A hybrid filtering method based on a novel empirical mode decomposition for friction signals

One of the most basic pieces of information gained from dynamic electromyography is accurately defining muscle action and phase timing within the gait cycle. The human gait relies on selective timing and the intensity of appropriate muscle activations for stability, loading, and progression over the supporting foot during stance, and further to advance the limb in the swing phase. A common clinical practice is utilizing a low-pass filter to denoise integrated electromyogram (EMG) signals and to determine onset and cessation events using a predefined threshold. However, the accuracy of the defining period of significant muscle activations via EMG varies with the temporal shift involved in filtering the signals; thus, the low-pass filtering method with a fixed order and cut-off frequency will introduce a time delay depending on the frequency of the signal. In order to precisely identify muscle activation and to determine the onset and cessation times of the muscles, we have explored here onset and cessation epochs with denoised EMG signals using different filter banks: the wavelet method, empirical mode decomposition (EMD) method, and ensemble empirical mode decomposition (EEMD) method. In this study, gastrocnemius muscle onset and cessation were determined in sixteen participants within two different age groups and under two different walking conditions. Low-pass filtering of integrated EMG (iEMG) signals resulted in premature onset (28% stance duration) in younger and delayed onset (38% stance duration) in older participants, showing the time-delay problem involved in this filtering method. Comparatively, the wavelet denoising approach detected onset for normal walking events most precisely, whereas the EEMD method showed the smallest onset deviation. In addition, EEMD denoised signals could further detect pre-activation onsets during a fast walking condition. A comprehensive comparison is discussed on denoising EMG signals using EMD, EEMD, and wavelet denoising in order to accurately define an onset of muscle under different walking conditions.

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An empirical mode decomposition based filtering method for classification of motor-imagery EEG signals for enhancing brain-computer interface

2015 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2015.7280754 ◽

2015 ◽

Cited By ~ 17

Author(s):

Pramod Gaur ◽

Ram Bilas Pachori ◽

Hui Wang ◽

Girijesh Prasad

Keyword(s):

Empirical Mode Decomposition ◽

Motor Imagery ◽

Brain Computer Interface ◽

Computer Interface ◽

Eeg Signals ◽

Filtering Method ◽

Mode Decomposition

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A Comparison of Denoising Methods in Onset Determination in Medial Gastrocnemius Muscle Activations during Stance

Sci ◽

10.3390/sci2020039 ◽

2020 ◽

Vol 2 (2) ◽

pp. 39 ◽

Cited By ~ 1

Author(s):

Jian Zhang ◽

Rahul Soangra ◽

Thurmon E. Lockhart

Keyword(s):

Time Delay ◽

Empirical Mode Decomposition ◽

Gastrocnemius Muscle ◽

Wavelet Denoising ◽

Filtering Method ◽

Mode Decomposition ◽

Low Pass ◽

Eemd Method ◽

Muscle Activations ◽

Stance Duration

One of the most basic pieces of information gained from dynamic electromyography is accurately defining muscle action and phase timing within the gait cycle. The human gait relies on selective timing and the intensity of appropriate muscle activations for stability, loading, and progression over the supporting foot during stance, and further to advance the limb in the swing phase. A common clinical practice is utilizing a low-pass filter to denoise integrated electromyogram (EMG) signals and to determine onset and cessation events using a predefined threshold. However, the accuracy of the defining period of significant muscle activations via EMG varies with the temporal shift involved in filtering the signals; thus, the low-pass filtering method with a fixed order and cut-off frequency will introduce a time delay depending on the frequency of the signal. In order to precisely identify muscle activation and to determine the onset and cessation times of the muscles, we have explored here onset and cessation epochs with denoised EMG signals using different filter banks: the wavelet method, empirical mode decomposition (EMD) method, and ensemble empirical mode decomposition (EEMD) method. In this study, gastrocnemius muscle onset and cessation were determined in sixteen participants within two different age groups and under two different walking conditions. Low-pass filtering of integrated EMG (iEMG) signals resulted in premature onset (28% stance duration) in younger and delayed onset (38% stance duration) in older participants, showing the time-delay problem involved in this filtering method. Comparatively, the wavelet denoising approach detected onset for normal walking events most precisely, whereas the EEMD method showed the smallest onset deviation. In addition, EEMD denoised signals could further detect pre-activation onsets during a fast walking condition. A comprehensive comparison is discussed on denoising EMG signals using EMD, EEMD, and wavelet denoising in order to accurately define an onset of muscle under different walking conditions.

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Mode Mixing Suppression Algorithm for Empirical Mode Decomposition Based on Self-Filtering Method

Radioelectronics and Communications Systems ◽

10.3103/s0735272719090036 ◽

2019 ◽

Vol 62 (9) ◽

pp. 462-473

Author(s):

Longwen Wu ◽

Yupeng Zhang ◽

Yaqin Zhao ◽

Guanghui Ren ◽

Shengyang He

Keyword(s):

Empirical Mode Decomposition ◽

Filtering Method ◽

Mode Decomposition ◽

Mode Mixing

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USING EMPIRICAL MODE DECOMPOSITION WEIGHTING FUNCTIONALS AS TIME SERIES FILTERING COEFFICIENTS FOR PHYSIOLOGICAL RECORDINGS

Advances in Adaptive Data Analysis ◽

10.1142/s179353691250015x ◽

2012 ◽

Vol 04 (01n02) ◽

pp. 1250015 ◽

Cited By ~ 1

Author(s):

JOHN L. AVEN ◽

ARNOLD J. MANDELL ◽

RICHARD COPPOLA

Keyword(s):

Time Series ◽

Lyapunov Exponents ◽

Empirical Mode Decomposition ◽

Temporal Resolution ◽

Fractal Dimensions ◽

Weighting Scheme ◽

Data Smoothing ◽

Filtering Method ◽

Smoothing Methods ◽

Mode Decomposition

We present a method for enhancing signals possessing nonlinear and nonstationary characteristics, which we call weighting functional-empirical mode decomposition (WF-EMD). The filtering method is based upon the empirical mode decomposition (EMD) and utilizes an energy-based weighting scheme to recombine the decomposed modes into a single cleansed version of the signal. The filter has been developed in such a way that no restrictive assumptions about the data are required. Furthermore, the temporal resolution of the data is left unaltered, as it would occur in many common data-smoothing methods. The design of this filter has been influenced by improving the calculation accuracy of dynamical measures, such as fractal dimensions and Lyapunov exponents, of neurodynamical recordings such as those obtained through electroencephalography (EEG) or magnetoencephalography (MEG).

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Ensemble empirical mode decomposition based ECG noise filtering method

2010 International Conference on Machine Learning and Cybernetics ◽

10.1109/icmlc.2010.5581064 ◽

2010 ◽

Cited By ~ 3

Author(s):

Kang-Ming Chang

Keyword(s):

Empirical Mode Decomposition ◽

Ensemble Empirical Mode Decomposition ◽

Noise Filtering ◽

Filtering Method ◽

Mode Decomposition

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GNSS-TS-NRS: An Open-Source MATLAB-Based GNSS Time Series Noise Reduction Software

Remote Sensing ◽

10.3390/rs12213532 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3532

Author(s):

Xiaoxing He ◽

Kegen Yu ◽

Jean-Philippe Montillet ◽

Changliang Xiong ◽

Tieding Lu ◽

...

Keyword(s):

Time Series ◽

Noise Reduction ◽

Empirical Mode Decomposition ◽

Spatial Filtering ◽

Theoretical Background ◽

Filter Method ◽

Human Machine Interaction ◽

Filtering Method ◽

Mode Decomposition ◽

Gnss Time Series

The global navigation satellite system (GNSS) has seen tremendous advances in measurement precision and accuracy, and it allows researchers to perform geodynamics and geophysics studies through the analysis of GNSS time series. Moreover, GNSS time series not only contain geophysical signals, but also unmodeled errors and other nuisance parameters, which affect the performance in the estimation of site coordinates and related parameters. As the number of globally distributed GNSS reference stations increases, GNSS time series analysis software should be developed with more flexible format support, better human–machine interaction, and with powerful noise reduction analysis. To meet this requirement, a new software named GNSS time series noise reduction software (GNSS-TS-NRS) was written in MATLAB and was developed. GNSS-TS-NRS allows users to perform noise reduction analysis and spatial filtering on common mode errors and to visualize GNSS position time series. The functions’ related theoretical background of GNSS-TS-NRS were introduced. Firstly, we showed the theoretical background algorithms of the noise reduction analysis (empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD)). We also developed three improved algorithms based on EMD for noise reduction, and the results of the test example showed our proposed methods with better effect. Secondly, the spatial filtering model supported five algorithms on a separate common model error: The stacking filter method, weighted stacking filter method, correlation weighted superposition filtering method, distance weighted filtering method, and principal component analysis, as well as with batch processing. Finally, the developed software also enabled other functions, including outlier detection, correlation coefficient calculation, spectrum analysis, and distribution estimation. The main goal of the manuscript is to share the software with the scientific community to introduce new users to the GNSS time series noise reduction and application.

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A Feature Extraction Method of Gear Fault Based on the SVD EMD and Morphology

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.433-435.477 ◽

2013 ◽

Vol 433-435 ◽

pp. 477-482 ◽

Cited By ~ 1

Author(s):

Gao Yan Hou ◽

Yong Lv ◽

Hao Huang ◽

Yi Zhu

Keyword(s):

Singular Value Decomposition ◽

Empirical Mode Decomposition ◽

Extraction Method ◽

Singular Value ◽

Background Signal ◽

Feature Extraction Method ◽

Filtering Method ◽

Mode Decomposition ◽

Gear Fault ◽

Value Decomposition

In order to extract the weak signal from strong background signal characteristics, a feature extraction method combined of the singular value decomposition (SVD), empirical mode decomposition (EMD) and mathematical morphology was proposed. The signal got through the singular value decomposition first. Next took the average value of the decomposed main components. And carried on the empirical mode decomposition and selected the main component to summate and refactor. Then morphological difference filter was used to extract the frequency characteristics of the fault signal. The results of numerical simulation test and gear fault simulation experiments show that the proposed method can clearly extract the frequency characteristics of weak signal from strong background signal and noise. Comparison has been done with the results of singular value decomposition (SVD) and morphological filtering method and empirical mode decomposition form of filtering method. It proves the effectiveness of the proposed method.

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