A continuous wavelet based technique for the analysis of electromyography signals

2020

Bulletin EEI

Self Cite

Automatic detection of neuromuscular disorders performed using electromyography (EMG) has become an interesting domain for many researchers. In this paper, we present an approach to evaluate and classify the non-stationary EMG signals based on discrete wavelet transform (DWT). Most often researches did not consider the effect of DWT factors on the performance of EMG signals classification. This problem is still an interesting unsolved challenge. However, the selection of appropriate mother wavelet and related level decomposition is an essential issue that should be addressed in DWT-based EMG signals classification. The proposed method consists of decomposing a raw EMG signal into different sub-bands. Several statistical features were extracted from each sub-band and six wavelet families were investigated. The feature vector was used as inputs to support vector machine (SVM) classifier for the diagnosis of neuromuscular disorders. The obtained results achieve satisfactory performances with optimal DWT factors using 10-fold cross-validation. From the classification performances, it was found that sym14 is the most suitable mother wavelet at the 8th optimal wavelet level of decomposition. These simulation results demonstrated that the proposed method is very reliable for reducing cost computational time of automated neuromuscular disorders system and removing the redundancy information.

“…According to the principle of CWT, the analyzed signal is convolved with mother wavelet function ψ(t). Hence, the CWT of the EMG signal provided by the following [19,21]:…”

Section: Emg Signal Decomposition Based On Dwtmentioning

confidence: 99%

A comparative study of wavelet families for electromyography signal classification based on discrete wavelet transform

2020

Bulletin EEI

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“…In consequence, the relative energy value, which corresponds to the energy of each TQWT sub-band is given by: (10) The two features, ( 9) and (10), measure the changes in the frequency distribution.…”

Section: Feature Extractionmentioning

confidence: 99%

“…It can also be helpful for expert doctors and neurologists to determine what further treatment would be needed in the event of disease. Moreover, EMG is used in many fields and Int J Artif Intell ISSN: 2252-8938  Automatic amyotrophic lateral sclerosis detection using tunable Q-factor… (Abdelouahad Achmamad) 745 applications such as medicine prosthesis control, rehabilitation, and clinical diagnosis [9][10]. Generally, the use of an invasive electrode is preferable in investigating neuromuscular disorders.…”

Section: Introductionmentioning

confidence: 99%

Automatic amyotrophic lateral sclerosis detection using tunable Q-factor wavelet transform

Belkhou

2020

IJ-AI

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Early diagnosis of amyotrophic lateral sclerosis (ALS) based on electromyography (EMG) is crucial. The processing of a non-stationary EMG signal requires powerful multi-resolution methods. Our study analyzes and classifies the EMG signals. In the present work, we introduce a novel flexible method for classification of EMG signals using tunable Q-factor wavelet transform (TQWT). Different sub-bands generated by the TQWT technique were served to extract useful information related to energy and then the calculated features were selected using a filter selection (FS) method. The effectiveness of the feature selection step resulted not only in the improvement of classification performance but also in reducing the computation time of the classification algorithm. The selected feature subsets were used as inputs to multiple classifier algorithms, namely, k-nearest neighbor (k-NN), least squares support vector machine (LS-SVM) and random forest (RF) for automated diagnosis. The experimental results show better classification measures with k-NN classifier compared with LS-SVM and RF. The robustness of the classification task was tested using a ten-fold cross-validation method. The outcomes of our proposed approach can be exploited to aid clinicians in neuromuscular disorders detection.

“…* denotes the complex conjugate of the mother wavelet function ψ (t), σ is the scale, τ refers to the translation of the wavelet and 1 √ σ is used for energy normalization [2]. Generally, the choice of appropriate scales and an adequate analysing wavelet function depends on the application.…”

Section: A Continuous Wavelet Transformmentioning

confidence: 99%

“…I N the biomedical area, Electromyography (EMG) refers to the study of the electrical activity of the muscle [1]. From a technical side, EMG is a biological signal acquired from muscles with the aim of evaluating their activities [2]. Typically EMG is recorded by invasive and non-invasive techniques.…”

Section: Introductionmentioning

confidence: 99%

Myopathy Detection and Classification Based on the Continuous Wavelet Transform

Belkhou

2019

JCOMSS

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Electromyography (EMG) is the study of the electrical activity of the muscle. This technique is often used in the diagnosis of neuromuscular diseases. Myopathy is one of these cases, which affect the muscle and causes many changes in the electromyography signal characteristics. This paper presents a new method for analysis and classification of normal and myopathy EMG signals based on continuous wavelet transform (CWT). Classification algorithms, namely Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree (DT), Discriminant Analysis (DA) and Naïve Bayes (NB) were used as classifiers in our study. Five Features were extracted from the continuous wavelet analysis and used as inputs to the mentioned classifiers. Comparison between different classification methods developed in this study was made by evaluation of their results based on multiple scalar performances, mainly accuracy, sensitivity, and specificity. Different combinations of features with different kernel functions were discussed to achieve better performances. Results showed that k-NN classifier achieved the best performances with an accuracy value of 93.68%.