Classification of EMG Signals Using Eigenvalue Decomposition-Based Time-Frequency Representation

Electromyogram (EMG) signals are commonly used by doctors to diagnose abnormality of muscles. Manual analysis of EMG signals is a time-consuming and cumbersome task. Hence, this chapter aims to develop an automated method to detect abnormal EMG signals. First, authors have applied the improved eigenvalue decomposition of Hankel matrix and Hilbert transform (IEVDHM-HT) method to obtain the time-frequency (TF) representation of motor unit action potentials (MUAPs) extracted from EMG signals. Then, the obtained TF matrices are used for features extraction. TF matrix has been sliced into several parts and fractional energy in each slice is computed. A percentile-based slicing is applied to obtain discriminating features. Finally, the features are used as an input to the classifiers such as random forest, least-squares support vector machine, and multilayer perceptron to classify the EMG signals namely, normal and ALS, normal and myopathy, and ALS and myopathy, and achieved accuracy of 83%, 80.8%, and 96.7%, respectively.

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Wavelet analysis for Support Vector Machine classification of motor unit action potentials

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology ◽

10.1109/iembs.2010.5626480 ◽

2010 ◽

Cited By ~ 2

Author(s):

Andrzej P Dobrowolski ◽

Mariusz Wierzbowski ◽

Kazimierz Tomczykiewicz

Keyword(s):

Support Vector Machine ◽

Wavelet Analysis ◽

Motor Unit ◽

Action Potentials ◽

Support Vector ◽

Support Vector Machine Classification ◽

Motor Unit Action Potentials ◽

Motor Unit Action ◽

Unit Action

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Eigenvalue Decomposition of Hankel Matrix-Based Time-Frequency Representation for Complex Signals

Circuits Systems and Signal Processing ◽

10.1007/s00034-018-0834-4 ◽

2018 ◽

Vol 37 (8) ◽

pp. 3313-3329 ◽

Cited By ~ 11

Author(s):

Rishi Raj Sharma ◽

Ram Bilas Pachori

Keyword(s):

Hankel Matrix ◽

Eigenvalue Decomposition ◽

Complex Signals ◽

Time Frequency ◽

Frequency Representation

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Classification of knee-joint vibroarthrographic signals using time-domain and time-frequency domain features and least-squares support vector machine

2009 16th International Conference on Digital Signal Processing ◽

10.1109/icdsp.2009.5201156 ◽

2009 ◽

Cited By ~ 5

Author(s):

Yunfeng Wu ◽

Sridhar Krishnan

Keyword(s):

Support Vector Machine ◽

Knee Joint ◽

Least Squares ◽

Frequency Domain ◽

Time Domain ◽

Support Vector ◽

Time Frequency ◽

Vibroarthrographic Signals

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Support-vector machine and naïve bayes based diagnostic analytic of harmonic source identification

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v20.i1.pp1-8 ◽

2020 ◽

Vol 20 (1) ◽

pp. 1

Author(s):

Mohd Hatta Jopri ◽

Abdul Rahim Abdullah ◽

Jingwei Too ◽

Tole Sutikno ◽

Srete Nikolovski ◽

...

Keyword(s):

Support Vector Machine ◽

Naive Bayes ◽

Current Source ◽

Naïve Bayes ◽

Support Vector ◽

Time Frequency ◽

Frequency Representation ◽

S Transform ◽

Harmonic Voltage ◽

Harmonic Source

A harmonic source diagnostic analytic is a vital to identify the location and type of harmonic source in the power system. This paper introduces a comparison of machine learning (ML) algorithm which are support vector machine (SVM) and Naïve Bayes (NB). Voltage and current features are used as the input for ML are extracted from time-frequency representation (TFR) of S-transform. Several unique cases of harmonic source location are considered, whereas harmonic voltage and harmonic current source type-load are used in the diagnosing process. To identify the best ML, the performance measurement of the propose method including accuracy, specificity, sensitivity, and F-measure are calculated. The adequacy of the proposed methodology is tested and verified on IEEE 4-bust test feeder and each ML algorithm is executed for 10 times due to different partitions and to prevent any overfitting result.

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Classification of Refrigerant Flow Noise of Air Conditioners Based on Continuous Wavelet Transform and Support Vector Machine

Applied Sciences ◽

10.3390/app10113959 ◽

2020 ◽

Vol 10 (11) ◽

pp. 3959

Author(s):

Un-Chang Jeong

Keyword(s):

Support Vector Machine ◽

Wavelet Transform ◽

Continuous Wavelet Transform ◽

Support Vector ◽

Continuous Wavelet ◽

Air Conditioner ◽

Time Frequency ◽

Flow Noise ◽

The Time Domain

This study proposes a classification method that uses the continuous wavelet transform and the support vector machine approach to classify refrigerant flow noises generated in an air conditioner. The air conditioning noise was identified as an abnormal signal by the use of the first- and second-order moments. The start and end times of refrigerant flow noises were identified by detecting the singularities of the continuous wavelet transform coefficient in the time domain and by means of listening to the measured sounds. Further, the time-frequency characteristics of refrigerant flow noise were analyzed with the continuous wavelet transform. For the support vector machine-based classification of refrigerant flow noise in an air conditioner, the grid search method was used to determine kernel hyperparameters. Five-fold cross validation was employed for the application of the support vector machine to the classification of air conditioner refrigerant noise. In addition, measured sound sources were modified based on classified refrigerant flow noise to compare the classification accuracy of a jury test with the results of the support vector machine.

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Support vector machine based optimization of multi-fault classification of gears with evolutionary algorithms from time–frequency vibration data

Measurement ◽

10.1016/j.measurement.2014.04.024 ◽

2014 ◽

Vol 55 ◽

pp. 1-14 ◽

Cited By ~ 51

Author(s):

D.J. Bordoloi ◽

Rajiv Tiwari

Keyword(s):

Support Vector Machine ◽

Evolutionary Algorithms ◽

Fault Classification ◽

Support Vector ◽

Time Frequency ◽

Vibration Data

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TIME-FREQUENCY PROCESSING METHOD OF EPILEPTIC EEG SIGNALS

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237215500155 ◽

2015 ◽

Vol 27 (02) ◽

pp. 1550015 ◽

Cited By ~ 1

Author(s):

Assya Bousbia-Salah ◽

Malika Talha-Kedir

Keyword(s):

Support Vector Machine ◽

Principal Component ◽

Support Vector ◽

Discrete Wavelet ◽

Statistical Features ◽

Eeg Signals ◽

Linear Discriminant ◽

Time Frequency ◽

Eeg Recordings

Wavelet transform decomposition of electroencephalogram (EEG) signals has been widely used for the analysis and detection of epileptic seizure of patients. However, the classification of EEG signals is still challenging because of high nonstationarity and high dimensionality. The aim of this work is an automatic classification of the EEG recordings by using statistical features extraction and support vector machine. From a real database, two sets of EEG signals are used: EEG recorded from a healthy person and from an epileptic person during epileptic seizures. Three important statistical features are computed at different sub-bands discrete wavelet and wavelet packet decomposition of EEG recordings. In this study, to select the best wavelet for our application, five wavelet basis functions are considered for processing EEG signals. After reducing the dimension of the obtained data by linear discriminant analysis and principal component analysis (PCA), feature vectors are used to model and to train the efficient support vector machine classifier. In order to show the efficiency of this approach, the statistical classification performances are evaluated, and a rate of 100% for the best classification accuracy is obtained and is compared with those obtained in other studies for the same dataset. However, this method is not meant to replace the clinician but can assist him for his diagnosis and reinforce his decision.

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Filtering and analyzing normal and abnormal electromyogram signals

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v20.i1.pp176-184 ◽

2020 ◽

Vol 20 (1) ◽

pp. 176

Author(s):

S. Elouaham ◽

A. Dliou ◽

Mostafa Laaboubi ◽

R. Latif ◽

N. Elkamoun ◽

...

Keyword(s):

Ensemble Empirical Mode Decomposition ◽

Time Frequency ◽

Motor Unit Action Potentials ◽

Mode Decomposition ◽

Motor Unit Action ◽

Adaptive Noise ◽

Optimal Kernel ◽

Important Measurement ◽

Unit Action

The electromyogram (EMG) is an important measurement to assess the health of muscles and the nerve cells that control them. The appearance of noise in electromyography (EMG) signals may unquestionably minimize the efficiency of the analysis of the signal. The denoising techniques are inevitable for minimizing noise affecting the EMG signals; these methods are Complete Ensemble Empirical Mode Decompositions with Adaptive Noise (CEEMDAN) and the Ensemble Empirical Mode Decomposition (EEMD). After that, we analyze these signals by time-frequency techniques as Adaptive Optimal Kernel (AOK) and Choi-Williams. Firstly, the obtained results illustrate the effectiveness of the CEEMDAN that permits reducing noise that interferes with normal and abnormal EMG signals with higher resolution than other techniques used as EEMD. Secondly, they show that the AOK technique is adapted to the detection and classification of these types of normal and abnormal EMG signals by the good localization of the Motor Unit Action Potentials (MUAPs) in the time-frequency plan. This paper shows the efficiency of the combination of the AOK and CEEMDAN techniques in analyzing the EMG signals.

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