normalised instantaneous frequency (IF n ), spectral momentum of power spectral density (SMPSD ) and mean of first derivative of instantaneous frequency (MFDIF ) are extracted from analytic IMFs obtained by EMD technique. These six features are used as input in least square support vector machine classifier for the classification of ALS and normal EMG signals. Experimental results and comparative analysis show that classification performance of the proposed method is better than other existing method in the same database.