Open AccessAnalysis of ALS and normal EMG signals based on empirical mode decomposition
Author(s) -
Mishra Vipin K.,
Bajaj Varun,
Kumar Anil,
Singh Girish Kumar
Publication year - 2016
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2016.0208
Subject(s) - hilbert–huang transform , pattern recognition (psychology) , instantaneous phase , spectral density , frequency modulation , bandwidth (computing) , support vector machine , time–frequency analysis , electromyography , computer science , amyotrophic lateral sclerosis , signal processing , artificial intelligence , speech recognition , mathematics , statistics , telecommunications , neuroscience , medicine , energy (signal processing) , radar , disease , pathology , biology
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 (SM PSD ) and mean of first derivative of instantaneous frequency (MFD IF ) 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.