CN105326501B - Muscle state evaluation method based on multi-channel sEMG - Google Patents
Muscle state evaluation method based on multi-channel sEMG Download PDFInfo
- Publication number
- CN105326501B CN105326501B CN201510915150.XA CN201510915150A CN105326501B CN 105326501 B CN105326501 B CN 105326501B CN 201510915150 A CN201510915150 A CN 201510915150A CN 105326501 B CN105326501 B CN 105326501B
- Authority
- CN
- China
- Prior art keywords
- channel
- semg
- signal
- extracting
- time
- 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
Links
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
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7221—Determining signal validity, reliability or quality
-
- 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
- A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
-
- 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)
- Molecular Biology (AREA)
- Biomedical Technology (AREA)
- Veterinary Medicine (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Signal Processing (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Animal Behavior & Ethology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Surgery (AREA)
- Artificial Intelligence (AREA)
- Psychiatry (AREA)
- Physiology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Power Engineering (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The invention aims to provide a muscle state evaluation method based on multi-channel surface electromyography (sEMG). Firstly preprocessing a multi-channel sEMG signal, then taking a first channel as a reference value, differentiating signals of other channels with the first channel, then extracting a release moment by adopting a K-means clustering convolution kernel compensation method (KMCKC), further extracting a single waveform, and finally fusing a plurality of characteristics of the waveform to evaluate the muscle state. Because external interference can all produce the influence to all electrodes, make the difference through the signal of first passageway and original multichannel sEMG, effectual external interference that has reduced can not influence subsequent testing result moreover, improves the accuracy that muscle detected. Due to the application of multiple characteristic parameters, the instability of monitoring of single parameters is effectively avoided, and the monitoring robustness is improved. The invention effectively overcomes various defects in the prior art and has important application value.
Description
Technical Field
The invention relates to a muscle disease monitoring method based on multichannel sEMG.
Background
With the development of our society, people's attention to health is increasing, and diagnosis or monitoring of muscle diseases becomes a focus of research in recent years. Common neurodegenerative muscle diseases include senile dementia, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis. In the case of Amyotrophic Lateral Sclerosis (ALS), ALS is a neurodegenerative disease characterized by progressive destruction of motor neurons in the cortex, brainstem, and spinal cord of the brain, resulting in muscle atrophy, paralysis, and death in the patient. The annual average population incidence rate is about (2-3)/100000, and the prevalence is about (4-6)/100000. ALS accounts for 1/500-1/1000[3] of adult deaths in the United states and the United kingdom, which also means that nearly 50 thousands of people now alive in the United states alone will die of ALS. The invention utilizes multi-channel surface electromyography (sEMG) to quickly detect the muscle function state of a patient with muscle diseases, helps neurologists to diagnose and track the new development process of ALS diseases, and can also accelerate the development of ASL treatment medicines. sEMG research has wide application prospects in the fields of ergonomics, man-machine interfaces, rehabilitation, sports injuries, artificial limbs and the like.
Muscle disease is often an increasing neurodegenerative disease because brain cells that control muscle movement deteriorate, eventually leading to patient death. Currently, there is no clear clinical indication of when muscles cease to contract, including death due to paralysis of heart and lung muscles, and patients can generally survive for a period of several years. Multichannel sEMG is a bioelectrical time series signal during neuromuscular system activity guided by surface electrodes, which is the result of the co-action of motor control information of the Central Nervous System (CNS) with various physicochemical factors affecting the bioelectrical activity of peripheral muscles. Therefore, the muscle change condition can be rapidly and accurately detected by utilizing the sEMG, the muscle function deterioration degree of the patient can be mastered, and the doctor can adjust the treatment scheme in time to save the life of the patient.
At present, when drugs for treating muscle diseases are developed, a quantitative test method for the treatment effectiveness of the diseases is extremely harsh, namely, the life span of a patient is observed. Generally, a drug is considered effective if the patient who is taking the therapeutic drug has a longer life than the control group, and the observation time required for the study is too long, possibly up to several years. And therefore, results of drug studies have progressed very slowly. sEMG technology can shorten the time required to observe whether a drug is effective, so that the faster the drug is approved, the greater the chance that the patient will be rescued. With the multi-channel sEMG detection technique, the physician can observe the changes in the patient's muscle function in real time, rather than in years.
Currently, the clinical muscle testing tools mainly include electromyographic signals (sEMG), phonograms (SMG), near infrared spectra (NIRS), sonograms (AMG), myotones (MMG), sonograms (AMG), and goniometric sensors. Compared with other technologies, the electromyographic signals are accurate detection tools, can be used for quantitative research, and are very suitable for monitoring the muscle function state. The electromyographic signals comprise needle electromyographic signals (IEMG) and surface electromyography (sEMG), and the sEMG is a simple and noninvasive technology and has good clinical application prospect. Therefore, the sEMG-based muscle monitoring technology provided by the invention has practical significance and clinical application value.
Disclosure of Invention
In view of the above problems, it is an object of the present invention to provide a method for monitoring muscle diseases based on multi-channel sEMG. Which comprises the following steps:
1): preprocessing a multi-channel sEMG signal, and eliminating interference by adopting a band-pass filter and a band-elimination filter.
2): and taking the first channel as a reference value, and differentiating signals of other channels with the first channel to obtain a new multi-channel signal.
3): and aiming at the new multi-channel signal, extracting the issuing time by adopting a KMCKC decomposition method.
4): and extracting the individual issuing time according to the motion unit time, and further extracting a single waveform.
5): a plurality of characteristics of the waveform are fused to assess muscle status.
The optimization measures further comprise:
step 1) preprocessing a multichannel sEMG signal, which comprises the following specific steps: the band-pass filter is used for reserving signals in a frequency range of 5Hz to 500Hz, and then the notch filter is used for filtering 50Hz power frequency interference. The band-pass filter and the band-stop filter adopt Butterworth digital filters, and the setting parameters comprise orders, pass bands of the filters, stop band cut-off frequency, 3dB cut-off frequency and the like.
And 2) taking the first channel as a reference value, and differentiating signals of other channels with the first channel to obtain a new multi-channel signal. Taking the 1 st channel signal as a reference value to obtain a new n-1 channel sEMG signal, S'2＝S2-S1，S'3＝S3-S1，...，S'n-1＝Sn-1-S1Subsequent sEMG signal processing is directed to a new n-1 channel sEMG signal. Because the multichannel sEMG signal is a weak signal and is very easy to be interfered, but the interference usually interferes all channels, the influence of the interference can be greatly weakened after the 1 st channel is subtracted, and meanwhile, the relative value of the multichannel signal is not changed, so that the analysis result is not influenced.
Step 3) for a new multichannel signal s'2,s'3,…,s'nAnd extracting the issuing time by adopting a KMCKC decomposition method. Issuing sequence to jth signal source
The estimate of (c) can be expressed as,
wherein XT(n0) Is n0Time of day send outTransposition of the play sequence, C-1 XXIs a correlation matrix of the detected signal containing noise. Thus, the pulse distribution sequence of a signal source can be calculated by the above formula to perform signal decomposition. To improve
Calculated mass of (2), X (n) in the above formula0) The K mean value is used for replacing the mean value of the most issued time clusters, and the core problem of the KMCKC is to find out the issued time of the same type of motion units.
And 4) extracting the independent issuing time according to the time of the motion unit, and further extracting a single waveform. Comparing the release time of all the motion units, finding out the unique release time of each motion unit, i.e. the release time d unique to the motion unit1,d2,…,dnAnd then extracting waveforms with certain lengths at the corresponding time of the sEMG, comparing the waveforms, and selecting the most complete three-phase waveform as the waveform of the motion unit.
And 5) fusing a plurality of characteristics of the waveform to evaluate the muscle state. The variation of 5 parameters of the number of the motion units, the amplitude of the electric potential, the number of phases, the time limit and the delivery frequency is extracted, and the muscle state is expressed by a function FUN:
FUN＝a·ΔI+b·ΔA+c·ΔP+d·ΔL+e·ΔR
where Δ I, Δ a, Δ P, Δ L, Δ R are the changes in the parameters of number of movement units, amplitude of potential, increase of number of phases, time limit, delivery frequency, respectively, and an increase corresponds to a positive sign and a decrease corresponds to a negative sign, a, b, c, d, e are the weighting coefficients of the respective variables, which reflect the weights of the respective quantities in the muscle state function. Generally, the weight of the number of motion units is the highest, and the dispensing frequency, the potential amplitude, the phase number and the time limit are reduced in sequence.
According to the method for monitoring the muscle diseases, firstly, the signal of the first channel is selected as a reference value, all electrodes are affected by external interference, the signal of the first channel is differed from the original multi-channel sEMG, the external interference is effectively reduced, a subsequent detection result is not affected, and the accuracy of muscle detection is improved. Through comparison, the waveform corresponding to the unique time of each motion unit which is the most complete is extracted, and the method is simple to implement and more accurate. Due to the application of multiple characteristic parameters, the instability of monitoring of single parameters is effectively avoided, and the monitoring robustness is improved.
Detailed Description
The embodiments of the present invention are described below with specific examples, and those skilled in the art can easily realize the embodiments from the disclosure of the present specification.
A muscle disease monitoring method based on multi-channel sEMG comprises the following steps:
step 1) preprocessing a multichannel sEMG signal, which comprises the following specific steps: the band-pass filter is used for reserving signals in a frequency range of 5Hz to 500Hz, and then the notch filter is used for filtering 50Hz power frequency interference. The band-pass filter and the band-stop filter adopt Butterworth digital filters, and the setting parameters comprise orders, pass bands of the filters, stop band cut-off frequency, 3dB cut-off frequency and the like.
And 2) taking the first channel as a reference value, and differentiating signals of other channels with the first channel to obtain a new multi-channel signal. Taking the 1 st channel signal as a reference value to obtain a new n-1 channel sEMG signal, S'2＝S2-S1，S'3＝S3-S1，...，S'n-1＝Sn-1-S1Subsequent sEMG signal processing is directed to a new n-1 channel sEMG signal. Because the multichannel sEMG signal is a weak signal and is very easy to be interfered, but the interference usually interferes all channels, the influence of the interference can be greatly weakened after the 1 st channel is subtracted, and meanwhile, the relative value of the multichannel signal is not changed, so that the analysis result is not influenced.
Step 3) for a new multichannel signal s'2,s'3,…,s'nAnd extracting the issuing time by adopting a KMCKC decomposition method. Issuing sequence to jth signal source
The estimate of (c) can be expressed as,
wherein XT(n0) Is n0Transposition of the time dispensing sequence, C-1 XXIs a correlation matrix of the detected signal containing noise. Thus, the pulse distribution sequence of a signal source can be calculated by the above formula to perform signal decomposition. To improve
Calculated mass of (2), X (n) in the above formula0) The K mean value is used for replacing the mean value of the most issued time clusters, and the core problem of the KMCKC is to find out the issued time of the same type of motion units.
And 4) extracting the independent issuing time according to the time of the motion unit, and further extracting a single waveform. Comparing the release time of all the motion units, finding out the unique release time of each motion unit, i.e. the release time d unique to the motion unit1,d2,…,dnAnd then extracting waveforms with the length of 90ms-120ms at the corresponding time of the sEMG, comparing the waveforms, and selecting the most complete waveform as the waveform of the motion unit.
And 5) fusing a plurality of characteristics of the waveform to evaluate the muscle state. The variation of 5 parameters of the number of the motion units, the amplitude of the electric potential, the number of phases, the time limit and the delivery frequency is extracted, and the muscle state is expressed by a function FUN:
FUN＝a·ΔI+b·ΔA+c·ΔP+d·ΔL+e·ΔR
where Δ I, Δ a, Δ P, Δ L, Δ R are the changes in the parameters of number of movement units, amplitude of potential, increase of number of phases, time limit, delivery frequency, respectively, and an increase corresponds to a positive sign and a decrease corresponds to a negative sign, a, b, c, d, e are the weighting coefficients of the respective variables, which reflect the weights of the respective quantities in the muscle state function. Generally, the weight of the number of motion units is the highest, and the dispensing frequency, the potential amplitude, the phase number and the time limit are reduced in sequence. and a, b, c, d and e are 0.4, 0.3, 0.15, 0.1 and 0.05 in sequence.
In summary, according to the method for monitoring muscle diseases, provided by the invention, the signal of the first channel is selected as the reference value, and since the external interference can affect all the electrodes, the signal of the first channel is differed from the original multi-channel sEMG, so that the external interference is effectively reduced, the subsequent detection result is not affected, and the accuracy of muscle detection is improved. Through comparison, the waveform corresponding to the unique time of each motion unit which is the most complete is extracted, and the method is simple to implement and more accurate. Due to the application of multiple characteristic parameters, the instability of monitoring of single parameters is effectively avoided, and the monitoring robustness is improved. The invention effectively overcomes various defects in the prior art and has important application value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (1)
1. A muscle state monitoring method based on multi-channel sEMG is characterized by comprising the following steps:
1): preprocessing a multichannel sEMG signal, and eliminating interference by adopting a band-pass filter and a band-elimination filter;
2): taking the first channel as a reference value, differentiating the signals of other channels with the first channel to obtain a new multi-channel signal, and taking the signal of the 1 st channel as the reference value to obtain a new n-1 channel sEMG signal, S'2＝S2-S1，S'3＝S3-S1，...，S'n-1＝Sn-1-S1In which S is1，S2，…，Sn-1,SnDenotes the 1, 2, …, n-1, n channel signal, S'2，S′3，...，S'n-1Representing the 2 nd, 3 rd, … th, n-1 th new channel signal, wherein the subsequent sEMG signal processing aims at the new n-1 channel sEMG signal;
3): aiming at a new multi-channel signal, extracting a release moment by adopting a KMCKC decomposition method;
4): according to the time of the motion unit, extracting the individual distribution time and further extracting a single waveform, comparing the distribution times of all the motion units, and finding out the unique distribution time of each motion unit, namely the distribution time d unique to the motion unit1,d2,…,dnThen extracting waveforms with certain length at the time corresponding to the sEMG, comparing the waveforms, and selecting the most complete three-phase waveform as the waveform of the motion unit;
5): fusing a plurality of characteristics of the waveform, evaluating the muscle state, extracting the variation of 5 parameters of the number of motion units, the amplitude of potential waves, the number of phases, the time limit and the release frequency, and expressing the muscle state by a function FUN:
FUN＝a·ΔI+b·ΔA+c·ΔP+d·ΔL+e·ΔR
wherein, Δ I, Δ A, Δ P, Δ L, Δ R are the variation of the parameters of the number of the motion units, the amplitude of the electric potential, the number of phases, the time limit and the issuing frequency respectively, an increase is a positive sign, a decrease is a negative sign, and a, b, c, d, e are the weighting coefficients of the corresponding variation, the weighting coefficients reflect the weight of each quantity in the muscle state function, wherein, the weight of the number of the motion units is the highest, and the issuing frequency, the amplitude of the electric potential, the number of phases and the time limit are reduced in sequence.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510915150.XA CN105326501B (en) | 2015-12-10 | 2015-12-10 | Muscle state evaluation method based on multi-channel sEMG |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510915150.XA CN105326501B (en) | 2015-12-10 | 2015-12-10 | Muscle state evaluation method based on multi-channel sEMG |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105326501A CN105326501A (en) | 2016-02-17 |
| CN105326501B true CN105326501B (en) | 2021-02-12 |
Family
ID=55277428
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510915150.XA Active CN105326501B (en) | 2015-12-10 | 2015-12-10 | Muscle state evaluation method based on multi-channel sEMG |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105326501B (en) |
Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108309295A (en) * | 2018-02-11 | 2018-07-24 | 宁波工程学院 | A kind of arm muscular strength assessment method |
| CN108985157A (en) * | 2018-06-07 | 2018-12-11 | 北京邮电大学 | A kind of gesture identification method and device |
| CN109893132A (en) * | 2019-04-08 | 2019-06-18 | 上海大学 | A kind of Surface Electromyography Signal Acquisition System |
| CN110720912B (en) * | 2019-10-12 | 2022-04-26 | 宁波工程学院 | Muscle movement unit extraction method based on waveform correlation |
| CN110720911B (en) * | 2019-10-12 | 2022-08-30 | 宁波工程学院 | Muscle movement unit extraction method |
| CN114115531B (en) * | 2021-11-11 | 2022-09-30 | 合肥工业大学 | End-to-end sign language recognition method based on attention mechanism |
| CN114343680B (en) * | 2021-12-24 | 2024-04-19 | 杭州电子科技大学 | Real-time decomposition method for dynamic surface electromyographic signals |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101482773A (en) * | 2009-01-16 | 2009-07-15 | 中国科学技术大学 | Multi-channel wireless surface myoelectric signal collection apparatus and system |
| CN102722643A (en) * | 2012-05-28 | 2012-10-10 | 天津大学 | Multi-information merging movement mode identification method used for control of artificial lower surface electromyogram limb |
| CN103345641A (en) * | 2013-07-16 | 2013-10-09 | 杭州电子科技大学 | Hand electromyographic signal motion recognition method based on wavelet entropy and support vector machine |
| CN104545906B (en) * | 2013-10-25 | 2017-09-29 | 中国科学院沈阳自动化研究所 | Detection facial paralysis patient's therapeutic effect of acupuncture system based on sEMG |
| CN103735263B (en) * | 2013-11-18 | 2015-08-19 | 浙江大学 | Array surface myoelectric image harvester and acquisition method |
| CN104224169A (en) * | 2014-10-14 | 2014-12-24 | 沈阳工程学院 | Surface electromyogram signal linear analyzing method for judging human body muscle fatigue |
| CN104997508B (en) * | 2015-08-19 | 2018-05-08 | 宁波工程学院 | Array sEMG signal automatic classifying methods |
-
2015
- 2015-12-10 CN CN201510915150.XA patent/CN105326501B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN105326501A (en) | 2016-02-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105326501B (en) | Muscle state evaluation method based on multi-channel sEMG | |
| Farzan et al. | Characterizing and modulating brain circuitry through transcranial magnetic stimulation combined with electroencephalography | |
| Lai et al. | Prognosis of sleep bruxism using power spectral density approach applied on EEG signal of both EMG1-EMG2 and ECG1-ECG2 channels | |
| Chen et al. | The clinical diagnostic utility of transcranial magnetic stimulation: report of an IFCN committee | |
| Staudinger et al. | Analysis of complexity based EEG features for the diagnosis of Alzheimer's disease | |
| Drey et al. | The motor unit number index (MUNIX) in sarcopenic patients | |
| Christakos et al. | Fast rhythms in phrenic motoneuron and nerve discharges | |
| Cassani et al. | The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer's disease diagnosis | |
| Ohashi et al. | Somatosensory cortical excitability changes precede those in motor cortex during human motor learning | |
| Pfurtscheller et al. | Discrimination of motor imagery-induced EEG patterns in patients with complete spinal cord injury | |
| CN100482155C (en) | Instant detection system and detection method for state of attention based on interaction between brain and computer | |
| Meigal et al. | Non-linear EMG parameters for differential and early diagnostics of Parkinson’s disease | |
| Frauscher et al. | Defining muscle activities for assessment of rapid eye movement sleep behavior disorder: from a qualitative to a quantitative diagnostic level | |
| Rodríguez-Carreño et al. | Motor unit action potential duration: measurement and significance | |
| JPH07500983A (en) | Brain biopotential analysis system and device | |
| Ozdemir et al. | Afferent input and sensory function after human spinal cord injury | |
| Wu et al. | Overview of the Application of EMG Recording in the Diagnosis and Approach of Neurological Disorders | |
| Fraga et al. | Towards an EEG-based biomarker for Alzheimer's disease: Improving amplitude modulation analysis features | |
| Nishikawa et al. | Detecting motor unit abnormalities in amyotrophic lateral sclerosis using high-density surface EMG | |
| Latreille et al. | Non-rapid eye movement sleep characteristics in idiopathic REM sleep behavior disorder | |
| Fulda et al. | Rapid eye movements during sleep in mice: High trait-like stability qualifies rapid eye movement density for characterization of phenotypic variation in sleep patterns of rodents | |
| Domínguez et al. | A transient postnatal quiescent period precedes emergence of mature cortical dynamics | |
| Santana-Gomez et al. | Harmonization of pipeline for detection of HFOs in a rat model of post-traumatic epilepsy in preclinical multicenter study on post-traumatic epileptogenesis | |
| Liang et al. | Propofol anesthesia decreased the efficiency of long-range cortical interaction in humans | |
| CN216257042U (en) | Multimode anesthesia degree of depth evaluation system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |