Robust Classification of Intramuscular EMG Signals to Aid the Diagnosis of Neuromuscular Disorders
<italic>Goal:</italic> This article presents the design and validation of an accurate automatic diagnostic system to classify intramuscular EMG (iEMG) signals into healthy, myopathy, or neuropathy categories to aid the diagnosis of neuromuscular diseases. <italic>Methods:</itali...
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2020-01-01
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| author | Shobha Jose S. Thomas George M. S. P. Subathra Vikram Shenoy Handiru Poornaselvan Kittu Jeevanandam Umberto Amato Easter Selvan Suviseshamuthu |
| author_facet | Shobha Jose S. Thomas George M. S. P. Subathra Vikram Shenoy Handiru Poornaselvan Kittu Jeevanandam Umberto Amato Easter Selvan Suviseshamuthu |
| author_sort | Shobha Jose |
| collection | DOAJ |
| description | <italic>Goal:</italic> This article presents the design and validation of an accurate automatic diagnostic system to classify intramuscular EMG (iEMG) signals into healthy, myopathy, or neuropathy categories to aid the diagnosis of neuromuscular diseases. <italic>Methods:</italic> First, an iEMG signal is decimated to produce a set of “disjoint” downsampled signals, which are decomposed by the lifting wavelet transform (LWT). The Higuchi's fractal dimensions (FDs) of LWT coefficients in the subbands are computed. The FDs of LWT subband coefficients are fused with one-dimensional local binary pattern derived from each downsampled signal. Next, a multilayer perceptron neural network (MLPNN) determines the class labels of downsampled signals. Finally, the sequence of class labels is fed to the Boyer-Moore majority vote (BMMV) algorithm, which assigns a class to every iEMG signal. <italic>Results:</italic> The MLPNN-BMMV classifier was experimented with 250 iEMG signals belonging to three categories. The performance of the classifier was validated in comparison with state-of-the-art approaches. The MLPNN-BMMV has resulted in impressive performance measures (%) using a 10-fold cross-validation—accuracy = <inline-formula><tex-math notation="LaTeX">$99.87\pm 0.25$</tex-math></inline-formula>, sensitivity (normal) = <inline-formula><tex-math notation="LaTeX">$99.97\pm 0.13$</tex-math></inline-formula>, sensitivity (myopathy) = <inline-formula><tex-math notation="LaTeX">$99.68\pm 0.95$</tex-math></inline-formula>, sensitivity (neuropathy) = <inline-formula><tex-math notation="LaTeX">$99.76\pm 0.66$</tex-math></inline-formula>, specificity (normal) = <inline-formula><tex-math notation="LaTeX">$99.72\pm 0.61$</tex-math></inline-formula>, specificity (myopathy) = <inline-formula><tex-math notation="LaTeX">$99.98\pm 0.10$</tex-math></inline-formula>, and specificity (neuropathy) = <inline-formula><tex-math notation="LaTeX">$99.96\pm 0.14$</tex-math></inline-formula>—surpassing the existing approaches. <italic>Conclusions:</italic> A future research direction is to validate the classifier performance with diverse iEMG datasets, which would lead to the design of an affordable real-time expert system for neuromuscular disorder diagnosis. |
| format | Article |
| id | doaj-art-51a6e63fdf714287871747c85712acf2 |
| institution | Kabale University |
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| language | English |
| publishDate | 2020-01-01 |
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| spelling | doaj-art-51a6e63fdf714287871747c85712acf22025-08-20T03:32:46ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762020-01-01123524210.1109/OJEMB.2020.30171309169782Robust Classification of Intramuscular EMG Signals to Aid the Diagnosis of Neuromuscular DisordersShobha Jose0S. Thomas George1https://orcid.org/0000-0003-0304-495XM. S. P. Subathra2Vikram Shenoy Handiru3https://orcid.org/0000-0001-7460-5107Poornaselvan Kittu Jeevanandam4Umberto Amato5Easter Selvan Suviseshamuthu6https://orcid.org/0000-0002-8584-5947School of Engineering and Technology, Karunya Institute of Technology and Sciences, Coimbatore, IndiaSchool of Engineering and Technology, Karunya Institute of Technology and Sciences, Coimbatore, IndiaSchool of Engineering and Technology, Karunya Institute of Technology and Sciences, Coimbatore, IndiaCenter for Mobility and Rehabilitation Engineering Research, Kessler Foundation, West Orange, NJ, USAData Business Group, Accenture, Bridgewater, NJ, USAIstituto di Scienze Applicate e Sistemi Intelligenti ‘Eduardo Caianiello,’ Consiglio Nazionale delle Ricerche, Napoli, ItalyCenter for Mobility and Rehabilitation Engineering Research, Kessler Foundation, West Orange, NJ, USA<italic>Goal:</italic> This article presents the design and validation of an accurate automatic diagnostic system to classify intramuscular EMG (iEMG) signals into healthy, myopathy, or neuropathy categories to aid the diagnosis of neuromuscular diseases. <italic>Methods:</italic> First, an iEMG signal is decimated to produce a set of “disjoint” downsampled signals, which are decomposed by the lifting wavelet transform (LWT). The Higuchi's fractal dimensions (FDs) of LWT coefficients in the subbands are computed. The FDs of LWT subband coefficients are fused with one-dimensional local binary pattern derived from each downsampled signal. Next, a multilayer perceptron neural network (MLPNN) determines the class labels of downsampled signals. Finally, the sequence of class labels is fed to the Boyer-Moore majority vote (BMMV) algorithm, which assigns a class to every iEMG signal. <italic>Results:</italic> The MLPNN-BMMV classifier was experimented with 250 iEMG signals belonging to three categories. The performance of the classifier was validated in comparison with state-of-the-art approaches. The MLPNN-BMMV has resulted in impressive performance measures (%) using a 10-fold cross-validation—accuracy = <inline-formula><tex-math notation="LaTeX">$99.87\pm 0.25$</tex-math></inline-formula>, sensitivity (normal) = <inline-formula><tex-math notation="LaTeX">$99.97\pm 0.13$</tex-math></inline-formula>, sensitivity (myopathy) = <inline-formula><tex-math notation="LaTeX">$99.68\pm 0.95$</tex-math></inline-formula>, sensitivity (neuropathy) = <inline-formula><tex-math notation="LaTeX">$99.76\pm 0.66$</tex-math></inline-formula>, specificity (normal) = <inline-formula><tex-math notation="LaTeX">$99.72\pm 0.61$</tex-math></inline-formula>, specificity (myopathy) = <inline-formula><tex-math notation="LaTeX">$99.98\pm 0.10$</tex-math></inline-formula>, and specificity (neuropathy) = <inline-formula><tex-math notation="LaTeX">$99.96\pm 0.14$</tex-math></inline-formula>—surpassing the existing approaches. <italic>Conclusions:</italic> A future research direction is to validate the classifier performance with diverse iEMG datasets, which would lead to the design of an affordable real-time expert system for neuromuscular disorder diagnosis.https://ieeexplore.ieee.org/document/9169782/Fractal dimensionintramuscular electromyographylifting wavelet transformlocal binary patternmajority votemultilayer perceptron neural network |
| spellingShingle | Shobha Jose S. Thomas George M. S. P. Subathra Vikram Shenoy Handiru Poornaselvan Kittu Jeevanandam Umberto Amato Easter Selvan Suviseshamuthu Robust Classification of Intramuscular EMG Signals to Aid the Diagnosis of Neuromuscular Disorders IEEE Open Journal of Engineering in Medicine and Biology Fractal dimension intramuscular electromyography lifting wavelet transform local binary pattern majority vote multilayer perceptron neural network |
| title | Robust Classification of Intramuscular EMG Signals to Aid the Diagnosis of Neuromuscular Disorders |
| title_full | Robust Classification of Intramuscular EMG Signals to Aid the Diagnosis of Neuromuscular Disorders |
| title_fullStr | Robust Classification of Intramuscular EMG Signals to Aid the Diagnosis of Neuromuscular Disorders |
| title_full_unstemmed | Robust Classification of Intramuscular EMG Signals to Aid the Diagnosis of Neuromuscular Disorders |
| title_short | Robust Classification of Intramuscular EMG Signals to Aid the Diagnosis of Neuromuscular Disorders |
| title_sort | robust classification of intramuscular emg signals to aid the diagnosis of neuromuscular disorders |
| topic | Fractal dimension intramuscular electromyography lifting wavelet transform local binary pattern majority vote multilayer perceptron neural network |
| url | https://ieeexplore.ieee.org/document/9169782/ |
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