CN107320097B - Method and device for extracting muscle fatigue features by using electromyographic signal marginal spectrum entropy - Google Patents

Method and device for extracting muscle fatigue features by using electromyographic signal marginal spectrum entropy Download PDF

Info

Publication number
CN107320097B
CN107320097B CN201710516492.3A CN201710516492A CN107320097B CN 107320097 B CN107320097 B CN 107320097B CN 201710516492 A CN201710516492 A CN 201710516492A CN 107320097 B CN107320097 B CN 107320097B
Authority
CN
China
Prior art keywords
muscle fatigue
array
hhe
entropy
marginal
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.)
Active
Application number
CN201710516492.3A
Other languages
Chinese (zh)
Other versions
CN107320097A (en
Inventor
王勇
侯言旭
吴平平
胡保华
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.)
Hefei University of Technology
Original Assignee
Hefei 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 Hefei University of Technology filed Critical Hefei University of Technology
Priority to CN201710516492.3A priority Critical patent/CN107320097B/en
Publication of CN107320097A publication Critical patent/CN107320097A/en
Application granted granted Critical
Publication of CN107320097B publication Critical patent/CN107320097B/en
Active 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
    • A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
    • A61B5/4519—Muscles
    • 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

Landscapes

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

Abstract

The invention discloses a method and a device for extracting muscle fatigue characteristics by utilizing a marginal spectral entropy of an electromyographic signal, wherein the method comprises the following steps: a. storing the collected electromyographic signals into an array sEMG [ CE ]; b. extracting marginal spectrum entropy characteristics of electromyographic signals collected in the array sEMG [ CE ] by using a Hilbert-Huang transform time-frequency analysis method in combination with an information entropy theory; c. storing the marginal spectrum entropy value into an array HHE [ CH ], defining HHE [ CE ] as a frame of data, and storing the marginal spectrum entropy according to a first-in first-out mode by the array HHE [ CH ]; and d, repeating the steps a, b and c, fully storing the array HHE [ CH ], and taking the fully stored array HHE [ CH ] as a muscle fatigue characteristic for judging muscle fatigue. The muscle fatigue is judged by adopting the method, the muscle fatigue index based on the marginal spectrum entropy is calculated quickly, the data length robustness is good, the reliability of muscle fatigue evaluation is strong, and the noise resistance is good.

Description

Method and device for extracting muscle fatigue features by using electromyographic signal marginal spectrum entropy
Technical Field
The invention relates to a method for evaluating muscle fatigue by using an electromyographic signal, in particular to a method for extracting muscle fatigue characteristics by using a marginal spectral entropy of the electromyographic signal and a muscle fatigue extracting device.
Background
Muscle fatigue is a complex physiological phenomenon, the mechanism of occurrence of which is extremely complex, involving the central nervous system, muscle energy metabolism, oxygen supply in blood, etc., and severe muscle fatigue can lead to irrecoverable muscle damage.
The assessment and monitoring of muscle fatigue has important significance for preventing muscle fatigue damage caused by excessive exercise, and is widely applied to the fields of clinical medicine, rehabilitation medicine, sports science, human ergonomics and the like.
The surface electromyogram (sEMG) is a non-stationary time-varying bioelectric signal reflecting the function and state of the nerve and muscle system, and the signal characteristics reflect the muscle fatigue-related processes from different aspects and degrees, so the sEMG plays an important role in the muscle fatigue assessment. Research shows that part of characteristic values of the electromyographic signals have obvious change trends along with the occurrence of muscle fatigue, so one of the key tasks for evaluating the muscle fatigue by using the electromyographic signals is to extract effective characteristic values from the electromyographic signals, and the characteristic values are required to have stable change trends along with the occurrence of the muscle fatigue.
At present, in the problem of evaluating muscle fatigue by using an electromyographic signal, extracting characteristic values of the electromyographic signal is mainly concentrated on a time domain and a frequency domain, wherein time domain characteristics comprise a Root Mean Square (RMS), an electromyographic integral value (IEMG), a mean value (AVG) and the like; the frequency domain features mostly use median frequency MPF and mean frequency MPF. However, the algorithm stability and the clustering performance of the time domain features and the frequency domain features still have a space for improvement.
In addition, nonlinear characteristic values such as fuzzy approximate entropy, sample entropy and wavelet entropy are also applied to muscle fatigue research, and some progress is made. However, most of the current non-linear indexes have the defects of parameter selection in advance and high calculation complexity, for example, more approximate entropies are applied, the algorithm tolerance needs to be determined according to the prior information of the signals, and meanwhile, the signal length influences the stability of the characteristic value. Sample entropy is generated by improving on the basis of approximate entropy, and has better data length robustness, but the undefined condition occurs when the data is short due to error in margin selection.
The fuzzy approximate entropy uses a fuzzy set to replace a binary classification function, so that the fuzzy approximate entropy has better reliability and noise resistance, but the fuzzy approximate entropy needs to calculate a membership function of each distance vector, and the time complexity of the algorithm is increased.
The wavelet entropy needs to select a wavelet basis function in advance, and the effect of the wavelet entropy is often determined to a great extent by the wavelet basis function. Therefore, in the problem of extracting a feature value for muscle fatigue, there is a need for an adaptive feature extraction method with high stability and good algorithm complexity.
The method has the advantages that the method utilizes the electromyographic signals to evaluate muscle fatigue, has special characteristics compared with other application fields of the electromyographic signals, and utilizes the electromyographic signals to evaluate the muscle fatigue, which is different from the detection of the initial point of the electromyographic signals and aims to detect the mutation value of a certain point in the electromyographic signals; the method is also different from the method for diagnosing diseases by utilizing the electromyographic signals, and the disease diagnosis is to extract a characteristic value of the whole section of the signal to be detected so as to maximize the difference between the normal signal and the pathological signal. The muscle fatigue is a process, feature value extraction needs to be carried out on the electromyographic signals of each period of the process, the muscle fatigue is evaluated through the change trend of the feature values, therefore, the feature values need to have a deterministic change trend along with the generation of the muscle fatigue, and the change trend is required to have good consistency and repeatability.
Disclosure of Invention
In view of the above disadvantages of the existing methods for evaluating muscle fatigue, the present invention aims to provide a method for extracting muscle fatigue features by using the electromyographic signal marginal spectral entropy, so that the variation trend of the extracted feature values has good consistency and repeatability.
The invention also provides a device for extracting the muscle fatigue characteristics, so that the variation trend of the extracted characteristic values has good consistency and repeatability.
To this end, the invention provides a method for extracting muscle fatigue characteristics by using electromyographic signal marginal spectrum entropy, which comprises the following steps: a. storing the collected electromyographic signals into an array sEMG [ CE ], wherein CE is the data length related to sampling duration and sampling frequency; b. extracting marginal spectrum entropy characteristics of electromyographic signals collected in the array sEMG [ CE ] by using a Hilbert-Huang transform time-frequency analysis method in combination with an information entropy theory; c. storing the marginal spectrum entropy values into an array HHE [ CH ], defining HHE [ CE ] as a frame of data, and storing the marginal spectrum entropy according to a first-in first-out mode by the array HHE [ CH ], wherein CH represents the number of the marginal spectrum entropy for storing electromyographic signals; and d, repeating the steps a, b and c, fully storing the array HHE [ CH ], and taking the fully stored array HHE [ CH ] as a muscle fatigue characteristic for judging muscle fatigue.
Further, the step d further includes: repeating the step CN times after the array HHE [ CH ] is fully stored to obtain CN marginal spectrum entropies; and storing the CN marginal spectrum entropies into the array HHE [ CH ] according to the sequence, overflowing the CN marginal spectrum entropies stored firstly in the array HHE [ CH ] to obtain a new array HHE [ CH ].
Further, the CN value is set according to prior information of muscle fatigue of different people.
Further, whether muscle fatigue occurs is evaluated according to the change trend of the marginal spectrum entropy of the electromyographic signals.
Further, the least square method is adopted to carry out linear fitting on the array HHE [ CH ] of the full-storage state, so that the slope of the linear fitting is obtained, and the slope represents the change trend of the marginal spectrum entropy along with the occurrence of muscle fatigue.
Furthermore, the sampling time length of each time of the collected electromyographic signals is 0.1s-10s, and the sampling frequency is 1024Hz-4096 Hz.
Furthermore, the method also comprises the step of denoising the electromyographic signals stored in the array sEMG [ CE ] by adopting wavelets to remove white noise in the electromyographic signals.
Further, the CH values are set to be 10-30.
Further, the step b includes the following substeps: EMG decomposition is carried out on the electromyographic signals collected in the array sEMG [ CE ] to obtain IMF of the electromyographic signals; then, carrying out Hilbert transformation on each order of IMF to obtain a Hilert-Huang time frequency spectrum of the electromyographic signal; then, performing time integration on the Hilert-Huang time frequency spectrum to obtain a Hilbert marginal spectrum; and calculating the Hilbert marginal spectrum according to an information entropy theory to obtain the marginal spectrum entropy of the myoelectric signal.
According to another aspect of the present invention, there is provided an apparatus for extracting muscle fatigue characteristics, comprising a processor, a memory, the memory having a computer program stored thereon, the processor implementing the following steps when executing the computer program: a. storing the collected electromyographic signals into an array sEMG [ CE ], wherein CE is the data length related to sampling duration and sampling frequency; b. extracting marginal spectrum entropy characteristics of electromyographic signals collected in the array sEMG [ CE ] by using a Hilbert-Huang transform time-frequency analysis method in combination with an information entropy theory; c. storing the marginal spectrum entropy values into an array HHE [ CH ], defining HHE [ CE ] as a frame of data, and storing the marginal spectrum entropy according to a first-in first-out mode by the array HHE [ CH ], wherein CH represents the number of the marginal spectrum entropy for storing electromyographic signals; and d, repeating the steps a, b and c, fully storing the array HHE [ CH ], and taking the fully stored array HHE [ CH ] as a muscle fatigue characteristic for judging muscle fatigue.
The invention utilizes a Hilbert-Huang transform time-frequency analysis method and combines an information entropy theory to extract the marginal spectral entropy characteristic of the electromyographic signal, and the empirical mode decomposition has the characteristic of signal self-adaption, thereby avoiding the selection of algorithm parameters. Muscle fatigue can be assessed by a physician by the rate of change of the marginal spectral entropy. According to the research, the marginal spectrum entropy algorithm is faster than the median frequency and the approximate entropy, has better reliability when used for fatigue, and has stronger anti-noise performance, so that the method can be better used for evaluating muscle fatigue compared with the approximate entropy and the median frequency. And (3) representing the change trend of the marginal spectral entropy accompanying the occurrence of muscle fatigue by using the slope of linear fitting of the marginal spectral entropy per time period within a fixed time length. The method can synthesize the marginal spectrum entropy values of multiple periods of time and avoid the singular point interference change trend with large discrete degree.
The muscle fatigue is judged by adopting the method, the muscle fatigue index based on the marginal spectrum entropy is calculated quickly, the data length robustness is good, the reliability of muscle fatigue evaluation is strong, and the noise resistance is good.
In addition to the objects, features and advantages described above, other objects, features and advantages of the present invention are also provided. The present invention will be described in further detail below with reference to the drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flowchart of a method for extracting muscle fatigue features by using electromyographic signal marginal spectral entropy according to an embodiment of the invention; and
fig. 2 is a flowchart of a method of evaluating muscle fatigue using an electromyographic signal according to an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 and 2 illustrate some embodiments according to the invention.
The invention extracts the marginal spectrum entropy value of electromyographic signals at each time interval in real time, and represents the change trend of the marginal spectrum entropy along with the occurrence of muscle fatigue by calculating the linear fitting slope of the marginal spectrum entropy at each time interval within fixed time, in particular, the muscle fatigue characteristic extraction method comprises the following steps:
s101: storing the collected electromyographic signals into an array sEMG [ CE ];
s103, performing Empirical Mode Decomposition (EMD) on the electromyographic signals of the collected array sEMG [ CE ], obtaining Intrinsic Mode components (IMF) of the electromyographic signals, removing residual items, performing Hilbert transform (Hilbert transform) on each order of IMF to obtain Hilbert-Huang time frequency spectrums of the electromyographic signals, integrating the Hilbert-Huang time frequency spectrums on a time domain to obtain Hilbert marginal spectrums, and calculating the marginal spectrum entropy by using the Hilbert marginal spectrums according to the definition of the information entropy.
S105: and storing the calculated marginal spectrum entropy into an array HHE [ CH ], defining HHE [ CE ] as frame data, and storing the marginal spectrum entropy of the electromyographic signals acquired each time by using the array HHE [ CH ], wherein CH represents the number of the marginal spectrum entropy for storing the electromyographic signals.
S107: repeating the steps until the array HHE [ CH ] is full, performing linear fitting on the array HHE [ CH ] by adopting a least square method to obtain a slope a1 of primary linear fitting, ending the repetition if the slope a1 can be used for judging the muscle fatigue trend, otherwise, continuously repeating the steps, updating the array HHE [ CH ], eliminating CN data which are firstly input into the array HHE [ CH ], leading the rest data to a CN bit, adding newly calculated CN marginal spectrum entropies to the CN bit, performing linear fitting on the updated array HHE [ CH ] by adopting a least square method to obtain a slope a2 of linear fitting, ending the step if the slope a2 can be used for judging the muscle fatigue trend, or else, repeating the step.
The muscle fatigue is judged by adopting the method, the muscle fatigue index based on the marginal spectrum entropy is calculated quickly, the data length robustness is good, the reliability of muscle fatigue evaluation is strong, and the noise resistance is good.
The electromyogram sampling frequency in step S101 may be set to 1024hz to 4096hz, and the sampling duration may be 0.1S to 10S. The number of the marginal spectrum entropies of the electromyographic signals stored in the CH in the step S105 may be set to 10 to 30. The CN data removed in step S107 may be set to 1, that is, the frame shift is 1, and the CN value may be set according to the prior information of muscle fatigue of different people.
Example one
As shown in fig. 2, the method for extracting muscle fatigue characteristics by using electromyogram signal marginal spectrum entropy of the invention comprises the following steps:
1. cleaning skin at the electrode plate-attached part of the extensor carpi radialis longus, removing hairs, attaching the electrode plate to the belly of a muscle, connecting the belly of the muscle with an electromyography acquisition device through a lead wire, setting the sampling frequency to be 2048 Hz, setting the sampling time to be 0.5 second, storing an array sEMG [1024] in a processor through AD conversion, and removing white noise in signals by adopting wavelet denoising to obtain an electromyography sEMG [1024] with a characteristic value to be extracted.
2. Extracting marginal spectrum entropy characteristics of the electromyographic signals collected in the array sEMG [2048], wherein the marginal spectrum entropy is calculated as follows:
a) firstly, performing EMD decomposition on a signal x (t) to obtain an n-order IMF component and a residual quantity:
Figure BDA0001336764410000061
where x (t) is a signal, ci(t) is the natural modal parameter component (IMF), rn(t) is the residual amount.
b) Performing a hilbert transform on each IMF component:
Figure BDA0001336764410000062
in the formula H (c)i(t)) is the Hilbert transform of the IMF.
c) Constructing an analytic signal:
Figure BDA0001336764410000063
then the signal x (t) can be expressed as:
Figure BDA0001336764410000064
taking the real part of the signal, ai (t), f (t),
Figure BDA0001336764410000065
Representing instantaneous amplitude, instantaneous frequency and instantaneous phase, respectively.
d) The Hilbert-Huang time spectrum is defined, representing the time and frequency distribution of signal amplitudes:
Figure BDA0001336764410000066
e) the Hilbert marginal spectrum of the signal is obtained by performing time integration on the above equation (5):
Figure BDA0001336764410000071
by definition of the margin spectrum, for a discrete frequency point f ═ i Δ f, then:
Figure BDA0001336764410000072
wherein n is the number of frequency discrete points of the signal in the analysis frequency band.
f) Then the HHT marginal spectral entropy, according to the definition of information entropy, can be expressed as:
Figure BDA0001336764410000073
in the formula, pi ═ h (i)/∑ h (i) indicates the probability that the i-th frequency corresponds to the amplitude.
To normalize the entropy value to the [0,1] range, then there are:
Figure BDA0001336764410000074
n is the sequence length of h (i).
And storing the marginal spectrum entropy value into an array HHE [ CH ], defining the HHE [ CH ] as frame data, and storing the marginal spectrum entropy of the electromyographic signals acquired each time by using the array HHE [ CH ], wherein CH represents the number of the marginal spectrum entropy values of the electromyographic signals, and in a specific embodiment, CH is 30.
3. Judging whether the array element full flag bit DataFullFlag is 1, if so, updating the array HHE [30], eliminating 1 data which enters the array HHE [30] firstly, leading the rest data to 1 bit, and adding 1 newly calculated marginal spectrum entropy into the last 1 bit; otherwise, the step 4 is carried out.
4. The computed marginal spectral entropies are stored in the array HHE [30] in order and the element count variable is incremented by 1, DL ═ DL + 1.
5. And judging whether the counting variable is larger than a preset element number value CH or not, if so, setting the array element flag bit DataFullflag to be 1, and otherwise, repeating the steps of 2-3.
6. And performing linear fitting on the updated array HHE [30] by adopting a least square method to obtain a slope a of the linear fitting.
7. And (4) evaluating whether muscle fatigue occurs according to the change trend and the slope a of the array HHE, stopping the program if the muscle fatigue occurs, and otherwise repeating the steps 2, 3, 6 and 7.
The present invention also provides an information processing apparatus having stored thereon an executable computer program for implementing the steps shown in fig. 1 or fig. 2 when executed to implement muscle fatigue feature extraction.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A method for extracting muscle fatigue features by using electromyographic signal marginal spectrum entropy is characterized by comprising the following steps:
a. storing the collected electromyographic signals into an array sEMG [ CE ], wherein CE is the data length related to sampling duration and sampling frequency;
b. extracting marginal spectrum entropy characteristics of electromyographic signals collected in the array sEMG [ CE ] by using a Hilbert-Huang transform time-frequency analysis method in combination with an information entropy theory;
c. b, storing the marginal spectrum entropy value obtained in the step b into an array HHE [ CH ], defining HHE [ CE ] as a frame of data, and storing the marginal spectrum entropy according to a first-in first-out mode by the array HHE [ CH ], wherein CH represents the number of the marginal spectrum entropy for storing electromyographic signals; and
d. and repeating the steps a, b and c, fully storing the array HHE [ CH ], and taking the fully stored array HHE [ CH ] as a muscle fatigue characteristic for judging muscle fatigue.
2. The method for extracting muscle fatigue features by using electromyographic signal marginal spectral entropy according to claim 1, wherein the step d further comprises:
after the array HHE [ CH ] is fully stored, repeating the steps a-c for CN times to obtain CN marginal spectrum entropies; and storing the CN marginal spectrum entropies into the array HHE [ CH ] according to the sequence, overflowing the CN marginal spectrum entropies stored firstly in the array HHE [ CH ], and obtaining a new array HHE [ CH ] as a muscle fatigue characteristic for judging muscle fatigue.
3. The method for extracting muscle fatigue features by using electromyographic signal marginal spectral entropy according to claim 2, wherein the CN value is set according to prior information of muscle fatigue of different people.
4. The method for extracting muscle fatigue features by using the marginal spectrum entropy of the electromyographic signals according to claim 1 or 2, wherein whether muscle fatigue is generated is evaluated according to the change trend of the marginal spectrum entropy of the electromyographic signals.
5. The method for extracting muscle fatigue features by utilizing electromyographic signal marginal spectral entropy as claimed in claim 3, wherein a least square method is adopted to perform linear fitting on the array HHE [ CH ] in the full storage state to obtain a slope of the linear fitting, and the slope represents a change trend of the marginal spectral entropy along with the occurrence of muscle fatigue.
6. The method for extracting muscle fatigue features by utilizing electromyographic signal marginal spectral entropy according to claim 1, wherein the sampling time of each time of the collected electromyographic signals is 0.1s-10s, and the sampling frequency is 1024Hz-4096 Hz.
7. The method for extracting muscle fatigue features by utilizing electromyographic signal marginal spectral entropy as claimed in claim 1, further comprising denoising the electromyographic signals stored in the array sEMG [ CE ] by adopting wavelet to remove white noise in the electromyographic signals.
8. The method for extracting muscle fatigue features by using electromyographic signal marginal spectral entropy according to claim 1, wherein the CH values are set to be 10-30.
9. The method for extracting muscle fatigue features by using electromyographic signal marginal spectral entropy according to claim 1, wherein the step b comprises the following sub-steps: EMG decomposition is carried out on the electromyographic signals collected in the array sEMG [ CE ] to obtain IMF of the electromyographic signals; then, carrying out Hilbert transformation on each order of IMF to obtain a Hilbert-Huang time frequency spectrum of the electromyographic signal; then, performing time integration on the Hilbert-Huang time frequency spectrum to obtain a Hilbert marginal spectrum; and calculating the Hilbert marginal spectrum according to an information entropy theory to obtain the marginal spectrum entropy of the myoelectric signal.
10. An apparatus for extracting muscle fatigue features, comprising a processor, a memory, a computer program stored on the memory, the processor when executing the computer program implementing the steps of:
a. storing the collected electromyographic signals into an array sEMG [ CE ], wherein CE is the data length related to sampling duration and sampling frequency;
b. extracting marginal spectrum entropy characteristics of electromyographic signals collected in the array sEMG [ CE ] by using a Hilbert-Huang transform time-frequency analysis method in combination with an information entropy theory;
c. b, storing the marginal spectrum entropy value obtained in the step b into an array HHE [ CH ], defining HHE [ CE ] as a frame of data, and storing the marginal spectrum entropy according to a first-in first-out mode by the array HHE [ CH ], wherein CH represents the number of the marginal spectrum entropy for storing electromyographic signals; and
d. and repeating the steps a, b and c, fully storing the array HHE [ CH ], and taking the fully stored array HHE [ CH ] as a muscle fatigue characteristic for judging muscle fatigue.
CN201710516492.3A 2017-06-29 2017-06-29 Method and device for extracting muscle fatigue features by using electromyographic signal marginal spectrum entropy Active CN107320097B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710516492.3A CN107320097B (en) 2017-06-29 2017-06-29 Method and device for extracting muscle fatigue features by using electromyographic signal marginal spectrum entropy

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710516492.3A CN107320097B (en) 2017-06-29 2017-06-29 Method and device for extracting muscle fatigue features by using electromyographic signal marginal spectrum entropy

Publications (2)

Publication Number Publication Date
CN107320097A CN107320097A (en) 2017-11-07
CN107320097B true CN107320097B (en) 2020-05-01

Family

ID=60198423

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710516492.3A Active CN107320097B (en) 2017-06-29 2017-06-29 Method and device for extracting muscle fatigue features by using electromyographic signal marginal spectrum entropy

Country Status (1)

Country Link
CN (1) CN107320097B (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3714782A1 (en) * 2019-03-27 2020-09-30 Koninklijke Philips N.V. Assessing muscle fatigue
CN110495893B (en) * 2019-07-30 2021-01-29 西安交通大学 System and method for multi-level dynamic fusion recognition of continuous brain and muscle electricity of motor intention
CN112006686A (en) * 2020-07-09 2020-12-01 浙江大学 Neck muscle fatigue analysis method and system
CN113616395B (en) * 2021-08-10 2023-04-14 长春理工大学 Prosthesis control method, device, prosthesis equipment and computer readable storage medium

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103006212A (en) * 2013-01-15 2013-04-03 中国医学科学院生物医学工程研究所 Electrostimulation induced muscular fatigue evaluation method using approximate entropy for analyzing induced myoelectric M waves
CN103054575A (en) * 2013-01-15 2013-04-24 中国医学科学院生物医学工程研究所 Method for studying muscle fatigue on basis of static myoelectricity after electrical stimulation

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FI119718B (en) * 2003-12-22 2009-02-27 Suunto Oy A method of measuring exercise performance

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103006212A (en) * 2013-01-15 2013-04-03 中国医学科学院生物医学工程研究所 Electrostimulation induced muscular fatigue evaluation method using approximate entropy for analyzing induced myoelectric M waves
CN103054575A (en) * 2013-01-15 2013-04-24 中国医学科学院生物医学工程研究所 Method for studying muscle fatigue on basis of static myoelectricity after electrical stimulation

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
基于HHT边际谱熵和能量谱熵的心率变异信号的分析方法;董红生 等;《中国生物医学工程学报》;20100630;第336-344页 *
康复运动中表面肌电信号分析方法研究;马静云;《中国优秀硕士学位论文全文数据库 医药卫生科技辑》;20160115;E070-149 *

Also Published As

Publication number Publication date
CN107320097A (en) 2017-11-07

Similar Documents

Publication Publication Date Title
Puthankattil et al. Classification of EEG signals in normal and depression conditions by ANN using RWE and signal entropy
Chua et al. Application of higher order statistics/spectra in biomedical signals—A review
Fraser et al. Automated biosignal quality analysis for electromyography using a one-class support vector machine
Subha et al. EEG signal analysis: a survey
CN107320097B (en) Method and device for extracting muscle fatigue features by using electromyographic signal marginal spectrum entropy
Phinyomark et al. Fractal analysis features for weak and single-channel upper-limb EMG signals
Xie et al. Fuzzy approximate entropy analysis of chaotic and natural complex systems: detecting muscle fatigue using electromyography signals
Krusienski et al. An evaluation of autoregressive spectral estimation model order for brain-computer interface applications
CN101259015B (en) Electroencephalogram signal analyzing monitoring method and device thereof
CN111310570B (en) Electroencephalogram signal emotion recognition method and system based on VMD and WPD
Karthick et al. Surface electromyography based muscle fatigue progression analysis using modified B distribution time–frequency features
CN105147248A (en) Physiological information-based depressive disorder evaluation system and evaluation method thereof
Naik et al. Applications of ICA and fractal dimension in sEMG signal processing for subtle movement analysis: a review
CN106901732B (en) Measuring method and measuring device for muscle strength and muscle tension in mutation state
Lee et al. Automated epileptic seizure waveform detection method based on the feature of the mean slope of wavelet coefficient counts using a hidden Markov model and EEG signals
Zanetti et al. Approximate zero-crossing: a new interpretable, highly discriminative and low-complexity feature for EEG and iEEG seizure detection
Hu et al. Single-channel EEG signal extraction based on DWT, CEEMDAN, and ICA method
Gupta et al. ECG signal analysis: Past, present and future
CN113040788A (en) Electroencephalogram signal quality detection method based on spectrum analysis
Subasi et al. Signal Acquisition Preprocessing and Feature Extraction Techniques for Biomedical Signals
Fong et al. A time-series pre-processing methodology for biosignal classification using statistical feature extraction
Loza et al. A marked point process framework for extracellular electrical potentials
Su et al. Dynamic analysis of heartbeat rate signals of epileptics using multidimensional phase space reconstruction approach
Arjunan et al. Measuring complexity in different muscles during sustained contraction using fractal properties of SEMG signal
Anishchenko et al. Comparative analysis of methods for classifying the cardiovascular system's states under stress

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