Analysis of Needle Electromyography Signal in Neuropathy and Myopathy Conditions using Tunable-Q Wavelet Transform
17 Oct 2019-pp 78-82
TL;DR: The results show that the proposed approach is able to distinguish between normal and different pathological electromyography signals better than the conventional time domain analysis and observed that myopathy and neuropathy signals are comprised of high frequency components than low frequency components as compared to normal signal.
Abstract: Analysis of needle electromyography signal is used for the differentiation of neuropathy and myopathy condition from the normal. Amplitude based features such as root mean square and mean absolute value are used to differentiate between normal and pathological signals. Tunable-Q wavelet transform is used to decompose the frequency bands of the signal. Further, the same set of features are used to analyse each frequency bands. The results show that the proposed approach is able to distinguish between normal and different pathological electromyography signals better than the conventional time domain analysis. It is also observed that myopathy and neuropathy signals are comprised of high frequency components than low frequency components as compared to normal signal. The proposed method yields a higher significance with a p-value
References
More filters
TL;DR: In this study, most complete and up-to-date thirty-seven time domain and frequency domain features have been proposed and it is indicated that most time domain features are superfluity and redundancy.
Abstract: Feature extraction is a significant method to extract the useful information which is hidden in surface electromyography (EMG) signal and to remove the unwanted part and interferences. To be successful in classification of the EMG signal, selection of a feature vector ought to be carefully considered. However, numerous studies of the EMG signal classification have used a feature set that have contained a number of redundant features. In this study, most complete and up-to-date thirty-seven time domain and frequency domain features have been proposed to be studied their properties. The results, which were verified by scatter plot of features, statistical analysis and classifier, indicated that most time domain features are superfluity and redundancy. They can be grouped according to mathematical property and information into four main types: energy and complexity, frequency, prediction model, and time-dependence. On the other hand, all frequency domain features are calculated based on statistical parameters of EMG power spectral density. Its performance in class separability viewpoint is not suitable for EMG recognition system. Recommendation of features to avoid the usage of redundant features for classifier in EMG signal classification applications is also proposed in this study.
1,151 citations
"Analysis of Needle Electromyography..." refers methods in this paper
...Time domain features such as mean absolute value, root mean square, simple square integral, Willison amplitude, waveform length are used in general [12]....
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...Amplitude based features such as Root Mean Square (RMS) and Mean Absolute Value (MAV) is used to analyse the signal [12]....
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TL;DR: A discrete-time wavelet transform for which the Q-factor is easily specified and the transform can be tuned according to the oscillatory behavior of the signal to which it is applied, based on a real-valued scaling factor.
Abstract: This paper describes a discrete-time wavelet transform for which the Q-factor is easily specified. Hence, the transform can be tuned according to the oscillatory behavior of the signal to which it is applied. The transform is based on a real-valued scaling factor (dilation-factor) and is implemented using a perfect reconstruction over-sampled filter bank with real-valued sampling factors. Two forms of the transform are presented. The first form is defined for discrete-time signals defined on all of Z. The second form is defined for discrete-time signals of finite-length and can be implemented efficiently with FFTs. The transform is parameterized by its Q-factor and its oversampling rate (redundancy), with modest oversampling rates (e.g., three to four times overcomplete) being sufficient for the analysis/synthesis functions to be well localized.
500 citations
"Analysis of Needle Electromyography..." refers methods in this paper
...Q factor is mathematically expressed as [13]....
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...This transform is designed using two channel filter bank operations, a low-pass filter with scaling factor α and a high-pass filter with scaling factor β [13]....
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TL;DR: This review looks at the studies concerned with the characterisation of neuromuscular pathologies using EMG parameters and focuses on high spatial resolution surface EMG as it is currently the best compromise between the selectivity of needle EMG and the representative nature of classical SEMG.
Abstract: Surface electromyography (SEMG) is still rarely used in clinical settings for the detection and analysis of myoelectric signals. The electromyographic signal detected on the skin surface includes information from a greater proportion of the muscle of interest than conventional clinical EMG, acquired using needle electrodes. SEMG is therefore more representative than the localised, and thus very selective needle EMG signals currently used. However, both reliability and interpretation of surface EMG need to be questioned. This review looks at the studies concerned with the characterisation of neuromuscular pathologies using EMG parameters. After introducing principles and limitations of surface EMG, an overview of the main results obtained in clinical settings is presented and discussed. There is a particular focus on high spatial resolution surface EMG as it is currently the best compromise between the selectivity of needle EMG and the representative nature of classical SEMG. Several perspectives are proposed that underline the fact that surface EMG is an evolving discipline and should be worthy of a place in routine clinical examinations.
139 citations
"Analysis of Needle Electromyography..." refers background or methods in this paper
...Surface Electromyography (sEMG) is the non-invasive method of acquiring electrical activity of muscle by placing electrode on the skin surface [6]....
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...It helps to identify the type of disorder and the site gets affected by the disease [5] [6]....
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TL;DR: The results show that SVM has high anticipation level in the diagnosis of neuromuscular disorders and it is proved that its test performance is high compared with MLP.
Abstract: In this study, the fast Fourier transform (FFT) analysis was applied to EMG signals recorded from ulnar nerves of 59 patients to interpret data. The data of the patients were diagnosed by the neurologists as 19 patients were normal, 20 patients had neuropathy and 20 patients had myopathy. The amount of FFT coefficients had been reduced by using principal components analysis (PCA). This would facilitate calculation and storage of EMG data. PCA coefficients were applied to multilayer perceptron (MLP) and support vector machine (SVM) and both classified systems of performance values were computed. Consequently, the results show that SVM has high anticipation level in the diagnosis of neuromuscular disorders. It is proved that its test performance is high compared with MLP.
129 citations
"Analysis of Needle Electromyography..." refers background or methods in this paper
...The time-frequency analyses methods used in general are Short Time Fourier Transform, wavelet transforms and empirical mode decomposition which decomposes the signal into various subbands and extract features from these subbands [9]....
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...Fast Fourier Transform, [9, 11] is the most widely used frequency domain method to extract features....
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...will increase with increase in the number of fibers in a motor unit [9]....
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TL;DR: A comparison of decomposing surface-recorded EMG signals into the discharge times of single motor units indicates that some decomposition methods are able to replicate many of the findings derived from intramuscular recordings, but additional improvements are required.
64 citations
"Analysis of Needle Electromyography..." refers background in this paper
...It helps to identify the type of disorder and the site gets affected by the disease [5] [6]....
[...]