An automatic feature extraction and fusion model: application to electromyogram (EMG) signal classification

Hazarika, Ankita; Dutta, Lachit; Boro, M.; Barthakur, Mausumi; Bhuyan, Manabendra

doi:10.1007/s13735-018-0149-z

Cited by 21 publications

(9 citation statements)

References 40 publications

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“…They employed ALS and normal EMG signals and achieved 96.80% accuracy with CWT and CNN. Hazarika et al [40] presented a real-time feature extraction and fusion model for automated classification of electromyographic signals with normal, myopathic, and amyotrophic lateral sclerosis using DWT and canonical correlation analysis. The extracted discriminant features are fed to the k -NN classifier, and 98.80% accuracy is achieved with two-fold cross-validation.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Bagging and Boosting Ensemble Machine Learning Methods for Automated EMG Signal Classification

Yaman

Subaşı

2019

BioMed Research International

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The neuromuscular disorders are diagnosed using electromyographic (EMG) signals. Machine learning algorithms are employed as a decision support system to diagnose neuromuscular disorders. This paper compares bagging and boosting ensemble learning methods to classify EMG signals automatically. Even though ensemble classifiers' efficacy in relation to real-life issues has been presented in numerous studies, there are almost no studies which focus on the feasibility of bagging and boosting ensemble classifiers to diagnose the neuromuscular disorders. Therefore, the purpose of this paper is to assess the feasibility of bagging and boosting ensemble classifiers to diagnose neuromuscular disorders through the use of EMG signals. It should be understood that there are three steps to this method, where the step number one is to calculate the wavelet packed coefficients (WPC) for every type of EMG signal. After this, it is necessary to calculate statistical values of WPC so that the distribution of wavelet coefficients could be demonstrated. In the last step, an ensemble classifier used the extracted features as an input of the classifier to diagnose the neuromuscular disorders. Experimental results showed the ensemble classifiers achieved better performance for diagnosis of neuromuscular disorders. Results are promising and showed that the AdaBoost with random forest ensemble method achieved an accuracy of 99.08%, F-measure 0.99, AUC 1, and kappa statistic 0.99.

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Section: Literature Reviewmentioning

confidence: 99%

Comparison of Bagging and Boosting Ensemble Machine Learning Methods for Automated EMG Signal Classification

Yaman

Subaşı

2019

BioMed Research International

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“…Classification is a popular technique in the field of biomedical condition monitoring and detection purposes. In few recent studies, the classification technique is used for brain disease [18,24] and tumour detection from medical image data [23,36], analysis of chest disease [19], electromyogram (EMG) signal classification [17], pneumonia disease classification [14], detection of epileptic seizure from EEG signals [21], arrhythmia detection [25], even detection of Covid-19 from x-ray [3] to name a few.…”

Section: Introductionmentioning

confidence: 99%

Classification of Hypertensive and Normotensive Subjects Using Bilateral Differential Biopotential Signals

Sarkar

Goswami

Ghosh

2021

Preprint

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Hypertension or high blood pressure is a severe health issue in the modern world, especially in this pandemic scenario, that can cause many heart related diseases or even death, and it is increasing day by day. For this reason, a reliable, automatic and easy to use system for hypertensive subject detection is an important focus for the researchers. Biopotential signals can play a pivotal role in this regard. Though, few strategies were proposed based on electrocardiogram (ECG) or electrodermal (EDA) signals, but those require special circuitry, as well as trained persons. In this article, a method is proposed to classify hypertensive and normotensive subjects using differential biopotential signals. Neither special circuitry, nor much expertise is required for handling this system. It was assumed that progression of rest is dependent upon blood pressure. To serve the purpose, signals were acquired from both hypertensive and normotensive subjects bilaterally for 10 continuous minutes. Result of the random forest (RF) classification establishes that from the analysis of the progression of the bilaterally acquired differential biopotential signals, hypertensive subjects can be distinguished from normotensive subjects.

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“…Technology support vector machine (SVM) [7], [18], [26], [31], [32]; k-nearest neighbor (KNN) [19], [22], [24], [33]; machine learning [11]; quadratic classifier [21]; and random forest decision tree [23], [34]. Moreover, iEMG signals were often preprocessed prior to classification, e.g., [5], [15], [16].…”

Section: Introductionmentioning

confidence: 99%

“…In like manner, several classifiers were attempted to enhance the iEMG classification performance. Typical examples include artificial neural networks (ANN) [12] , [14] , [20] , [27] – [30] ; deep learning algorithm [17] ; neuro-fuzzy system [8] , [13] ; support vector machine (SVM) [7] , [18] , [26] , [31] , [32] ; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k$\end{document} -nearest neighbor (KNN) [19] , [22] , [24] , [33] ; machine learning [11] ; quadratic classifier [21] ; and random forest decision tree [23] , [34] . Moreover, iEMG signals were often preprocessed prior to classification, e.g., [5] , [15] , [16] .…”

Section: Introductionmentioning

confidence: 99%

Robust Classification of Intramuscular EMG Signals to Aid the Diagnosis of Neuromuscular Disorders

Jose

George

Subathra

et al. 2020

IEEE Open J. Eng. Med. Biol.

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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. Methods: 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. Results: 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 = 99.87 ± 0.25, sensitivity (normal) = 99.97 ± 0.13, sensitivity (myopathy) = 99.68 ± 0.95, sensitivity (neuropathy) = 99.76 ± 0.66, specificity (normal) = 99.72 ± 0.61, specificity (myopathy) = 99.98 ± 0.10, and specificity (neuropathy) = 99.96 ± 0.14-surpassing the existing approaches. Conclusions: 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. Index Terms-Fractal dimension, intramuscular electromyography, lifting wavelet transform, local binary pattern, majority vote, multilayer perceptron neural network, neuromuscular disorders. Impact Statement-An iEMG classifier is designed and validated with iEMG signals from healthy, myopathy, and neuropathy subjects. The classifier's outstanding performance is a step toward real-time diagnostic system for neuromuscular diseases.

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An automatic feature extraction and fusion model: application to electromyogram (EMG) signal classification

Cited by 21 publications

References 40 publications

Comparison of Bagging and Boosting Ensemble Machine Learning Methods for Automated EMG Signal Classification

Comparison of Bagging and Boosting Ensemble Machine Learning Methods for Automated EMG Signal Classification

Classification of Hypertensive and Normotensive Subjects Using Bilateral Differential Biopotential Signals

Robust Classification of Intramuscular EMG Signals to Aid the Diagnosis of Neuromuscular Disorders

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