Comparison of AR parametric methods with subspace-based methods for EMG signal classification using stand-alone and merged neural network models

Abstract

Abstract:This research introduces an electromyogram (EMG) pattern classification of individual motor unit action potentials (MUPs) from intramuscular electromyographic signals. The presented technique automatically classifies EMG patterns into healthy, myopathic, or neurogenic categories. To extract a feature vector from the EMG signal, we use different autoregressive (AR) parametric methods and subspace-based methods. The proposal was validated using EMG recordings composed of 1200 EMG patterns obtained from 7 healthy, 7 myopathic, and 13 neurogenic-disordered people.A feedforward error backpropagation artificial neural network (FEBANN) and combined neural network (CNN) were used for classification, where the success rate was slightly higher in CNN. Among the different AR and subspace methods used in this study, the highest performance was obtained with the eigenvector method. The following rates were the results achieved by using the CNN. The correct classification rate for EMG patterns was 97% for healthy, 93% for myopathic, and 92% for neurogenic patterns. The obtained accuracy for EMG signal classification is approximately 94% for CNN. The rates for FEBANN were as follows: 97% for healthy patterns, 92% for myopathic patterns, and 91% for neurogenic patterns. The obtained accuracy was 93.3%. By directly using raw EMG signals, EMG classifications of healthy, myopathic, or neurogenic classes are automatically addressed.

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

“…Finally, in [10], the classification accuracy improved to 97.67% due to the use of DWT method for feature extraction and evolutionary SVM for classification. Furthermore, after several years, Bozkurt et al [13] suggested a model for classifying EMG signal through the use of MUSIC method for feature extraction, combined neural network for classification, and the same dataset. This model was proposed in 2016 and it achieved 94% accuracy.…”

“…Bozkurt et al [13] employed several parametric methods and subspace-based methods for EMG recordings composed of normal, neurogenic, and myopathic subjects. A combined neural network (CNN) and FEBANN were employed for classification, and the highest performance was achieved with the eigenvector method.…”

“…Hence, eliminating those factors can enhance the capability of an EMG signal classification system's capability. Recently, several techniques were utilized to obtain efficient features to depict EMG signals for classification [4, 5, 9–11, 13–16]. However, the design of robust and practical computer-aided decision support system is complicated.…”

Hand Movement Classification Using Burg Reflection Coefficients

“…Several authors used autoregressive models and the characteristics of random processes, such as first and second moments, etc., in tasks related to the classification of myopathy or neuropathy [21]. For example, Bozkurt et al reported a 97% classification performance using fifteenth order AR models, Yule–Walker, Burg, covariance, modified covariance, and subspace-based methods to extract features from 1200 sEMG, applying high-resolution and a high-sampling rate invasive electrodes implanted in a bicep brachii muscle [22].…”

Hand Movement Classification Using Burg Reflection Coefficients

“…Several authors use autoregressive models and the characteristics of random processes, such as first and second moments and others, in tasks related with classification of myopathy or neuropathy diseases. For example, Bozkurt et al report 97% in performance using fifteenth order autoregressive models (AR) Yule-Walker, Burg, Covariance, Modified Covariance and subspace base methods to extract features from 1200 sEMG, applying high resolution and high sampling rate in invasive electrodes implanted in a Bicep brachii muscle [16].…”