A two-stage method for MUAP classification based on EMG decomposition (original) (raw)
Related papers
A novel method for automated EMG decomposition an MUAP classification
Objective: This paper proposes a novel method for the extraction and classification of individual motor unit action potentials (MUAPs) from intramuscular electromyographic signals. Methodology: The proposed method automatically detects the number of template MUAP clusters and classifies them into normal, neuropathic or myopathic. It consists of three steps: (i) preprocessing of electromyogram (EMG) recordings, (ii) MUAP detection and clustering and (iii) MUAP classification. Results: The approach has been validated using a dataset of EMG recordings and an annotated collection of MUAPs. The correct identification rate for MUAP clustering is 93, 95 and 92% for normal, myopathic and neuropathic, respectively. Ninety-one percent of the superimposed MUAPs were correctly identified. The obtained accuracy for MUAP classification is about 86%. Conclusion: The proposed method, apart from efficient EMG decomposition addresses automatic MUAP classification to neuropathic, myopathic or normal classes directly from raw EMG signals. #
A novel method for automated EMG decomposition and MUAP classification
Artificial Intelligence in Medicine, 2006
Objective: This paper proposes a novel method for the extraction and classification of individual motor unit action potentials (MUAPs) from intramuscular electromyographic signals. Methodology: The proposed method automatically detects the number of template MUAP clusters and classifies them into normal, neuropathic or myopathic. It consists of three steps: (i) preprocessing of electromyogram (EMG) recordings, (ii) MUAP detection and clustering and (iii) MUAP classification. Results: The approach has been validated using a dataset of EMG recordings and an annotated collection of MUAPs. The correct identification rate for MUAP clustering is 93, 95 and 92% for normal, myopathic and neuropathic, respectively. Ninety-one percent of the superimposed MUAPs were correctly identified. The obtained accuracy for MUAP classification is about 86%. Conclusion: The proposed method, apart from efficient EMG decomposition addresses automatic MUAP classification to neuropathic, myopathic or normal classes directly from raw EMG signals. #
Automatic Classification of Intramuscular EMG to Recognize Pathologies
2019
This paper proposes to assess the relevance of new automated tools for electromyography (EMG) analysis, in order to differentiate neuropathic from myopathic patterns. The challenge is to define the diagnosis with only one iEMG signal per patient. Our proposed method uses the decomposition of the EMG signal to characterize motor unit action potentials (MUAPs). The decomposition of each iEMG signal is carried out with EMGLAB. For each signal, the decomposition provides a code which is used by the automated classification algorithms.We use here the linear Support Vector Machine (SVM) and the Bagging Trees methods. For the learning process we use several EMG signals and in different parts of the muscle. Only one recorded electromyography EMG signal per subject is used for the diagnostic test. We evaluate the k—fold cross-validation and the confusion matrix for both models. The accuracy is 77.3% for the SVM and 68.2% for the Bagging Trees. These are the first developments of this tool to...
A Multi-Classifier Approach to MUAP Classification for Diagnosis of Neuromuscular Disorders
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2000
The shapes and sounds of isolated motor unit action potentials (MUAPs) in an electromyographic (EMG) signal provide a significant source of information for diagnosis, treatment and management of neuromuscular disorders. These parameters can be analyzed qualitatively by an expert or quantitatively by using pattern recognition techniques. Due to the advantages of quantitative EMG method, developing robust automated MUAP classifiers have been explored and several systems have been developed for this purpose by now, but the accuracy of the existing methods is not high enough to be used in clinical environments. In this paper, a novel classification strategy based on ensemble of support vector machines (SVMs) classifiers in hybrid serial/parallel architecture is proposed to determine the class label (myopathic, neuropathic, or normal) for a given MUAP. The developed system employs both time domain and time-frequency domain features of the MUAPs extracted from an EMG signal using an EMG signal decomposition system. Different classification strategies including single classifier and multiple classifiers with several subsets of features were investigated. Experimental results using a set of real EMG signals showed robust performance of multi-classifier methods proposed here. Of the methods studied, the multi-classifier that uses multiple features sets and a combination of both trainable and nontrainable fusion techniques to aggregate base classifiers showed the best performance with average accuracy of 97% which is significantly higher than the average accuracy of single SVM-based classifier system (i.e., 88%).
Decomposition and quantitative analysis of clinical electromyographic signals
Medical Engineering & Physics, 1999
Procedures for the quantitative analysis of clinical electromyographic (EMG) signals detected simultaneously using selective or micro and non-selective or macro electrodes are presented. The procedures first involve the decomposition of the micro signals and then the quantitative analysis of the resulting motor unit action potential trains (MUAPTs) in conjunction with the associated macro signal. The decomposition procedures consist of a series of algorithms that are successively and iteratively applied to resolve a composite micro EMG signal into its constituent MUAPTs. The algorithms involve the detection of motor unit action potentials (MUAPs), MUAP clustering and supervised classification and they use shape and firing pattern information along with data dependent assignment criteria to obtain robust performance across a variety of EMG signals. The accuracy, extent and speed with which a set of 10 representative 20-30 s, concentric needle detected, micro signals could be decomposed are reported and discussed. The decomposition algorithms had a maximum and average error rate of 2.5% and 0.7%, respectively, on average assigned 88.7% of the detected MUAPs and took between 4 to 8 s. Quantitative analysis techniques involving average micro and macro MUAP shapes, the variability of micro MUAPs shapes and motor unit firing patterns are described and results obtained from analysis of the data set used to evaluate the decomposition algorithms are summarized and discussed.
Journal of biomedical physics & engineering, 2013
The time and frequency features of motor unit action potentials (MUAPs) extracted from electromyographic (EMG) signal provide discriminative information for diagnosis and treatment of neuromuscular disorders. However, the results of conventional automatic diagnosis methods using MUAP features is not convincing yet. The main goal in designing a MUAP characterization system is obtaining high classification accuracy to be used in clinical decision system. For this aim, in this study, a robust classifier is proposed to improve MUAP classification performance in estimating the class label (myopathic, neuropathic and normal) of a given MUAP. The proposed scheme employs both time and time-frequency features of a MUAP along with an ensemble of support vector machines (SVMs) classifiers in hybrid serial/parallel architecture. Time domain features includes phase, turn, peak to peak amplitude, area, and duration of the MUAP. Time-frequency features are discrete wavelet transform coefficients o...
Classification of EMG Signals for Assessment of Neuromuscular Disorders
International Journal of Electronics and Electrical Engineering, 2014
An accurate and computationally efficient means of feature extraction of electromyographic (EMG) signal patterns has been the subject of considerable research effort in recent years. Quantitative analysis of EMG signals provides an important source of information for the classification of neuromuscular disorders. The objective of this study is to discriminate between normal (NOR), myopathic (MYO) and neuropathic (NEURO) subjects. The experiment consisted of 22 pathogenic (11 MYO and 11 NEURO) and 12 healthy persons. The signals were recorded at 30% Maximum Voluntary Contraction (MVC) for 5 seconds. Features of MUAPs extracted in time have been quantitatively analysed. We have used binary SVM for classification. Separation of normal subjects from neuromuscular disease subjects has an accuracy of 83.45%, whereas separation of subjects from the two types of subjects (myopathic and neuropathic) has an accuracy of 68.29% which is again high.
Robust supervised classification of motor unit action potentials
Medical & Biological Engineering & Computing, 1998
AbstractmA certainty-based classification algorithm is described, which comprises part of a clinically used EMG signal decomposition system. This algorithm classifies a candidate motor unit action potential (MUAP) to the motor unit action potential train (MUAPT) that produces the greatest estimated certainty, provided this maximal certainty is above a given threshold. The algorithm is iterative, such that the certainty with which assignments are made increases with each pass through the data, and it has specific stopping criteria. The performance and sensitivity (to the assignment threshold) of the Certainty algorithm and an iterative minimum Euclidean distance (MED) algorithm are compared by classifying sets of MUAPs detected in real concentric needle-detected EMG signals, using a range of assignment thresholds for each algorithm. With regard to MUAP assignment and error rates, the Certainty algorithm consistently provides better mean results and, more importantly, less variable results than the MED algorithm. The Certainty algorithm can provide mean assignment and error rates of 80.8 and 1.5%, respectively, with a maximum error rate of 3.2%; the MED algorithm can provide mean assignment and error rates of 80.3 and 3.3%, respectively, with a maximum error rate of 6.5%. The Certainty algorithm is relatively insensitive to the certainty threshold used, can consistently differentiate between similarly shaped MUAPs from different MUAPTs, and can make correct classifications despite biological shape variability, background noise and signal shape nonstationarity.
Efficient training of neural network models in classification of electromyographic data
1995
THE APPLICATION of artificial neural networks (ANt'N) in the diagnosis of neuromuscular disorders based on electromyography (EMG) has recently been proposed (SCHtZAS et aL, 1990; PATTICHIS, 1992). Artificial neural network models have been trained to diagnose normal (NOR), motor neuron disease (MND) and myopathy (MYO) subjects successfully (PATTICHIS et al., 1990; PATTICHIS, 1992).
—The features of motor unit action potentials(MUAPs) are extracted from electromyographic (EMG) signals which provide information for diagnosis of neuromuscular disorders. Neuromuscular Disorders are classified into two categories Myopathic and Amyotrophic Lateral Sclerosis(ALS). ALS is a progressive neurodegenerative disease that affects nerve cells in the brain and the spinal cord. The progressive degeneration of the motor neurons in ALS eventually leads to their demise. When the motor neurons die, the ability of the brain to initiate and control muscle movement is lost hence the EMG signals of the patient of this disease are characterized by signals that have a increased value of amplitude , thereby increasing the peak to peak value of the signal. On the other hand Myopathies are a group of disorders characterized by a primary structural or functional impairment of skeletal muscle. They usually affect muscle without involving the nervous system, resulting in muscular weakness hence the EMG signals of the patients of this group of disorder are characterized by signals of shorter duration and smaller amplitude. The aim of this study, is to design a automated system which can classify the signals as ALS , Myopathic and Normal.The proposed scheme employs extracting both time and time–frequency features of a MUAP and then providing it to classifier which can classify the signals as ALS, myopathic and normal.In the proposed system, three classifiers are implemented and their results are evaluated out of which Random Forest classification technique provides the highest accuracy of 97.85%.