CN105125211A - Myoelectricity collection and display device - Google Patents
Myoelectricity collection and display device Download PDFInfo
- Publication number
- CN105125211A CN105125211A CN201510625069.8A CN201510625069A CN105125211A CN 105125211 A CN105125211 A CN 105125211A CN 201510625069 A CN201510625069 A CN 201510625069A CN 105125211 A CN105125211 A CN 105125211A
- Authority
- CN
- China
- Prior art keywords
- signal
- module
- myoelectricity
- wavelet
- filtering
- 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.)
- Pending
Links
Landscapes
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
A myoelectricity collection and display device belongs to the technical field of physiotherapeutic instrument manufacture, and comprises a myoelectricity collection electrode, a signal processing module, an analog-digital conversion module, a self-adaptive interference filtration module, a storage module, a network communication module and a display module, wherein the signal processing module comprises a signal amplifying module used for amplifying myoelectricity signals in all signals, a notch filter used for filtering power frequency signal interference and a bandpass filter used for filtering high-frequency interference; the self-adaptive interference filtration module is used for filtering electrocardio and electromagnetic interference components in digital electric energy signals. During operation, a myoelectricity biofeedback electrode plate is used for collecting the myoelectricity signals on the surface of a body. The device can effectively filter electrocardio signals in the myoelectricity signals and the electromagnetic interference components, and breaks through the condition that an electric pulse treating instrument cannot explicitly display the pathogenesis of a user and the treatment state during treatment.
Description
Technical field
The invention belongs to physical therapy apparatus manufacturing technology field, is a kind of device gathering myoelectricity.
Background technology
Society often occurs patient has slight illness but to can not get definite Diagnosis and Treat phenomenon to hospital through test on multi-tenns inspection, the patient of these slight illness torment for a long time, waste financial resources and manpower, and these pain is caused by muscle-triggered point mostly.Trigger point is also called Severe Pain point or trigger point, is that intramuscular can some ad-hoc locations of excitation pain, has pain to increase the weight of and local muscle twitch and cause the symptom of referred pain at a distance when pressing; The pain of 75% is caused by him.The private doctor janet of U.S. president Kennedy. Travell (JanetG.Travell) describes the knowledge about trigger point in detail in its " myofascial pain and dysfunction: trigger point handbook " of writing.Travell believes that trigger point is the first cause of pain, and the public bears unnecessary pain and corresponding medical burden, for no other reason than that too many doctor and masses still not relieve pain.If clinicist does not know muscle-triggered, point is only source of disease place, just very possible mistaken diagnosis.She think when pain be caused by muscle-triggered point time, most of doctor does not usually consider this point completely, thus gives many unnecessary inspections and non-ly to treat targetedly.
This pain can treat by electric pulse therapeutic equipment, it is by the direct effect to body local by electric pulse, with indirect action that is neural, body fluid, partial musculature's cell can be made to react, the permeability of ion-transfer, the flood of molecule town, transmembrane potential, film and the acid-base value of human body change, local vascular dilation, blood circulation are accelerated, neural generation is excited or suppress, and promote recovery and the regeneration of peripheral nerve.Activate neural transmission, by neuroregulation to normal condition, promote the secretory function of body of gland, improve body immunity, the release of the inner analgesic matter of exciting human, plays good analgesia and therapeutical effect.
But the maximum shortcoming of electric pulse therapeutic equipment be operator cannot visual interpretation disease because of and current treatment status.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of myoelectricity collection and display device.
The scheme of technical solution problem of the present invention adopts myoelectricity acquisition electrode, signal processing module, analog-to-digital conversion module, adaptive disturbance filtering module, memory module, network communication module and display module to form, wherein:
Myoelectricity acquisition electrode, uses EMG biofeedback electrode slice for the electromyographic signal by gathering health, and by described signalisation to signal processing module;
Signal processing module comprises the band filter for the notch filter and the filtering High-frequency Interference electromyographic signal in described signal being carried out to amplifying signal amplification module and the interference of filtering power frequency component;
Analog-to-digital conversion module, for carrying out analog digital conversion by the signal after process;
Adaptive disturbance filtering module, for the electrocardio in filtering digitalized electric energy signal and electromagnetic interference component;
Memory module, for storing the digitized electromyographic signal of filtering interference component;
Network communication module, for the communication connection of network;
Display module, for showing the digitized electromyographic signal of filtering interference component.
Further, in signal processing module, signal amplification module input is connected with described myoelectricity acquisition electrode outfan, for tentatively amplifying electromyographic signal, and improves common mode rejection ratio, its sample rate: 2kSps; Sampling resolution: 12Bits; Bandwidth: 25-500Hz; Adopt the instrument amplifier AD8221 of high cmrr;
Notch filter input is connected with aforementioned signal amplification module outfan, for filtering 50Hz power frequency component;
Band filter input is connected with described notch filter outfan, for filtering High-frequency Interference.
Analog-to-digital conversion module input is connected with described band filter outfan, for filtered signal is carried out analog digital conversion, obtain digitized electromyographic signal, chip MSP430-1471 can be selected realize, this chip has 12 A/D, directly can realize the digitized processing of electromyographic signal.
Adaptive disturbance filtering module input is connected with analog-to-digital conversion module outfan, for the electrocardio composition in filtering digitized electromyographic signal, adopts the BlackfinBF533 of ADI company.
Memory module input is connected with adaptive disturbance filtering module outfan, for storing the digitized electromyographic signal of the compositions such as filtering electrocardio, adopts SD card storage information.
Network communication module input is connected, for the communication connection of network with described adaptive disturbance filtering module outfan.Network communication module adopts chip CS8900, this chip supports lOM/lOOMbps communication speed, support that 16 is/32 BITBUS network bandwidth, full and half duplex mode of operation, CS8900 FPDP DEVICE_A1-DEVICE_A8 with DEVICEDO-DEVICE_D15 is respectively through the Al-A8(address of 74HC245 and Blackfin) with DO-D15(data) port is connected.
Display module input is connected with described memory module outfan, for showing the electromyographic signal of the compositions such as filtering electrocardio, realizes the Real-Time Monitoring to the electromyographic signal gathered.
During operation, use EMG biofeedback electrode slice for gathering the electromyographic signal of body surface.During as gathered arm electromyographic signal, acquisition electrode is sticked in the middle of dorsal forearm, the horizontal finger place of processus styloideus ulnae near-end three; During as gathered leg electromyographic signal, acquisition electrode is sticked on peroneus brevis and extensor digitorum brevis position.
Apparatus of the present invention have the following advantages:
(1) therapeutic state when electric pulse therapeutic equipment clearly cannot show the user cause of disease and treat is breached.
(2) owing to have employed adaptive disturbance filtering module, the electrocardiosignal in effective filtering electromyographic signal and electromagnetic interference component.
Accompanying drawing explanation
Fig. 1 is the block diagram of apparatus of the present invention;
Fig. 2 is the circuit diagram of notch filter in apparatus of the present invention;
Fig. 3 is the chip schematic diagram of adaptive disturbance filtering module in apparatus of the present invention;
Fig. 4 is the circuit connection diagram of apparatus of the present invention.
Detailed description of the invention
Below in conjunction with drawings and Examples, the present invention is described further, but does not limit the scope of the invention with this.
Please first consult figure l, figure l is the structural representation of myoelectricity of the present invention collection and display device, as shown in the figure, a kind of myoelectricity collection and display device, this device comprises myoelectricity acquisition electrode, signal processing module, analog-to-digital conversion module, adaptive disturbance filtering module, memory module, network communication module, display module.
Described myoelectricity acquisition electrode uses EMG biofeedback electrode slice for gathering the electromyographic signal of body surface.During as gathered arm electromyographic signal, need acquisition electrode be sticked in the middle of dorsal forearm, the horizontal finger place of processus styloideus ulnae near-end three; During as gathered leg electromyographic signal, acquisition electrode need be sticked on peroneus brevis and extensor digitorum brevis position.
Described signal processing module comprises:
Signal amplification module, signal amplification module input is connected with described myoelectricity acquisition electrode outfan, for tentatively amplifying electromyographic signal, and improves common mode rejection ratio.The sample rate that the present embodiment adopts: 2kSps; Sampling resolution: 12Bits; Bandwidth: 25-500Hz; Adopt the instrument amplifier AD8221 of high cmrr;
Notch filter, notch filter input is connected with described signal amplification module outfan, and its main circuit is shown in Fig. 2, for filtering 50Hz power frequency component;
Band filter, band filter input is connected with described notch filter outfan, for filtering High-frequency Interference;
Described analog-to-digital conversion module, analog-to-digital conversion module input is connected with described band filter outfan, for filtered signal is carried out analog digital conversion, obtains digitized electromyographic signal.Can select chip MSP430-1471 to realize, this chip has 12 A/D, directly can realize the digitized processing of electromyographic signal.
Described adaptive disturbance filtering module, adaptive disturbance filtering module input is connected with analog-to-digital conversion module outfan, for the electrocardio composition in filtering digitized electromyographic signal, adopt the BlackfinBF533 of ADI company, this chip can easily complete the complicated calculations such as electrocardio elimination algorithm, and its partial circuit figure is shown in Fig. 3.
Described memory module, memory module input is connected with adaptive disturbance filtering module outfan, for storing the digitized electromyographic signal of the compositions such as filtering electrocardio, adopts SD card storage information.
Described network communication module, network communication module input is connected, for the communication connection of network with described adaptive disturbance filtering module outfan.Network communication module adopts chip CS8900, this chip supports lOM/lOOMbps communication speed, support that 16 is/32 BITBUS network bandwidth, full and half duplex mode of operation, CS8900 FPDP DEVICE_A1-DEVICE_A8 with DEVICEDO-DEVICE_D15 is respectively through the Al-A8(address of 74HC245 and Blackfin) with DO-D15(data) port is connected.
Described display module, display module input is connected with described memory module outfan, for showing the electromyographic signal of the compositions such as filtering electrocardio, realizes the Real-Time Monitoring to the electromyographic signal gathered.
Fig. 2 is the circuit diagram of notch filter in the device of myoelectricity of the present invention collection and display, as shown in the figure, the circuit of notch filter is the active filter of band twin-T network, with double-T shaped wave trap in the past unlike, this circuit is introduced amplifier A2 and is formed positive feedback, to reduce resistance band, the amplitude on both sides near stopband center frequency is increased.Quality factor q can be regulated by rheostat Rw.The value of R and C can by mid frequency.Determine.
0=R
2=R
3
C
0=C
1=C
2
=
When.During=50Hz, C and R gets 0.068 respectively
f and 47k Ω; .During=100Hz, C and R gets 0.068 respectively
f and 24k Ω.
Fig. 3 is that myoelectricity of the present invention gathers and the chip schematic diagram of adaptive disturbance filtering module in display device, as shown in the figure, and the primary input end of the digitized electromyographic signal feeding BlackfinBF533 chip that analog-to-digital conversion module receives.Adaptive disturbance filtering module utilizes Algorithms of Wavelet Analysis carry out denoising to signal and be separated with other data by the electrocardiogram (ECG) data in the signal of denoising by MLMS algorithm.
Below Algorithms of Wavelet Analysis and MLMS algorithm are described in detail.
(1) Algorithms of Wavelet Analysis.
Algorithms of Wavelet Analysis carries out multi-faceted, multi-level dynamic Decomposition, to obtain multi-level Wavelet Transform coefficient to aliasing signal (namely from the signal of myoelectricity acquisition electrode, wherein comprising myoelectricity data, electrocardiogram (ECG) data and various residual electromagnetic interference signals etc.); Afterwards, the wavelet coefficient for every one deck carries out threshold process, is separated by noise wavelet coefficients with the wavelet coefficient of useful signal; Then utilize wavelet reconstruction algorithm to recover original signal, thus reach the effect of noise reduction.Concrete, the details of Wavelet Denoising Method is as follows:
Suppose f (x)
l
2(-), then define about
a.bx the continuous wavelet transform of () is:
W(a,b)=<,ψ
a,b>=（1）
ψ is wavelet mother function, and wherein, variable a is contraction-expansion factor, and b is shift factor.
Carry out multiple dimensioned by above formula (1) to signal, multi-faceted dynamic Decomposition, to obtain the wavelet coefficient under different contraction-expansion factor and shift factor; In the different levels of wavelet decomposition, by arranging appropriate threshold value η, the wavelet coefficient not meeting threshold value is then considered to be caused by noise, and after its zero setting, remaining part then represents the true composition of signal.
It should be noted that the selection of threshold value is the key of relation Wavelet Denoising Method effect, the thresholding method proposed for Donoho, then the mode arranging threshold value η is:
（2）
Wherein, n is the length of signal,
for noise signal standard variance,
for estimating threshold value.
Further, by threshold process, after wavelet coefficient is reduced, obtain new matrix of wavelet coefficients W (a, b), and utilize wavelet inverse transformation formula (3) to be reconstructed, then can recover original signal.
(x)=
ab(x)db（3）
It should be noted that the wavelet coefficient gap of signal and noise in critical zone is little, even area coincidence, therefore can affect the readability of data.Based on this, the embodiment of the present invention is on the basis of Wavelet Denoising Method, and the mode adopting wavelet analysis to combine with MLMS algorithm is strengthened myoelectricity data and extracts.
(2) MLMS algorithm.
d
j=b
j+h
j’+n
j’
Wherein, b
jfor the collection value of myoelectricity composition in signal; h
j' be electrocardio composition; n
j' be primary input end and random noise.The electrocardiosignal collected is:
X
j=h
j+n
j
Wherein, h
jfor electrocardio reference information collection value; n
jfor the noise in reference signal.
If n
j, n
j' and b
jfor incoherent mutually, and they and h
j,h
j' also uncorrelated, the fundamental equation that can obtain self-adapted noise elimination is thus:
e
j=d
j-y
j
Group determines y
jequation determined by the adaptive algorithm adopted.Adopt MLMS algorithm, its push away equation and be:
e
j=d
j-W
i-1 TX
j
G
j=2μ/[1+2μX
j TX
j]
And W
j=W
j-1+ G
je
jx
j
W in formula
jfor the self adaptation weight vector in j moment.If it is p rank vectors, if
W
j=[W
j0，W
j1，……,W
j,p-1]
T
And X
jfor the input signal vector of sef-adapting filter, for: X
j=[X
j, X
j-1..., X
j-p-1]
t.
The results showed, the device that myoelectricity of the present invention gathers and shows, its circuit function is powerful, and reliability is high, has the function of electromyographic signal collection, data storage, electrocardio filtering, network transmission, electromyographic signal display.
Above-described embodiment only listing property illustrates principle of the present invention and effect, but not for limiting the present invention.Any person skilled in the art person all can without departing from the spirit and scope of the present invention, all drop within protection scope of the present invention the amendment that above-described embodiment carries out.
Claims (8)
1. myoelectricity collection and a display device, is characterized in that: it is made up of myoelectricity acquisition electrode, signal processing module, analog-to-digital conversion module, adaptive disturbance filtering module, memory module, network communication module and display module, wherein:
Myoelectricity acquisition electrode, uses EMG biofeedback electrode slice for the electromyographic signal by gathering health, and by described signalisation to signal processing module;
Signal processing module comprises the band filter for the notch filter and the filtering High-frequency Interference electromyographic signal in described signal being carried out to amplifying signal amplification module and the interference of filtering power frequency component;
Analog-to-digital conversion module, for carrying out analog digital conversion by the signal after process;
Adaptive disturbance filtering module, for the electrocardio in filtering digitalized electric energy signal and electromagnetic interference component;
Memory module, for storing the digitized electromyographic signal of filtering interference component;
Network communication module, for the communication connection of network;
Display module, for showing the digitized electromyographic signal of filtering interference component.
2. myoelectricity collection according to claim 1 and display device, it is characterized in that: in signal processing module, signal amplification module input is connected with described myoelectricity acquisition electrode outfan, for tentatively amplifying electromyographic signal, and improve common mode rejection ratio, its sample rate: 2kSps; Sampling resolution: 12Bits; Bandwidth: 25-500Hz; Adopt the instrument amplifier AD8221 of high cmrr;
Notch filter input is connected with aforementioned signal amplification module outfan, for filtering 50Hz power frequency component;
Band filter input is connected with described notch filter outfan, for filtering High-frequency Interference.
3. myoelectricity collection according to claim 1 and display device, it is characterized in that: analog-to-digital conversion module input is connected with described band filter outfan, for filtered signal is carried out analog digital conversion, obtain digitized electromyographic signal, select chip MSP430-1471 to realize.
4. myoelectricity collection according to claim 1 and display device, it is characterized in that: adaptive disturbance filtering module input is connected with analog-to-digital conversion module outfan, for the electrocardio composition in filtering digitized electromyographic signal, adopt the BlackfinBF533 of ADI company.
5. myoelectricity collection according to claim 1 and 2 and display device, it is characterized in that: the circuit of notch filter is the active filter of band twin-T network, this circuit is introduced amplifier A2 and is formed positive feedback, to reduce resistance band, the amplitude on both sides near stopband center frequency is increased, and quality factor q is regulated by rheostat Rw; All resistance R and the value of electric capacity C can by mid frequencyes
0determine,
R
0=R
2=R
3
C
0=C
1=C
2
0=
When
0during=50Hz, C and R gets 0.068 respectively
f and 47k Ω;
0during=100Hz, C and R gets 0.068 respectively
f and 24k Ω.
6. the myoelectricity collection according to claim 1 or 4 and display device, is characterized in that: adaptive disturbance filtering module utilizes Algorithms of Wavelet Analysis carry out denoising to signal and be separated with other data by the electrocardiogram (ECG) data in the signal of denoising by MLMS algorithm.
7. myoelectricity collection according to claim 6 and display device, is characterized in that: Algorithms of Wavelet Analysis carries out dynamic Decomposition to aliasing signal, to obtain multi-level Wavelet Transform coefficient; Afterwards, the wavelet coefficient for every one deck carries out threshold process, is separated by noise wavelet coefficients with the wavelet coefficient of useful signal; Then utilize wavelet reconstruction algorithm to recover original signal, thus reach the effect of noise reduction, concrete, the details of Wavelet Denoising Method is as follows:
Suppose f (x)
l
2(-), then define about
a.bx the continuous wavelet transform of () is:
W(a,b)=<,ψ
a,b>=（1）
ψ is wavelet mother function, and wherein, variable a is contraction-expansion factor, and b is shift factor;
By above formula (1), dynamic Decomposition is carried out to signal, to obtain the wavelet coefficient under different contraction-expansion factor and shift factor; In the different levels of wavelet decomposition, by arranging appropriate threshold value η, the wavelet coefficient not meeting threshold value is then considered to be caused by noise, and after its zero setting, remaining part then represents the true composition of signal;
The selection of threshold value is the key of relation Wavelet Denoising Method effect, the thresholding method proposed for Donoho, then the mode arranging threshold value η is:
（2）
Wherein, n is the length of signal,
for noise signal standard variance,
for estimating threshold value;
Further, by threshold process, after wavelet coefficient is reduced, obtain new matrix of wavelet coefficients W (a, b), and utilize wavelet inverse transformation formula (3) to be reconstructed, recover original signal;
(x)=
ab(x)db（3）。
8. myoelectricity collection according to claim 6 and display device, is characterized in that: the mode adopting wavelet analysis to combine with MLMS algorithm is strengthened myoelectricity data and extracts;
The processing method of MLMS:
d
j=b
j+h
j’+n
j’
Wherein, b
jfor the collection value of myoelectricity composition in signal; h
j' be electrocardio composition; n
j' be primary input end and random noise, the electrocardiosignal collected is:
X
j=h
j+n
j
Wherein, h
jfor electrocardio reference information collection value; n
jfor the noise in reference signal;
If n
j, n
j' and b
jfor incoherent mutually, and they and h
j,h
j' also uncorrelated, the fundamental equation that can obtain self-adapted noise elimination is thus:
e
j=d
j-y
j
Group determines y
jequation determined by the adaptive algorithm adopted, adopt MLMS algorithm, its push away equation and be:
e
j=d
j-W
i-1 TX
j
G
j=2μ/[1+2μX
j TX
j]
And W
j=W
j-1+ G
je
jx
j
W in formula
jfor the self adaptation weight vector in j moment;
If it is p rank vectors, if
W
j=[W
j0，W
j1，……,W
j,p-1]
T
And X
jfor the input signal vector of sef-adapting filter, for: X
j=[X
j, X
j-1..., X
j-p-1]
t.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510625069.8A CN105125211A (en) | 2015-09-28 | 2015-09-28 | Myoelectricity collection and display device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510625069.8A CN105125211A (en) | 2015-09-28 | 2015-09-28 | Myoelectricity collection and display device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105125211A true CN105125211A (en) | 2015-12-09 |
Family
ID=54711062
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510625069.8A Pending CN105125211A (en) | 2015-09-28 | 2015-09-28 | Myoelectricity collection and display device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105125211A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106308792A (en) * | 2016-09-06 | 2017-01-11 | 武汉大学 | Portable collection device for high precision myoelectric signal |
| CN107510454A (en) * | 2017-10-11 | 2017-12-26 | 兰州交通大学 | Myoelectric signal collection apparatus and system based on multistage filtering |
| CN110236532A (en) * | 2019-04-30 | 2019-09-17 | 深圳和而泰家居在线网络科技有限公司 | Processing of bioelectric signals method, apparatus, computer equipment and storage medium |
| CN110275621A (en) * | 2019-06-26 | 2019-09-24 | 陕西科技大学 | Barrier movable platform is helped based on electro-ocular signal control |
| CN110859619A (en) * | 2018-08-28 | 2020-03-06 | 易适康连（上海）科技有限公司 | Device, system and method for acquiring abdominal electromyogram data |
| WO2022142205A1 (en) * | 2020-12-31 | 2022-07-07 | 深圳市韶音科技有限公司 | Signal processing circuit and apparatus |
| CN116139409A (en) * | 2023-04-17 | 2023-05-23 | 江西朴拙医疗设备有限公司 | Anti-interference magnetic therapy detection system, threshold detection method and all-in-one machine |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110160802A1 (en) * | 2009-09-30 | 2011-06-30 | Broadcom Corporation | Bio-medical unit system for physical therapy |
| CN102151133A (en) * | 2011-03-04 | 2011-08-17 | 上海理工大学 | Portable respiratory muscle electric collecting device |
| US20110251512A1 (en) * | 2010-04-12 | 2011-10-13 | Reproductive Research Technologies, Llp | System and method for acquiring and displaying abdominal emg signals |
| CN102512153A (en) * | 2011-10-25 | 2012-06-27 | 电信科学技术研究院 | Non-contact electrocardio monitoring mobile terminal and electrocardio monitoring method |
| CN104799854A (en) * | 2015-04-29 | 2015-07-29 | 深圳大学 | Surface myoelectricity acquisition device and myoelectricity signal processing method thereof |
-
2015
- 2015-09-28 CN CN201510625069.8A patent/CN105125211A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110160802A1 (en) * | 2009-09-30 | 2011-06-30 | Broadcom Corporation | Bio-medical unit system for physical therapy |
| US20110251512A1 (en) * | 2010-04-12 | 2011-10-13 | Reproductive Research Technologies, Llp | System and method for acquiring and displaying abdominal emg signals |
| CN102151133A (en) * | 2011-03-04 | 2011-08-17 | 上海理工大学 | Portable respiratory muscle electric collecting device |
| CN102512153A (en) * | 2011-10-25 | 2012-06-27 | 电信科学技术研究院 | Non-contact electrocardio monitoring mobile terminal and electrocardio monitoring method |
| CN104799854A (en) * | 2015-04-29 | 2015-07-29 | 深圳大学 | Surface myoelectricity acquisition device and myoelectricity signal processing method thereof |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106308792A (en) * | 2016-09-06 | 2017-01-11 | 武汉大学 | Portable collection device for high precision myoelectric signal |
| CN107510454A (en) * | 2017-10-11 | 2017-12-26 | 兰州交通大学 | Myoelectric signal collection apparatus and system based on multistage filtering |
| CN110859619A (en) * | 2018-08-28 | 2020-03-06 | 易适康连（上海）科技有限公司 | Device, system and method for acquiring abdominal electromyogram data |
| CN110859619B (en) * | 2018-08-28 | 2023-02-14 | 易适康连（上海）科技有限公司 | Device, system and method for acquiring abdominal electromyogram data |
| CN110236532A (en) * | 2019-04-30 | 2019-09-17 | 深圳和而泰家居在线网络科技有限公司 | Processing of bioelectric signals method, apparatus, computer equipment and storage medium |
| CN110275621A (en) * | 2019-06-26 | 2019-09-24 | 陕西科技大学 | Barrier movable platform is helped based on electro-ocular signal control |
| WO2022142205A1 (en) * | 2020-12-31 | 2022-07-07 | 深圳市韶音科技有限公司 | Signal processing circuit and apparatus |
| CN116139409A (en) * | 2023-04-17 | 2023-05-23 | 江西朴拙医疗设备有限公司 | Anti-interference magnetic therapy detection system, threshold detection method and all-in-one machine |
| CN116139409B (en) * | 2023-04-17 | 2023-07-21 | 江西朴拙医疗设备有限公司 | Anti-interference magnetic therapy detection system, threshold detection method and all-in-one machine |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105125211A (en) | Myoelectricity collection and display device | |
| Kumar et al. | Stationary wavelet transform based ECG signal denoising method | |
| Lin et al. | Discrete-wavelet-transform-based noise removal and feature extraction for ECG signals | |
| Limaye et al. | ECG noise sources and various noise removal techniques: A survey | |
| Alfaouri et al. | ECG signal denoising by wavelet transform thresholding | |
| Nayak et al. | Filtering techniques for ECG signal processing | |
| Nagendra et al. | Application of wavelet techniques in ECG signal processing: an overview | |
| Tinati et al. | A wavelet packets approach to electrocardiograph baseline drift cancellation | |
| Rasti-Meymandi et al. | A deep learning-based framework For ECG signal denoising based on stacked cardiac cycle tensor | |
| CN103932687B (en) | Method and device for preprocessing pulse condition signal | |
| Dora et al. | Correlation-based ECG artifact correction from single channel EEG using modified variational mode decomposition | |
| Amri et al. | ECG signal processing using offline-wavelet transform method based on ECG-IoT device | |
| Mir et al. | ECG denoising and feature extraction techniques–a review | |
| CN103750835A (en) | Electrocardiosignal characteristic detection algorithm | |
| Yadav et al. | Denoising and SNR improvement of ECG signals using wavelet based techniques | |
| Islam et al. | Wavelet based denoising algorithm of the ECG signal corrupted by WGN and Poisson noise | |
| Chen et al. | Centralized wavelet multiresolution for exact translation invariant processing of ECG signals | |
| Lin et al. | Discrete-wavelet-transform-based noise reduction and R wave detection for ECG signals | |
| Tripathi et al. | A novel approach for real-time ECG signal denoising using Fourier decomposition method | |
| Cornelia et al. | ECG signals processing using Wavelets | |
| Jegan et al. | High-performance ECG signal acquisition for heart rate measurement | |
| Das et al. | Optimized orthogonal wavelet-based filtering method for electrocardiogram signal denoising | |
| Jegan et al. | Low cost and improved performance measures on filtering techniques for ECG signal processing and TCP/IP based monitoring using LabVIEW | |
| Anandhi et al. | Performance analysis of wavelet transform in the removal of baseline wandering from ECG signals in children with autism spectrum disorder (ASD) | |
| Karnewar et al. | The combined effect of median and FIR filter in pre-processing of ECG signal using matlab |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20151209 |
|
| WD01 | Invention patent application deemed withdrawn after publication |