DWT-based electromyogram signal classification using maximum likelihood-estimated features for neurodiagnostic applications

Jose, Shobha; George, S. Thomas; Roopchand, P. S.

doi:10.1007/s11760-019-01590-6

Cited by 12 publications

(5 citation statements)

References 35 publications

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“…S. Jose et al [ 186 ] developed an automated technique for diagnosing neuromuscular disorders, a standard concentric needle electrode, which has a 0.07 mm 2 leading‐off area was placed at three levels of insertion (deep, medium, and low) in the brachial biceps muscles in five places. Preliminarily processed by DWT and using maximum likelihood estimation (ML‐estimation) to extract features, the ML‐perceptron neural network (PNN) was used to classify.…”

Section: Applicationmentioning

confidence: 99%

“…To detect these analytical dynamic quantities, pressure sensors like force sensing resistors (FSR), textile-based capacitive pressure sensors, and strain gauged transducers are usually utilized. The joint use of EMG and analytical Diagniose diseases [130,131,186,188] Concentric needle electrodes DWT ML- dynamic sensors stimulates improvement in fields like real-time control, gait parameters measurement, and fall detection.…”

Section: Force and Moment Sensorsmentioning

confidence: 99%

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Use of Advanced Materials and Artificial Intelligence in Electromyography Signal Detection and Interpretation

Gao

Gong

Chen

et al. 2022

Advanced Intelligent Systems

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Electromyography (EMG) is an integral part of many biomedical and healthcare applications. It has been used as a metric for tracking rehabilitation progress and identifying diseases that affect muscle activation patterns. Although it is widely used in many disciplines, conventional EMG recording and interpretation techniques lack in providing precise signal detection and robust classification accuracy. In recent years, thanks to advances in both material science and artificial intelligence, EMG detection techniques are improving at a rapid pace. Materials that allow for enhanced biocompatibility have improved the quality of data recorded by electrodes. The use of machine learning algorithms has paved new ways to understand complex EMG signals, triggering diverse, novel, and improved application scenarios within the healthcare framework. To help readers establish a clear picture of the two most important components in EMG technology, i.e., electrodes and algorithms, while catching up with the latest research outcomes, this review article is composed. The article starts by introducing conventional EMG electrode materials and architectures, then explains how state‐of‐the‐art works have improved electrode utilization. Subsequently, EMG signal conditioning and interpretation algorithms are investigated. Finally, current challenges in the research domain and authors’ perspectives are discussed.

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Section: Applicationmentioning

confidence: 99%

Section: Force and Moment Sensorsmentioning

confidence: 99%

Use of Advanced Materials and Artificial Intelligence in Electromyography Signal Detection and Interpretation

Gao

Gong

Chen

et al. 2022

Advanced Intelligent Systems

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“…The iEMG analysis is effective in identifying neuromuscular abnormalities, as evidenced by a plethora of studies on iEMG classification over the last two decades. [1][2][3][4][5][6][7][8][9][10][11][12][13] A computer-based iEMG classifier initially extracts features from raw iEMG signals and then employs a classification algorithm to distinguish these features. A comprehensive account of iEMG feature extraction approaches and the associated classification algorithms has been presented in past studies.…”

Section: Introductionmentioning

confidence: 99%

“…Electromyography (EMG) is the recording of electrical activities from skeletal muscles, and it can be broadly classified into two categories: non‐invasive surface EMG (sEMG) and invasive intramuscular EMG (iEMG). The iEMG analysis is effective in identifying neuromuscular abnormalities, as evidenced by a plethora of studies on iEMG classification over the last two decades 1–13 . A computer‐based iEMG classifier initially extracts features from raw iEMG signals and then employs a classification algorithm to distinguish these features.…”

Section: Introductionmentioning

confidence: 99%

IntramuscularEMGclassifier for detecting myopathy and neuropathy

Jose

George

Samuel

et al. 2022

Int J Imaging Syst Tech

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This article presents an automatic diagnostic system to classify intramuscular electromyography (iEMG) signals, thereby detecting neuromuscular disorders. To this end, we tailored the center symmetric local binary pattern (CSLBP) to analyze one‐dimensional (1‐D) signals. In this approach, the 1‐D CSLBP feature extracted from a decimated iEMG signal is fed to a combination of classifiers, which in turn assigns a set of labels to the signal, and ultimately the signal category is determined by the Boyer‐Moore majority voting (BMMV) algorithm. The proposed framework was investigated with a benchmark iEMG dataset that contains signals recorded from three different muscles: biceps brachii (BB), deltoideus (DE), and vastus medialis (VM). In a repeated 10‐fold cross‐validation, CSLBP‐Combined‐Classifiers‐BMMV (CSLBP‐CC‐BMMV) achieved an average classification accuracy of 92.80%, 94.25%, and 93.71% for the iEMG signals recorded from BB, DE, and VM muscle, respectively. Interestingly, the performance of CSLBP‐CC‐BMMV surpassed the other published approaches and ensemble learning methods that are akin to our scheme in terms of classification accuracy and computational time.

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“…The selection of an optimal feature set is pivotal for improving the classification accuracy [9] , [10] . Therefore, diverse feature sets were explored by various researchers: time domain features [5] , [11] , [12] ; frequency domain features [13] – [16] ; and time-frequency (TF) domain features [17] – [25] . An alternate approach is to combine features from different domains [8] , [26] – [29] .…”

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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DWT-based electromyogram signal classification using maximum likelihood-estimated features for neurodiagnostic applications

Cited by 12 publications

References 35 publications

Use of Advanced Materials and Artificial Intelligence in Electromyography Signal Detection and Interpretation

Use of Advanced Materials and Artificial Intelligence in Electromyography Signal Detection and Interpretation

IntramuscularEMGclassifier for detecting myopathy and neuropathy

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

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