An efficient method for analysis of EMG signals using improved empirical mode decomposition

Mishra, Vijaya Nath; Bajaj, Varun; Kumar, Arun; Sharma, Dheeraj; Singh, Girish Kumar

doi:10.1016/j.aeue.2016.12.008

Cited by 57 publications

(26 citation statements)

References 21 publications

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“…For high frequency IMFs a smaller window sized median filter is applied whereas the window size is increased for the lower frequency IMFs. These variable window median filters allow eliminating noise in high frequency components while keeping the information of lower frequency components intact [20,21].…”

Section: Memd With Median Filtermentioning

confidence: 99%

“…The method links each window size with the whole data by means of using the number of the IMFs; moreover it also links the window size with each IMF itself via the entropy of that given IMF. A fixed window size per IMF [21] provides a means to improve the data by removing as much impulse noise as possible, whereas this new method applied in MEMD additionally provides a direct connection between the entropy of each IMF and the window size for the median filter. As it will be explained further within this paper as part of the experiment and the analysis, this new method does not only help to eliminate noise and improve mode-mixing [21], but also allows a clear distinction of physically meaningful frequency components of the data.…”

Section: Memd With Median Filtermentioning

confidence: 99%

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An Improved Empirical Mode Decomposition Method Using Variable Window Median Filter for Early Fault Detection in Electric Motors

Karatoprak

Şeker

2019

Mathematical Problems in Engineering

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This paper proposes an improved Empirical Mode Decomposition (EMD) method by using variable window size median filters during the Intrinsic Mode Functions (IMFs) generation. Compared to the traditional EMD, the improved EMD, namely, Median EMD (MEMD), helps to reduce mode-mixing providing an improvement in terms of separating the fundamental frequencies per IMF. The MEMD method applies the EMD to the signal and then applies a variable window size median filter to the resulting IMFs. A narrow window is used for high frequency components where a broader window is used for the lower frequency components. The filtered IMFs are then summed again and another round of EMD is applied to yield the improved MEMD IMFs. A test setup for accelerated aging of bearings in induction motors is used for the comparison of the traditional and the improved EMD methods with the goal of finding potential bearing defects in an induction motor. The potential defect at the early stage is compared with the faulty state and is used to extract the characteristics of the bearing damage that develops gradually. Comparing the EMD and MEMD, it is seen that MEMD is an improvement to EMD in terms of mode-mixing problem. The MEMD method demonstrated to have better performance compared to the traditional EMD for the extraction of the fault features from the healthy operational state of the motor.

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Section: Memd With Median Filtermentioning

confidence: 99%

Section: Memd With Median Filtermentioning

confidence: 99%

An Improved Empirical Mode Decomposition Method Using Variable Window Median Filter for Early Fault Detection in Electric Motors

Karatoprak

Şeker

2019

Mathematical Problems in Engineering

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“…Empirical mode decomposition (EMD) is used to decompose a signal as a finite sum of intrinsic mode functions (IMFs) and has gained considerable interest for signal analysis applications over the last decade [11][12][13]. EMD can perform well in the separation of stationary or nonstationary hybrid signals which are completely overlapped within the time domain by using its adaptivity on different time scales, but it is only effective for high sparsity of the source signals in the frequency domain [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Effective Separation Method for Single-Channel Time-Frequency Overlapped Signals Based on Improved Empirical Wavelet Transform

2019

KSII TIIS

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To improve the separation performance of time-frequency overlapped radar and communication signals from a single channel, this paper proposes an effective separation method based on an improved empirical wavelet transform (EWT) that introduces a fast boundary detection mechanism. The fast boundary detection mechanism can be regarded as a process of searching, difference optimization, and continuity detection of the important local minima in the Fourier spectrum that enables determination of the sub-band boundary and thus allows multiple signal components to be distinguished. An orthogonal empirical wavelet filter bank that was designed for signal adaptive reconstruction is then used to separate the input time-frequency overlapped signals. The experimental results show that if two source components are completely overlapped within the time domain and the spectrum overlap ratio is less than 60%, the average separation performance is improved by approximately 32.3% when compared with the classic EWT; the proposed method also improves the suitability for multiple frequency shift keying (MFSK) and reduces the algorithm complexity.

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“…The former employs needle electrodes and the latter employs surface electrodes to acquire signals known as intramuscular EMG and surface EMG [3]. In addition to the muscle fatigue evaluation, sports science, rehabilitation and the development of the prosthetic device purposes [4] [5], the primary use of the EMG signal is the diagnosis of the neuromuscular diseases [6] [7] [8]. As already mentioned and studied in different researches [8] [9], the most famous neuromuscular diseases of muscles are myopathy and neuropathy.…”

Section: Introductionmentioning

confidence: 99%

Myopathy Detection and Classification Based on the Continuous Wavelet Transform

Belkhou

Achmamad

Jbari

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%.

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An efficient method for analysis of EMG signals using improved empirical mode decomposition

Cited by 57 publications

References 21 publications

An Improved Empirical Mode Decomposition Method Using Variable Window Median Filter for Early Fault Detection in Electric Motors

An Improved Empirical Mode Decomposition Method Using Variable Window Median Filter for Early Fault Detection in Electric Motors

Effective Separation Method for Single-Channel Time-Frequency Overlapped Signals Based on Improved Empirical Wavelet Transform

Myopathy Detection and Classification Based on the Continuous Wavelet Transform

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