CN109700457A - A kind of ECG acquisition platform - Google Patents
A kind of ECG acquisition platform Download PDFInfo
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- CN109700457A CN109700457A CN201910164603.8A CN201910164603A CN109700457A CN 109700457 A CN109700457 A CN 109700457A CN 201910164603 A CN201910164603 A CN 201910164603A CN 109700457 A CN109700457 A CN 109700457A
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Abstract
The invention belongs to medical instruments fields, disclose a kind of ECG acquisition platform, are provided with input module, and input module is connect with pre-amplifying module by conducting wire；Pre-amplifying module is internally integrated instrument amplifier circuit, and pre-amplifying module is connect with main amplifier device module by conducting wire；Main amplifier device module is connect with filter network module by conducting wire；Filter network module has high-pass filtering circuit and low-pass filter circuit, and low-pass filter circuit uses second order Bezier filter circuit；Filter network module is connected with electrocardio image-forming module by conducting wire；Electrocardio image-forming module is connected with picture recognition module by conducting wire；Picture recognition module is connected with display module.The present invention can acquire the electrocardiogram of user, by identifying that the image of electrocardiogram is diagnosed, and intuitively show diagnostic result on the display module, have very strong practicability, the person of being convenient to use understands the state of an illness of oneself in the case where no doctor.
Description
Technical field
The invention belongs to medical equipment field more particularly to a kind of ECG acquisition platforms.
Background technique
Currently, with the improvement of living standards, more than patient, ordinary people also increasingly pay attention to the health of body.And it is present
Heart disease is commonplace in crowd, and people are frequently necessary to have an electro-cardiogram.But electrocardio equipment on the market now, requires
Hospital carries out using need to carry out image recognition with professional knowledge by doctor, and then judge the body of patient after obtaining electrocardiogram
Situation.
In conclusion problem of the existing technology is: electrocardio equipment on the market now requires to be made in hospital
With, it, need to be by doctor with professional knowledge progress image recognition after obtaining electrocardiogram, and then judge the physical condition of patient.
Summary of the invention
In view of the problems of the existing technology, the present invention provides a kind of ECG acquisition platforms.
The invention is realized in this way a kind of ECG acquisition platform is provided with
Input module is connect by conducting wire with pre-amplifying module, is put for partes corporis humani's sub-signal to be transmitted to electrocardio
Big module；
Pre-amplifying module is connect by conducting wire with main amplifier device module, is internally integrated instrument amplifier circuit, and being used for will be defeated
The slow electrocardiosignal of faint variation for entering module transfer amplifies；
Main amplifier device module is connect with filter network module by conducting wire, is carried out input module transmission for again micro-
The slow electrocardiosignal of weak variation amplifies；
The main amplifier device module estimates the jumping moment of each jump and respectively jumps corresponding normalization using clustering algorithm
Hybrid matrix column vector, Hopping frequencies when, comprising the following steps:
The first step, at p (p=0,1,2 ... the P-1) moment,Indicate the response of p moment time-frequencyCorresponding frequency indices when non-zero,The frequency values of expression are clustered, and what is obtained is poly-
Class Center NumberIndicate carrier frequency number existing for the p moment,A cluster centre then indicates the size of carrier frequency, uses respectivelyIt indicates；
Second step utilizes clustering algorithm pair to each sampling instant p (p=0,1,2 ... P-1)It is clustered,
It is same availableA cluster centre is usedIt indicates；
Third step, to allIt averages and is rounded, obtain the estimation of source signal numberI.e.
4th step, finds outAt the time of, use phIt indicates, to the p of each section of continuous valuehIntermediate value is sought, is usedIndicate the l sections of p that are connectedhIntermediate value, thenIndicate the estimation at first of frequency hopping moment；
5th step is obtained according to estimation in second stepAnd the 4th estimate to obtain in step
The frequency hopping moment estimate it is each jump it is correspondingA hybrid matrix column vectorSpecific formula are as follows:
HereIt is corresponding to indicate that l is jumpedA mixing
Matrix column vector estimated value；
6th step is estimated the corresponding carrier frequency of each jump, is usedIt is corresponding to indicate that l is jumpedA frequency estimation, calculation formula are as follows:
Filter network module is connected with electrocardio image-forming module by conducting wire, has high-pass filtering circuit and low-pass filtering electricity
Road, low-pass filter circuit uses second order Bezier filter circuit, for being filtered to signal；
The normalization hybrid matrix column vector that the filter network module is estimated estimates time-frequency domain frequency hopping source signal, tool
Steps are as follows for body:
The first step judges which moment index belongs to and jump to all sampling instants index p, method particularly includes: ifThen indicate that moment p belongs to l jump；IfThen indicate that moment p belongs to the 1st
It jumps, whereinFirst of frequency hopping moment estimation；
Second step, all moment p that l (l=1,2 ...) is jumpedl, estimate the time-frequency domain number of each frequency hopping source signal of the jump
According to calculation formula is as follows:
Electrocardio image-forming module is connect, for electronic signal to be converted into image by conducting wire with picture recognition module；
Picture recognition module is connect with display module, for identification the image of electrocardiogram, and is stored in picture recognition module
The image of each situation of heart be compared, judge the health condition of heart；
Described image identification module assumes that weighting processing specifically includes more:
1) current iteration result is each piece as reference picture first and obtains corresponding more hypothesis set, changed currently
In generation reconstruct gained image, centered on current block, search window is established according to the window size W of setting, later in search window
Preliminary more hypothesis set are obtained by sliding pixel-by-pixel in mouthful；
2) after obtaining preliminary hypothesis set, the measurement field distance between each hypothesis and current block is calculated, calculating process can
It indicates are as follows:
Wherein yiIndicate i-th piece in original image of measured value,Indicate i-th piece pair it will be assumed that set in jth
It is a it is assumed that D is bigger to indicate that the similitude that changes between hypothesis and current block is poorer, to all hypothesis according to similitude from high to low
Sequence is ranked up, and takes preceding TnumIt is a to assume to form new hypothesis set as finally selected hypothesis；
3) to obtained new hypothesis set, model is estimated using the weight based on elastic network(s), calculates pair of each hypothesis
Answer weight, it is assumed that more similar to current block, shared weight is bigger, and the weighted sum of each hypothesis is finally enabled most to connect in measurement field
Nearly original image；Later according to gained weight, weighted sum is gone to each hypothesis to obtain the optimal estimating value i.e. side letter to reconstructed image
Breath；
Display module, for showing the image of electrocardiogram and to the diagnostic result of health of heart situation；
The image processing method of the display module includes:
Step 1 obtains the smoothed out curve of spectrum of each pixelIn conjunction with the colour matching letter of CIE1931 standard colorimetric system
NumberCIEXYZ tristimulus values under CIE1931 standard colorimetric system is calculated to obtain using following formula
(X, Y, Z), wherein Δ λ is the spectrum sample interval of imaging spectral instrument；
Step 2, according to the tristimulus values (X of standard illuminants D65D65,YD65,ZD65), by following formula by each pixel
CIEXYZ tristimulus values is converted to homogeneous color aware space CIEL*C*h*, obtain three Color perception parameters, i.e. lightness
ChromaAnd tone h1；
Wherein,
XD65=95.047, YD65=100, ZD65=108.883；
Brightness coefficient k is arranged in step 3L, chroma coefficient kCWith tone coefficient khValue, pass through following formula modulation step two
Obtain the lightness of each pixelChromaAnd tone h1, obtain modulated Color perception parameter, i.e. lightnessChroma
And tone h2, so that effect of visualization is met fidelity reproduction demand, then kL=kC=1, kh=0, change kLIt realizes and adjusts image light and shade
Demand, change kCIt realizes the demand for adjusting the bright-coloured degree of image, changes khRealize the demand for adjusting image white balance；
Step 4, according to the white point tristimulus values (X of display equipmentW,YW,ZW), by following formula, step 5 is obtained into each picture
The lightness of elementChromaAnd tone h2It converts to CIEXYZ value (X', Y', Z') to be shown on the display device；
Step 5, according to the primary colors tristimulus values (X of display equipment red, green, blue triple channelRmax,YRmax,ZRmax)、(XGmax,
YGmax,ZGmax、(XBmax,YBmax,ZBmax) in conjunction with the gamma factor γ of triple channelR、γG、γB, it is established that such as the characterization mould of following formula
Type, by characterization model, the CIEXYZ value (X', Y', Z') of each pixel is calculated to corresponding digital drive values (dR,dG,dB),
The color visualization of high spectrum image is completed, wherein N is the display single pass storage bit number of equipment；
The present invention can not only acquire the electrocardiogram of user, but also can be examined by identifying the image of electrocardiogram
It is disconnected, and diagnostic result is intuitively shown on the display module, there is very strong practicability, the person of being convenient to use is in no doctor
In the case where understand oneself the state of an illness.
Detailed description of the invention
Fig. 1 is ECG acquisition platform structural schematic diagram provided in an embodiment of the present invention；
In figure: 1, input module；2, pre-amplifying module；3, main amplifier device module；4, filter network module；5, electrocardio at
As module；6, picture recognition module；7, display module.
Specific embodiment
In order to further understand the content, features and effects of the present invention, the following examples are hereby given, and cooperate attached drawing
Detailed description are as follows.
Structure of the invention is explained in detail with reference to the accompanying drawing.
As shown in Figure 1, ECG acquisition platform provided in an embodiment of the present invention includes: input module 1, pre-amplifying module
2, main amplifier device module 3, filter network module 4, electrocardio image-forming module 5, picture recognition module 6, display module 7.
Input module 1 is connect with pre-amplifying module 2 by conducting wire, for partes corporis humani's sub-signal to be transmitted to electrocardio
Amplification module 2；
Pre-amplifying module 2 is connect by conducting wire with main amplifier device module 3, is internally integrated instrument amplifier circuit, and being used for will
The slow electrocardiosignal of faint variation that the transmission of input module 1 comes amplifies；
Main amplifier device module 3 is connect with filter network module 4 by conducting wire, for again carry out the transmission of input module 1
The slow electrocardiosignal of faint variation amplifies, the ability for inhibiting various interference signals enough have signal；
Filter network module 4 is connected with electrocardio image-forming module 5 by conducting wire, has high-pass filtering circuit and low-pass filtering
Circuit, low-pass filter circuit uses second order Bezier filter circuit, for being filtered to signal；
Electrocardio image-forming module 5 is connect with picture recognition module 6 by conducting wire, for electronic signal to be converted into image；
Picture recognition module 6, connect with display module, deposits in the image of electrocardiogram, with picture recognition module 6 for identification
The image of each situation of the heart of storage is compared, and then judges the health condition of heart.
Display module 7, for showing the image of electrocardiogram and to the diagnostic result of health of heart situation.
The main amplifier device module estimates the jumping moment of each jump and respectively jumps corresponding normalization using clustering algorithm
Hybrid matrix column vector, Hopping frequencies when, comprising the following steps:
The first step, at p (p=0,1,2 ... the P-1) moment,Indicate the response of p moment time-frequencyCorresponding frequency indices when non-zero,The frequency values of expression are clustered, and what is obtained is poly-
Class Center NumberIndicate carrier frequency number existing for the p moment,A cluster centre then indicates the size of carrier frequency, uses respectivelyIt indicates；
Second step utilizes clustering algorithm pair to each sampling instant p (p=0,1,2 ... P-1)It is clustered,
It is same availableA cluster centre is usedIt indicates；
Third step, to allIt averages and is rounded, obtain the estimation of source signal numberI.e.
4th step, finds outAt the time of, use phIt indicates, to the p of each section of continuous valuehIntermediate value is sought, is usedIndicate the l sections of p that are connectedhIntermediate value, thenIndicate the estimation at first of frequency hopping moment；
5th step is obtained according to estimation in second stepp≠phAnd the 4th estimate to obtain in step
The frequency hopping moment estimate it is each jump it is correspondingA hybrid matrix column vectorSpecific formula are as follows:
HereIt is corresponding to indicate that l is jumpedA mixing
Matrix column vector estimated value；
6th step is estimated the corresponding carrier frequency of each jump, is usedIt is corresponding to indicate that l is jumpedA frequency estimation, calculation formula are as follows:
The normalization hybrid matrix column vector that the filter network module is estimated estimates time-frequency domain frequency hopping source signal, tool
Steps are as follows for body:
The first step judges which moment index belongs to and jump to all sampling instants index p, method particularly includes: ifThen indicate that moment p belongs to l jump；IfThen indicate that moment p belongs to the 1st
It jumps, whereinFirst of frequency hopping moment estimation；
Second step, all moment p that l (l=1,2 ...) is jumpedl, estimate the time-frequency domain number of each frequency hopping source signal of the jump
According to calculation formula is as follows:
Described image identification module assumes that weighting processing specifically includes more:
1) current iteration result is each piece as reference picture first and obtains corresponding more hypothesis set, changed currently
In generation reconstruct gained image, centered on current block, search window is established according to the window size W of setting, later in search window
Preliminary more hypothesis set are obtained by sliding pixel-by-pixel in mouthful；
2) after obtaining preliminary hypothesis set, the measurement field distance between each hypothesis and current block is calculated, calculating process can
It indicates are as follows:
Wherein yiIndicate i-th piece in original image of measured value,Indicate i-th piece pair it will be assumed that set in jth
It is a it is assumed that D is bigger to indicate that the similitude that changes between hypothesis and current block is poorer, to all hypothesis according to similitude from high to low
Sequence is ranked up, and takes preceding TnumIt is a to assume to form new hypothesis set as finally selected hypothesis；
3) to obtained new hypothesis set, model is estimated using the weight based on elastic network(s), calculates pair of each hypothesis
Answer weight, it is assumed that more similar to current block, shared weight is bigger, and the weighted sum of each hypothesis is finally enabled most to connect in measurement field
Nearly original image；Later according to gained weight, weighted sum is gone to each hypothesis to obtain the optimal estimating value i.e. side letter to reconstructed image
Breath.
The image processing method of the display module includes:
Step 1 obtains the smoothed out curve of spectrum of each pixelIn conjunction with the colour matching letter of CIE1931 standard colorimetric system
NumberCIEXYZ tristimulus values under CIE1931 standard colorimetric system is calculated to obtain using following formula
(X, Y, Z), wherein Δ λ is the spectrum sample interval of imaging spectral instrument；
Step 2, according to the tristimulus values (X of standard illuminants D65D65,YD65,ZD65), by following formula by each pixel
CIEXYZ tristimulus values is converted to homogeneous color aware space CIEL*C*h*, obtain three Color perception parameters, i.e. lightness
ChromaAnd tone h1；
Wherein,
XD65=95.047, YD65=100, ZD65=108.883；
Brightness coefficient k is arranged in step 3L, chroma coefficient kCWith tone coefficient khValue, pass through following formula modulation step two
Obtain the lightness of each pixelChromaAnd tone h1, obtain modulated Color perception parameter, i.e. lightnessChroma
And tone h2, so that effect of visualization is met fidelity reproduction demand, then kL=kC=1, kh=0, change kLIt realizes and adjusts image light and shade
Demand, change kCIt realizes the demand for adjusting the bright-coloured degree of image, changes khRealize the demand for adjusting image white balance；
Step 4, according to the white point tristimulus values (X of display equipmentW,YW,ZW), by following formula, step 5 is obtained into each picture
The lightness of elementChromaAnd tone h2It converts to CIEXYZ value (X', Y', Z') to be shown on the display device；
Step 5, according to the primary colors tristimulus values (X of display equipment red, green, blue triple channelRmax,YRmax,ZRmax)、(XGmax,
YGmax,ZGmax、(XBmax,YBmax,ZBmax) in conjunction with the gamma factor γ of triple channelR、γG、γB, it is established that such as the characterization mould of following formula
Type, by characterization model, the CIEXYZ value (X', Y', Z') of each pixel is calculated to corresponding digital drive values (dR,dG,dB),
The color visualization of high spectrum image is completed, wherein N is the display single pass storage bit number of equipment；
In the use of the present invention, input module 1 will monitor the cardiac data of user, and this data is passed by conducting wire
Transport to pre-amplifying module 2 and main amplification module 3, by input module 1 transmission come the slow electrocardiosignal of faint variation into
Row amplification inhibits the abilities of various interference signals have signal enough, then by signal by wire transmission to filtering net
Network module 4；Filter network module 4, has high-pass filtering circuit and low-pass filter circuit, and low-pass filter circuit uses second order shellfish plug
That filter circuit, is filtered signal, then transmits a signal to electrocardio image-forming module 5；Mould is imaged in electrocardio in signal
Block 5 is imaged, and by image transmitting to identification module 6；Picture recognition module 6 can identify the image of electrocardiogram, with image
The image of each situation of the heart stored in identification module 6 is compared, and then judges the health condition of heart；Image recognition mould
Block 6 is connected with display module 7, and the figure and picture recognition module 6 that electrocardiogram then can be shown on display module 7 are to heart
Diagnostic result.
The above is only the preferred embodiments of the present invention, and is not intended to limit the present invention in any form,
Any simple modification made to the above embodiment according to the technical essence of the invention, equivalent variations and modification, belong to
In the range of technical solution of the present invention.
Claims (3)
1. a kind of ECG acquisition platform, which is characterized in that the ECG acquisition platform is provided with
Input module is connect with pre-amplifying module by conducting wire, for partes corporis humani's sub-signal to be transmitted to electrocardio amplification mould
Block；
Pre-amplifying module is connect by conducting wire with main amplifier device module, is internally integrated instrument amplifier circuit, for that will input mould
The slow electrocardiosignal of faint variation that block transmission comes amplifies；
Main amplifier device module is connect with filter network module by conducting wire, is carried out input module transmission for again faint
Change slow electrocardiosignal to amplify；
The main amplifier device module is estimated the jumping moment of each jump using clustering algorithm and is respectively jumped corresponding normalized mixed
When closing matrix column vector, Hopping frequencies, comprising the following steps:
The first step, at p (p=0,1,2 ... the P-1) moment,Indicate the response of p moment time-frequencyCorresponding frequency indices when non-zero,The frequency values of expression are clustered, and what is obtained is poly-
Class Center NumberIndicate carrier frequency number existing for the p moment,A cluster centre then indicates the size of carrier frequency, uses respectivelyIt indicates；
Second step utilizes clustering algorithm pair to each sampling instant p (p=0,1,2 ... P-1)It is clustered, equally
It is availableA cluster centre is usedIt indicates；
Third step, to allIt averages and is rounded, obtain the estimation of source signal numberI.e.
4th step, finds outAt the time of, use phIt indicates, to the p of each section of continuous valuehIntermediate value is sought, is usedIndicate the l sections of p that are connectedhIntermediate value, thenIndicate the estimation at first of frequency hopping moment；
5th step is obtained according to estimation in second stepAnd the 4th frequency for estimating in step
It is corresponding that rate jumping moment estimates each jumpA hybrid matrix column vectorSpecific formula are as follows:
HereIt is corresponding to indicate that l is jumpedA hybrid matrix
Column vector estimated value；
6th step is estimated the corresponding carrier frequency of each jump, is usedIt is corresponding to indicate that l is jumpedIt is a
Frequency estimation, calculation formula are as follows:
Filter network module is connected with electrocardio image-forming module by conducting wire, has high-pass filtering circuit and low-pass filter circuit, low
Bandpass filter circuit uses second order Bezier filter circuit, for being filtered to signal；
The normalization hybrid matrix column vector that the filter network module is estimated estimates time-frequency domain frequency hopping source signal, specific to walk
It is rapid as follows:
The first step judges which moment index belongs to and jump to all sampling instants index p, method particularly includes: ifThen indicate that moment p belongs to l jump；IfThen indicate that moment p belongs to the 1st
It jumps, whereinFirst of frequency hopping moment estimation；
Second step, all moment p that l (l=1,2 ...) is jumpedl, estimate the time-frequency numeric field data of each frequency hopping source signal of the jump, count
It is as follows to calculate formula:
Electrocardio image-forming module is connect, for electronic signal to be converted into image by conducting wire with picture recognition module；
Picture recognition module is connect with display module, for identification the image of electrocardiogram, with the heart stored in picture recognition module
The image of dirty each situation is compared, and judges the health condition of heart；
Described image identification module assumes that weighting processing specifically includes more:
1) current iteration result is each piece as reference picture first and obtains corresponding more hypothesis set, in current iteration weight
In image obtained by structure, centered on current block, search window is established according to the window size W of setting, later in search window
Preliminary more hypothesis set are obtained by sliding pixel-by-pixel；
2) after obtaining preliminary hypothesis set, the measurement field distance between each hypothesis and current block is calculated, calculating process can indicate
Are as follows:
Wherein yiIndicate i-th piece in original image of measured value,Indicate i-th piece pair it will be assumed that j-th in set false
If D is bigger, the similitude for indicating to change between hypothesis and current block is poorer, the sequence to all hypothesis according to similitude from high to low
It is ranked up, and takes preceding TnumIt is a to assume to form new hypothesis set as finally selected hypothesis；
3) to obtained new hypothesis set, model is estimated using the weight based on elastic network(s), calculates the corresponding power of each hypothesis
Weight, it is assumed that more similar to current block, shared weight is bigger, finally enables the weighted sum of each hypothesis closest former in measurement field
Image；Later according to gained weight, weighted sum is gone to obtain the optimal estimating value i.e. side information to reconstructed image to each hypothesis；
Display module, for showing the image of electrocardiogram and to the diagnostic result of health of heart situation；
The image processing method of the display module includes:
Step 1 obtains the smoothed out curve of spectrum of each pixelIn conjunction with the color matching function of CIE1931 standard colorimetric systemUsing following formula calculate under CIE1931 standard colorimetric system CIEXYZ tristimulus values (X,
Y, Z), wherein Δ λ is the spectrum sample interval of imaging spectral instrument；
Step 2, according to the tristimulus values (X of standard illuminants D65D65,YD65,ZD65), by following formula by each pixel
CIEXYZ tristimulus values is converted to homogeneous color aware space CIEL*C*h*, obtain three Color perception parameters, i.e. lightness
ChromaAnd tone h1；
Wherein,
XD65=95.047, YD65=100, ZD65=108.883；
Brightness coefficient k is arranged in step 3L, chroma coefficient kCWith tone coefficient khValue, obtained by following formula modulation step two
The lightness of each pixelChromaAnd tone h1, obtain modulated Color perception parameter, i.e. lightnessChromaAnd color
Adjust h2, so that effect of visualization is met fidelity reproduction demand, then kL=kC=1, kh=0, change kLRealize the need for adjusting image light and shade
It asks, changes kCIt realizes the demand for adjusting the bright-coloured degree of image, changes khRealize the demand for adjusting image white balance；
Step 4, according to the white point tristimulus values (X of display equipmentW,YW,ZW), by following formula, step 5 is obtained into each pixel
LightnessChromaAnd tone h2It converts to CIEXYZ value (X', Y', Z') to be shown on the display device；
Step 5, according to the primary colors tristimulus values (X of display equipment red, green, blue triple channelRmax,YRmax,ZRmax)、(XGmax,YGmax,
ZGmax、(XBmax,YBmax,ZBmax) in conjunction with the gamma factor γ of triple channelR、γG、γB, it is established that such as the characterization model of following formula,
By characterization model, the CIEXYZ value (X', Y', Z') of each pixel is calculated to corresponding digital drive values (dR,dG,dB), it completes
The color visualization of high spectrum image, wherein N is display equipment single pass storage bit number；
2. ECG acquisition platform as described in claim 1, which is characterized in that the input module and pre-amplifying module are logical
Conducting wire connection, including electrode plate, lead device, Conduction choice switch are crossed, for partes corporis humani's sub-signal to be transmitted to preposition amplification
Module.
3. ECG acquisition platform as described in claim 1, which is characterized in that the filter network module has high-pass filtering
Circuit and low-pass filter circuit, low-pass filter circuit use second order Bezier filter circuit.
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| CN101102812A (en) * | 2004-11-29 | 2008-01-09 | 卡梅伦保健公司 | Method and apparatus for beat alignment and comparison |
| CN103051367A (en) * | 2012-11-27 | 2013-04-17 | 西安电子科技大学 | Clustering-based blind source separation method for synchronous orthogonal frequency hopping signals |
| US20190015006A1 (en) * | 2014-06-05 | 2019-01-17 | Guangren CHEN | The New Method for Recognizing Point Quantification Standard Elevation or Depression Near the Equipotential Line of Each Heartbeat |
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