CN107049310B - EMG (electromyography) preprocessing method based on empirical mode decomposition - Google Patents

EMG (electromyography) preprocessing method based on empirical mode decomposition Download PDF

Info

Publication number
CN107049310B
CN107049310B CN201710098270.4A CN201710098270A CN107049310B CN 107049310 B CN107049310 B CN 107049310B CN 201710098270 A CN201710098270 A CN 201710098270A CN 107049310 B CN107049310 B CN 107049310B
Authority
CN
China
Prior art keywords
imfs
electromyogram
emg
characteristic parameters
method based
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Fee Related
Application number
CN201710098270.4A
Other languages
Chinese (zh)
Other versions
CN107049310A (en
Inventor
刘宇巍
凌永权
李亚
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong University of Technology
Original Assignee
Guangdong University of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangdong University of Technology filed Critical Guangdong University of Technology
Priority to CN201710098270.4A priority Critical patent/CN107049310B/en
Publication of CN107049310A publication Critical patent/CN107049310A/en
Application granted granted Critical
Publication of CN107049310B publication Critical patent/CN107049310B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/389—Electromyography [EMG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • Veterinary Medicine (AREA)
  • Public Health (AREA)
  • Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Pathology (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
  • Signal Processing (AREA)
  • Artificial Intelligence (AREA)
  • Physiology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Psychiatry (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)

Abstract

The invention discloses an electromyogram preprocessing method based on empirical mode decomposition, which comprises the following steps of: (1) linear phase finite long impulse response filters with different cut-off frequencies are selected to respectively filter electromyogram signals; (2) performing EMD on each filtered signal to obtain IMF corresponding to each signal; (3) and taking the obtained entropy and extreme values of all IMFs as characteristic parameters for electromyogram analysis. The invention provides an improved electromyogram preprocessing method based on empirical mode decomposition, and the number and the types of characteristic parameters are determined by the number of selected filters and the difference of cut-off frequencies, so that the aim of flexibility is fulfilled. The more characteristic parameters, the more information of the electromyogram is available. In subsequent use, the relationship between the characteristic parameters of the electromyogram and the need of detecting the nerve or muscle or other functions can be established, and great reference is provided for medical diagnosis, daily physical examination and the like.

Description

EMG (electromyography) preprocessing method based on empirical mode decomposition
Technical Field
The invention relates to an electromyogram processing technology, in particular to an electromyogram preprocessing method based on empirical mode decomposition.
Background
Electromyography (EMG), is an electronic instrument used to record the electrical activity of muscles when they are resting or contracting, and to examine the excitation and conduction functions of nerves and muscles by electrical stimulation. Electromyography can be used to record nerve and muscle activity to determine its function. The muscle movement has tiny bioelectricity changes, and electromyography is used as a non-invasive detection method to detect the bioelectricity changes. The indexes such as the amplitude, the frequency and the like of the electromyogram can be correspondingly changed during exercise, and the changes are closely related to the exercise mode, the exercise state and the muscle fatigue. Therefore, the application of electromyogram is very wide, and the electromyogram is applied to clinical detection.
However, the traditional electromyogram still contains a lot of noises of human bodies, the effect of the modern method is still poor after the electromyogram signal is preprocessed, so that the electromyogram is limited to be recognized and applied, the electromyogram cannot be directly read in many times, and other tools are needed.
Empirical Mode Decomposition (EMD) is based on the time-domain local features of the signal, so that the decomposition is adaptive and efficient, and is particularly suitable for analyzing the non-stationary nonlinear time-varying process, and the method can clearly distinguish the intrinsic modes of overlapped complex data and mine the intrinsic relation between the data. Has been well applied in many fields. In medicine, analysis is carried out on some electrogram signals by using an EMD method, so that doctors can be helped to better identify physical information of patients, and certain auxiliary effect can be played for diagnosis of the doctors. The method can also be used in daily life, and by analyzing the human body signals obtained by some instruments (such as wearable equipment), the method can effectively warn a user that some diseases or hidden dangers may occur so as to avoid delaying treatment. However, the parameters selected by the EMG processing of the EMG decomposition are fixed, more noise is still mixed, the characteristics are not obvious enough, and the number of the selected characteristics is not flexible enough.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide an EMG preprocessing method based on empirical mode decomposition, which improves the preprocessing effect and further improves the denoising effect of the EMG.
In order to achieve the purpose, the technical scheme of the invention is as follows: a pretreatment method for electromyography based on empirical mode decomposition comprises the following steps: (1) linear phase finite long impulse response filters with different cut-off frequencies are selected to respectively filter electromyogram signals; (2) performing EMD on each filtered signal to obtain IMF corresponding to each signal; (3) and taking the obtained entropy and extreme values of all IMFs as characteristic parameters for electromyogram analysis.
As an improvement of the above technical solution, in the step (2), the method includes: and EMD decomposition is carried out on the filtered signal groups respectively to obtain a group of IMFs of the filtered signal groups.
As an improvement of the above technical solution, the number of the linear phase finite long impulse response filters with different cut-off frequencies is more than one.
As an improvement of the above technical solution, in the step (3), the step of obtaining the entropy and the extremum of the IMF includes:
(301) establishing an X-Y coordinate system, wherein an X axis represents the number of IMFs of each group of signals, a Y axis represents the number of extreme points of the IMFs, connecting all points to obtain a broken line, and taking the Y intercept of each broken line as c and the slope as | m |;
(302) solving the first unconstrained quadratic programming problem, taking J (m, c) as a characteristic parameter,
Figure BDA0001230142060000021
wherein the content of the first and second substances,
Figure BDA0001230142060000022
representing the total number of extreme values of k IMFs, wherein k is the number of IMFs;
(303) establishing an n-order polynomial according to the maximum number m of the single group IMFs of each group of signals, wherein m is less than or equal to 2n<2m, corresponding to each order coefficient of an；
(304) Solving the second unconstrained quadratic programming problem will
Figure BDA0001230142060000023
As a feature vector, the feature vector is,
Figure BDA0001230142060000031
wherein E (k) represents the entropy of the k-th IMF,
Figure BDA0001230142060000032
represents the estimation error of the k-th IMF;
(305) mixing the obtained | m |, c, J (m, c) and
Figure BDA0001230142060000033
as characteristic parameters of electromyography.
Compared with the prior art, the invention provides an improved EMG preprocessing method based on empirical mode decomposition, because the cut-off frequencies of the filters are different, IMFs obtained by EMD decomposition of each processed signal are linearly independent, the entropy and the extreme value used as characteristic parameters are also linearly independent and independent, the number and the types of the characteristic parameters are determined by the number of the selected filters and the difference of the cut-off frequencies, and the flexible purpose is achieved. The more characteristic parameters, the more information of the electromyogram is available. In subsequent use, the relationship between the characteristic parameters of the electromyogram and the need of detecting the nerve or muscle or other functions can be established, and great reference is provided for medical diagnosis, daily physical examination and the like.
Drawings
The structure and advantageous effects of the present invention will be described in detail with reference to the accompanying drawings and the detailed description.
FIG. 1 is an error approximation graph for each electromyogram of the present invention.
FIG. 2 is an entropy diagram corresponding to an intrinsic mode function index of an electromyogram of the present invention.
FIG. 3 is an entropy diagram corresponding to the intrinsic mode functions of electromyography under different noises according to the present invention.
FIG. 4 is a graph of electromyography, filter index versus slope, intercept and error approximation of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantageous technical effects of the present invention clearer, the present invention is described in further detail below with reference to the accompanying drawings and the detailed description. It should be understood that the detailed description and specific examples, while indicating the preferred embodiment of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
The method comprises the steps of preprocessing a electromyogram signal, extracting an envelope of the processed signal by utilizing a cubic spline interpolation method, obtaining a series of Intrinsic Mode Functions (IMFs) of the signal through EMD decomposition, and using extreme values and entropies of the IMFs as features to serve as the basis for further detection and analysis.
The method for preprocessing the electromyogram based on empirical mode decomposition in the embodiment comprises the following steps:
1. the electromyogram signal is preprocessed by flexibly selecting linear phase finite long pulse response filters with different cut-off frequencies to obtain a filtered signal;
2. EMD decomposition is carried out on each group of filtered signals respectively to obtain a group of IMFs of the signals;
3. and establishing an X-Y coordinate system, wherein the X axis represents the number of IMFs of each group of signals, the Y axis represents the number of extreme points of the IMFs, and the points are connected to obtain a broken line. Taking the Y intercept of each section of broken line as c and the slope as | m |;
4. solving a first unconstrained quadratic programming problem (1) by taking J (m, c) as a characteristic parameter;
Figure BDA0001230142060000041
wherein the content of the first and second substances,
Figure BDA0001230142060000042
representing the total number of extreme values of k IMFs, wherein k is the number of IMFs;
5. establishing an n-order polynomial according to the maximum number m of the single group IMFs of each group of signals, wherein m is less than or equal to 2n<2m, corresponding to each order coefficient of an；
6. Solving the second unconstrained quadratic programming problem (2) will
Figure BDA0001230142060000043
As a feature vector; e (k) denotes the entropy of the k-th IMF,
Figure BDA0001230142060000044
represents the estimation error of the k-th IMF;
Figure BDA0001230142060000045
wherein
Figure BDA0001230142060000046
7. Mixing the obtained | m |, c, J (m, c) and
Figure BDA0001230142060000047
as characteristic parameters of electromyography.
Referring to fig. 1, fig. 2, fig. 3 and fig. 4, the inspection effect of the method of the present embodiment is shown.
Appropriate changes and modifications to the embodiments described above will become apparent to those skilled in the art from the disclosure and teachings of the foregoing description. Therefore, the present invention is not limited to the specific embodiments disclosed and described above, and some modifications and variations of the present invention should fall within the scope of the claims of the present invention. Furthermore, although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.

Claims (3)

1. A pretreatment method for electromyography based on empirical mode decomposition is characterized by comprising the following steps:
(1) linear phase finite long impulse response filters with different cut-off frequencies are selected to respectively filter electromyogram signals;
(2) performing EMD on each filtered signal to obtain IMF corresponding to each signal;
(3) using the obtained entropies and extreme values of all IMFs as characteristic parameters for electromyogram analysis, specifically:
(301) establishing an X-Y coordinate system, wherein an X axis represents the number of IMFs of each group of signals, a Y axis represents the number of extreme points of the IMFs, connecting all points to obtain a broken line, and taking the Y intercept of each broken line as c and the slope as | m |;
(302) solving the first unconstrained quadratic programming problem, taking J (m, c) as a characteristic parameter,
Figure FDA0002238378960000011
wherein the content of the first and second substances,
Figure FDA0002238378960000012
representing the total number of extreme values of k IMFs, wherein k is the number of IMFs;
(303) according to each groupEstablishing an n-order polynomial with the maximum number m of the IMFs in the single group of signals, wherein m is less than or equal to 2n<2m, corresponding to each order coefficient of an；
(304) Solving the second unconstrained quadratic programming problem will
Figure FDA0002238378960000013
As a feature vector, the feature vector is,
Figure FDA0002238378960000021
wherein E (k) represents the entropy of the k-th IMF,
Figure FDA0002238378960000022
represents the estimation error of the k-th IMF;
(305) mixing the obtained | m |, c, J (m, c) and
Figure FDA0002238378960000023
as characteristic parameters of electromyography.
2. The EMG preprocessing method based on EMG of claim 1, wherein in step (2), it comprises: and EMD decomposition is carried out on the filtered signal groups respectively to obtain a group of IMFs of the filtered signal groups.
3. The EMG preprocessing method based on EMG of claim 1, wherein the number of said linear phase finite long impulse response filters with different cut-off frequencies is more than one respectively.
CN201710098270.4A 2017-02-22 2017-02-22 EMG (electromyography) preprocessing method based on empirical mode decomposition Expired - Fee Related CN107049310B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710098270.4A CN107049310B (en) 2017-02-22 2017-02-22 EMG (electromyography) preprocessing method based on empirical mode decomposition

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710098270.4A CN107049310B (en) 2017-02-22 2017-02-22 EMG (electromyography) preprocessing method based on empirical mode decomposition

Publications (2)

Publication Number Publication Date
CN107049310A CN107049310A (en) 2017-08-18
CN107049310B true CN107049310B (en) 2020-01-17

Family

ID=59621726

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710098270.4A Expired - Fee Related CN107049310B (en) 2017-02-22 2017-02-22 EMG (electromyography) preprocessing method based on empirical mode decomposition

Country Status (1)

Country Link
CN (1) CN107049310B (en)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6434417B1 (en) * 2000-03-28 2002-08-13 Cardiac Pacemakers, Inc. Method and system for detecting cardiac depolarization
CN1838109A (en) * 2006-04-10 2006-09-27 西安交通大学 Mode parameter recognition method based on experience mode decomposition and Laplace wavelet
CN101991418A (en) * 2009-08-14 2011-03-30 深圳市理邦精密仪器股份有限公司 Method for improving respiratory rate detection accuracy
CN106108897A (en) * 2016-07-20 2016-11-16 西安中科比奇创新科技有限责任公司 A kind of electromyographic signal filtering method based on empirical mode decomposition

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080131020A1 (en) * 2006-12-05 2008-06-05 Ying-Ching Hou Method for enhancing three-dimensionality of electronic images and device of the same

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6434417B1 (en) * 2000-03-28 2002-08-13 Cardiac Pacemakers, Inc. Method and system for detecting cardiac depolarization
CN1838109A (en) * 2006-04-10 2006-09-27 西安交通大学 Mode parameter recognition method based on experience mode decomposition and Laplace wavelet
CN101991418A (en) * 2009-08-14 2011-03-30 深圳市理邦精密仪器股份有限公司 Method for improving respiratory rate detection accuracy
CN106108897A (en) * 2016-07-20 2016-11-16 西安中科比奇创新科技有限责任公司 A kind of electromyographic signal filtering method based on empirical mode decomposition

Also Published As

Publication number Publication date
CN107049310A (en) 2017-08-18

Similar Documents

Publication Publication Date Title
Han et al. Electrocardiogram signal denoising based on empirical mode decomposition technique: an overview
Islam et al. Study and analysis of ecg signal using matlab &labview as effective tools
Nayak et al. Filtering techniques for ECG signal processing
Xu et al. Wavelet-based cascaded adaptive filter for removing baseline drift in pulse waveforms
US7245961B2 (en) ECG electrode characterization and compensation
Andrade et al. The application of the Hilbert spectrum to the analysis of electromyographic signals
Pan et al. Accurate removal of baseline wander in ECG using empirical mode decomposition
Lakhwani et al. Design and comparison of digital filters for removal of baseline wandering from ECG signal
Bajaj et al. A robust approach to denoise ECG signals based on fractional Stockwell transform
Sharma et al. EMG classification using wavelet functions to determine muscle contraction
Jamaluddin et al. Performance of DWT and SWT in muscle fatigue detection
Lenka Time-frequency analysis of non-stationary electrocardiogram signals using Hilbert-Huang Transform
Cataldo et al. A method for optimizing the artifact subspace reconstruction performance in low-density EEG
Elouaham et al. Combination time-frequency and empirical wavelet transform methods for removal of composite noise in EMG signals
Schmidt et al. Filtering of ECG signals distorted by magnetic field gradients during MRI using non-linear filters and higher-order statistics
US8326410B2 (en) Method for automated analysis of submaximal F-waves
CN107049310B (en) EMG (electromyography) preprocessing method based on empirical mode decomposition
Toresano et al. Data acquisition system of 16-channel EEG based on ATSAM3X8E ARM Cortex-M3 32-bit microcontroller and ADS1299
Aung et al. Analysis of EEG signals contaminated with motion artifacts using multiscale modified-distribution entropy
CN105997067B (en) Adaptive electromyography signal detection process method based on fraction Fourier conversion
Lolure et al. Wavelet transform based EMG feature extraction and evaluation using scatter graphs
Pretorius et al. Feature extraction from ECG for classification by artificial neural networks
CN113057655A (en) Recognition method, recognition system and detection system for electroencephalogram signal interference
Li et al. sEMG signal filtering study using synchrosqueezing wavelet transform with differential evolution optimized threshold
Nagasirisha et al. EMG Signal denoising using adaptive filters through hybrid optimization algorithms

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20200117

CF01 Termination of patent right due to non-payment of annual fee