CN107753012A - A kind of mcg-signalses method for evaluating quality, system and server - Google Patents
A kind of mcg-signalses method for evaluating quality, system and server Download PDFInfo
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Abstract
The present invention provides a kind of mcg-signalses method for evaluating quality, system and server, and mcg-signalses method for evaluating quality includes：The MCG data for being used to record mcg-signalses caused by cardiac electrical activity collected are pre-processed；The R peaks of QRS wave in pretreated MCG data are identified, and period divisions are carried out to pretreated MCG data；Based on the MCG data after period divisions, calculating is multiple associated with the MCG data, to assess the quality assessment parameter of mcg-signalses quality；According to it is multiple it is corresponding with the quality assessment parameter evaluate threshold value, differentiate the affiliated grade of multiple quality assessment parameters；Mcg-signalses quality is assessed according to the affiliated grade of multiple quality assessment parameters.Instant invention overcomes existing signal quality evaluating method assessment reliability it is not high the shortcomings that, improve the reliability of mcg-signalses quality evaluation, and the speed of service is fast, can be applied to the real-time analysis of signal quality during MCG online acquisitions.
Description
Technical field
The invention belongs to biomedicine signals analysis field, is related to a kind of appraisal procedure and system, more particularly to one kind
Mcg-signalses method for evaluating quality, system and server.
Background technology
Magnetocardiograph (Magnetocardiography, MCG) can record magnetic field drilling with the time caused by cardiac electrical activity
Change process, the important information of evaluation cardiac function is included, available for the heart disease such as diagnosis of coronary heart disease, myocardial infarction, arrhythmia cordis
Disease.However, mcg-signalses are very faint, the interference of external environment is highly prone to, the subway opened, the elevator moved up and down etc.
The quality of mcg-signalses will be weakened.
Mcg-signalses quality is directly connected to the reliability of later stage heart disease diagnosis, therefore, it is necessary to mcg-signalses
Quality is assessed, if this collection heart magnetic data assessment result show it is poor, it is proposed that heart magnetic system operator resurveys
Heart magnetic data.
Five only are divided into cardiechema signals with a parameter (quality factor) to signal quality evaluating method in the prior art
Grade, classification foundation is not abundant enough, and classification reliability can be caused not high；
Or it is to every in multiple noise sources in physiological signal to signal quality evaluating method that prior art, which also includes,
One noise source carries out qualitative analysis to obtain corresponding multiple qualitative gradings, however, the frequency range of mcg-signalses is 0-
100Hz, and some Low Frequency Noise Generators, such as muscle noise source, (being derived from patient, caused unconscious muscle is received due to anxiety
Contracting), the frequency band of motion artifacts noise source (be derived from patient move) etc. also among 0-100Hz, so it is difficult to from heart magnetic data
In effectively extract these noises, reduce the reliability of signal quality evaluation result, therefore, this method is not suitable for mcg-signalses yet
The assessment of quality.
Therefore it provides a kind of mcg-signalses method for evaluating quality, system and server, to solve in the prior art can not
According to mcg-signalses feature, accurate evaluation mcg-signalses quality, to cause mcg-signalses Quality estimation result reliability not high
Defect, it is real to have turned into practitioner in the art's technical problem urgently to be resolved hurrily.
The content of the invention
In view of the above the shortcomings that prior art, it is an object of the invention to provide a kind of mcg-signalses quality evaluation side
Method, system and server, for solve in the prior art can not according to mcg-signalses feature, accurate evaluation mcg-signalses quality,
The problem of to cause mcg-signalses Quality estimation result reliability not high.
In order to achieve the above objects and other related objects, one aspect of the present invention provides a kind of mcg-signalses quality evaluation side
Method, the mcg-signalses method for evaluating quality comprise the following steps：To collecting for recording the heart caused by cardiac electrical activity
The MCG data of magnetic signal are pre-processed；The R peaks of QRS wave in pretreated MCG data are identified, and to pretreated MCG
Data carry out period divisions；Based on the MCG data after period divisions, calculating is multiple associated with the MCG data, to comment
Estimate the quality assessment parameter of mcg-signalses quality；According to it is multiple it is corresponding with the quality assessment parameter evaluate threshold value, differentiate more
The individual affiliated grade of quality assessment parameter；Mcg-signalses quality is assessed according to the affiliated grade of multiple quality assessment parameters.
Mcg-signalses method for evaluating quality also includes described in one embodiment of the invention：It is used to record to what is collected
The ECG data of electric signal caused by cardiac electrical activity is pre-processed；The R peaks of QRS wave in pretreated ECG data are identified,
And period divisions are carried out to pretreated ECG data；Based on the ECG data after period divisions, calculating include it is multiple with it is described
What MCG data and ECG data were associated, to assess the quality assessment parameter of mcg-signalses quality.
The step of period divisions are carried out to pretreated MCG data and ECG data in one embodiment of the invention bag
Include：Calculate the average period of pretreated MCG data and ECG data；Wherein, the pretreated MCG data of the identification
With the average period of ECG dataRtiFor i-th of R in pretreated MCG data/ECG data
At the time of corresponding to peak, Rti+1At the time of for corresponding to i+1 R peaks in pretreated MCG data/ECG data；N1To know
The number of R ripples in other pretreated MCG data/ECG data；With in pretreated the MCG data and ECG data of identification
R peaks corresponding at the time of it is as a reference point, towards taking 0.4 average period before the reference point, towards after the reference point
0.6 average period is taken, is calculated with carrying out period divisions to the pretreated MCG data and ECG data respectively with forming
In the cycle, split the pretreated MCG data and ECG data according to the calculating cycle.
In one embodiment of the invention, the quality assessment parameter bag associated with the MCG data and ECG data
Include the average correlation coefficient of pretreated MCG data and ECG data；Calculate the flat of pretreated MCG data and ECG data
Related coefficientThe step of include：It is absolute related to ECG data to calculate pretreated MCG data in each calculating cycle
Coefficient | ri|, i=1,2,3 ... N, i are the sequence number that number of cycles is calculated in MCG data；
Wherein,
xj, j=1,2,3 ..., M, xjFor the data of MCG in a calculating cycle, j is MCG data sequence numbers, and M is a meter
Calculate the total number of MCG data in the cycle；For MCG data x in a calculating cyclejAverage value；yj, j=1,2,3 ..., M,
yjFor the ECG data in a calculating cycle, j is ECG data sequence number；For y in a cyclejAverage value；Calculate all meters
Calculate the average correlation coefficient of pretreated MCG data and ECG data in the cycleN is calculating cycle in MCG data
Number.
The quality assessment parameter associated with the MCG data and ECG data described in one embodiment of the invention is also
Including can use ratio Ratio；Calculating can be included with the step of ratio Ratio：According to judgment criterion, judge in each calculating cycle
The absolute correlation coefficient of pretreated MCG data and ECG data | ri| it is good or poor, the judgment criterion isWherein, σ be differentiate coefficient correlation good job threshold value, ziTo differentiate result；Count in all calculating cycles
The number G of the absolute correlation coefficient of pretreated MCG data and ECG data preferably, calculating can use ratioN is
Number of cycles is calculated in MCG data.
The quality assessment parameter associated with the MCG data described in one embodiment of the invention also includes pretreatment
The R variances of MCG data afterwards；The step of R variances for calculating pretreated MCG data, includes：Count pretreated MCG numbers
The variance R at the R peaks of N number of calculating cycle invar,RiFor i-th of R in pretreated MCG data
Peak value,For the average value of all R peak values.
The quality assessment parameter associated with the MCG data described in one embodiment of the invention also includes pretreatment
The average base value variance and maximum base value variance of MCG data afterwards；Calculate pretreated MCG data average base value variance and
The step of maximum base value variance, includes：Calculate the variance of QRS wave base value in a calculating cycle of pretreated MCG dataI=1,2,3 ... N, it is the sequence number that number of cycles is calculated in MCG data；Pretreated MCG data calculate at one
QRS wave base value B in cyclejIt is the MCG data values in preset base value time range, preset base value time range refers to T2-TStart time in cycle
=0.33T；Wherein,K is base value BjNumber,For base value BjAverage value；According to pretreatment
The variance of QRS wave base value in a calculating cycle of MCG data afterwardsCalculate pretreated in all calculating cycles
The average base value variance of MCG dataWith maximum base value variance：Wherein, average base value varianceN is MCG numbers
According to middle calculating number of cycles；The maximum base value variance of pretreated MCG data is QRS wave base value variance in all calculating cyclesMaximum.
In one embodiment of the invention, the quality assessment parameter associated with the MCG data also includes pre- place
The average signal-to-noise ratio of MCG data after reasonAnd SNRmin；Calculate the average signal-to-noise ratio of pretreated MCG dataWith
SNRminThe step of include：Calculate the signal to noise ratio snr of pretreated MCG data in each calculating cyclei, i=1,2,3 ... N,
To calculate the sequence number of number of cycles in MCG data；RiWeek is calculated for pretreated MCG data i-th
The R peak values of phase,For the base value variance of pretreated i-th of calculating cycle of MCG data；According to pre- in each calculating cycle
The SNR of MCG data after processingi, calculate the average signal-to-noise ratios of pretreated MCG dataAfter pretreatment
MCG data SNRminFor SNR in all calculating cyclesiMinimum value.
Multiple affiliated grades of quality assessment parameter include first level, the second level described in one embodiment of the invention
Not and third level；The grade of mcg-signalses quality includes first level, second level, and third level；It is described according to multiple
The step of affiliated grade of quality assessment parameter assesses mcg-signalses quality includes：Judge the quality assessment parameter for belonging to first level
Number whether be more than be used for represent mcg-signalses quality grade be first level number threshold value；If, then it represents that the heart
The credit rating of magnetic signal is first level, if it is not, continue judge belong to third level quality assessment parameter number whether
It is the threshold value of the number of third level more than the grade for representing mcg-signalses quality；If, then it represents that the mcg-signalses
Credit rating is third level, if not, then it represents that the grade of the mcg-signalses quality is second level.
Another aspect of the present invention provides a kind of mcg-signalses quality evaluation system, the mcg-signalses quality evaluation system bag
Include：Pretreatment module, for being located in advance to the MCG data for being used to record mcg-signalses caused by cardiac electrical activity collected
Reason；Split module, carried out for the R peaks of QRS wave in pretreated MCG data will to be identified, and to pretreated MCG data
Period divisions；Computing module, it is multiple associated with the MCG data for based on the MCG data after period divisions, calculating,
To evaluate the quality assessment parameter of mcg-signalses quality；Evaluation module, for the evaluation threshold according to multiple quality assessment parameters
Value, differentiates the affiliated grade of multiple quality assessment parameters；And mcg-signalses matter is assessed according to the affiliated grade of multiple quality assessment parameters
Amount.
Another aspect of the invention provides a kind of server, including described mcg-signalses quality evaluation system.
As described above, the mcg-signalses method for evaluating quality of the present invention, system, and server, have the advantages that：
Mcg-signalses method for evaluating quality of the present invention, system, and server overcome existing Signal quality assessment
Method assesses the shortcomings that mcg-signalses reliability is not high, there is provided a kind of reliable, suitable for mcg-signalses quality evaluation
System and method.The invention considers mcg-signalses feature, and seven kinds of parameters are proposed from MCG and ECG data：MCG with
ECG average correlation coefficient, ratio, MCG R variances, average base value variance, maximum base value variance, average SNR, minimum can be used
SNR etc., and qualitative gradings are provided to MCG signal quality using ballot mode, improve the reliable of mcg-signalses quality evaluation
Property, and the speed of service is fast, can be applied to the real-time analysis of signal quality during MCG online acquisitions.
Brief description of the drawings
Fig. 1 is shown as schematic flow sheet of the mcg-signalses method for evaluating quality of the present invention in an embodiment.
Fig. 2 is shown as the schematic flow sheet of step S3 in the mcg-signalses method for evaluating quality of the present invention.
Fig. 3 is shown as the schematic flow sheet of step S6 in the mcg-signalses method for evaluating quality of the present invention.
Fig. 4 is shown as the mcg-signalses and quality assessment result schematic diagram of the different noise contents of the present invention.
Fig. 5 is shown as theory structure schematic diagram of the mcg-signalses quality evaluation system of the present invention in an embodiment.
Fig. 6 is shown as theory structure schematic diagram of the server of the present invention in an embodiment.
Component label instructions
1 mcg-signalses quality evaluation system
11 pretreatment modules
12 identification modules
13 segmentation modules
14 computing modules
15 evaluation modules
131 first cutting units
132 second cutting units
141 first computing units
142 second computing units
143 the 3rd computing units
144 the 4th computing units
145 the 5th computing units
146 the 6th computing units
147 the 7th computing units
S1~S6 steps
S31~S33 steps
Embodiment
Illustrate embodiments of the present invention below by way of specific instantiation, those skilled in the art can be by this specification
Disclosed content understands other advantages and effect of the present invention easily.The present invention can also pass through specific realities different in addition
The mode of applying is embodied or practiced, the various details in this specification can also be based on different viewpoints with application, without departing from
Various modifications or alterations are carried out under the spirit of the present invention.It should be noted that in the case where not conflicting, following examples and implementation
Feature in example can be mutually combined.
It should be noted that the diagram provided in following examples only illustrates the basic structure of the present invention in a schematic way
Think, only show the component relevant with the present invention in schema then rather than according to component count, shape and the size during actual implement
Draw, kenel, quantity and the ratio of each component can be a kind of random change during its actual implementation, and its assembly layout kenel
It is likely more complexity.
Embodiment one
The present embodiment provides a kind of mcg-signalses method for evaluating quality, and the mcg-signalses method for evaluating quality includes following
Step：
The MCG data for being used to record mcg-signalses caused by cardiac electrical activity collected are pre-processed；
The R peaks of QRS wave in pretreated MCG data are identified, and period divisions are carried out to pretreated MCG data；
Based on the MCG data after period divisions, calculating is multiple associated with the MCG data, to assess heart magnetic letter
The quality assessment parameter of number quality；
According to the evaluation threshold value of multiple quality assessment parameters, the affiliated grade of multiple quality assessment parameters is differentiated；
Mcg-signalses quality is assessed according to the affiliated grade of multiple quality assessment parameters.
The mcg-signalses method for evaluating quality described in the present embodiment is described in detail below with reference to diagram.Performing
It is used for before mcg-signalses method for evaluating quality described in the present embodiment, it is necessary to be gathered respectively by magnetocardiograph and electrocardiograph
Record mcg-signalses and the ECG data for recording electric signal caused by cardiac electrical activity caused by cardiac electrical activity.Refer to
Fig. 1 is shown as schematic flow sheet of the mcg-signalses method for evaluating quality in an embodiment.As shown in figure 1, the mcg-signalses
Method for evaluating quality specifically includes following steps：
S1, to collecting for recording the MCG data of mcg-signalses caused by cardiac electrical activity and for recording heart
The ECG data of electric signal caused by electrical activity is pre-processed.Specifically, the cardiac electrical activity that is used to record collected is produced
Mcg-signalses MCG data and for record electric signal caused by cardiac electrical activity ECG data carry out 100Hz low pass filtered
The processing of ripple and 50Hz trap.
S2, identify the R peaks of QRS wave in pretreated MCG data and ECG data.In the present embodiment, by pre- place
MCG data after reason and ECG data carry out once differentiation, nonlinear transformation and subdifferential again, from differentiated data result g ' again
(n) point not for 0 is picked out in, is designated as the R peaks of QRS wave.In the present embodiment, the R peaks for identifying QRS wave are to be used for follow-up MCG
The calculating of the period divisions and quality assessment parameter of data and ECG data.
S3, period divisions are carried out to pretreated MCG data.Referring to Fig. 2, it is shown as step S3 flow signal
Figure.As shown in Fig. 2 the step S3 specifically includes following steps：
S31, calculate the average period of pretreated MCG data and ECG data(because an ECG data can correspond to
One MCG data, therefore, ECG data and MCG data average periods are identicals).
In the present embodiment, the average period of pretreated the MCG data and ECG data of the identificationRtiAt the time of for corresponding to i-th of R peak in pretreated MCG data/ECG data, Rti+1
At the time of for corresponding to i+1 R peaks in pretreated MCG data/ECG data；N1For the pretreated MCG numbers of identification
According to the number of R ripples in/ECG data.In the present embodiment, in pretreated MCG data R ripples number=pretreated
The number of R ripples in ECG data.
S32, it is as a reference point at the time of with corresponding to the R peaks in pretreated the MCG data and ECG data of identification,
0.4 average period is taken before towards the reference point, 0.6 average period is taken afterwards towards the reference point, with respectively to described
Pretreated MCG data and ECG data carry out period divisions, and reformulate calculating cycle, split according to the calculating cycle
The pretreated MCG data and ECG data.
S4, based on the MCG data and ECG data after period divisions, calculate multiple associated with the MCG data, use
To assess the quality assessment parameter of mcg-signalses quality.In the present embodiment, the quality assessment parameter also includes and the MCG
What data and ECG data were associated, to assess the quality assessment parameter of mcg-signalses quality.Specifically, the described and MCG
What data and ECG data were associated, include to assess the quality assessment parameter of mcg-signalses quality in each calculating cycle in advance
The average correlation coefficient and available ratio Ratio of MCG data and ECG data after processing.
In the present embodiment, the average phase relation of pretreated MCG data and ECG data in each calculating cycle is calculated
NumberThe step of include：
Calculate the absolute correlation coefficient of pretreated MCG data and ECG data in each calculating cycle | ri|, i=1,
2,3 ... N, i are the sequence number that number of cycles is calculated in MCG data；
Wherein,
xj, j=1,2,3 ..., M, xjFor the MCG data in a calculating cycle, j is MCG data sequence numbers, and M is a meter
Calculate the total number of MCG data in the cycle；For MCG data x in a calculating cyclejAverage value；yj, j=1,2,3 ..., M,
yjFor the ECG data in a calculating cycle, j is ECG data sequence number；For y in a cyclejAverage value；
Calculate the average correlation coefficient of pretreated MCG data and ECG data in all calculating cyclesN
To calculate number of cycles in MCG data.
In the present embodiment, calculating can be included with the step of ratio Ratio：
Foundation judgment criterion, judge the absolute correlation of pretreated MCG data and ECG data in each calculating cycle
Number | ri| it is good or poor, the judgment criterion isWherein, σ be differentiate coefficient correlation good job threshold value, zi
To differentiate result；
Count the number of the absolute correlation coefficient of pretreated MCG data and ECG data in all calculating cycles preferably
G, calculating can use ratioN is to calculate number of cycles in MCG data.
In the present embodiment, it is associated with the MCG data, to assess the quality assessment parameter of mcg-signalses quality
R variances including pretreated MCG data, the average base value variance of pretreated MCG data, pretreated MCG numbers
According to maximum base value variance, pretreated MCG data average signal-to-noise ratioAnd/or pretreated MCG data
SNRmin。
Specifically, the step of R variances for calculating pretreated MCG data, includes：
Count the variance R at the R peaks of N number of calculating cycle in pretreated MCG datavar,Ri
For i-th of R peak value in pretreated MCG data,For the average value of all R peak values.
The step of calculating the average base value variance of pretreated MCG data and maximum base value variance includes：
Calculate the variance of QRS wave base value in a calculating cycle of pretreated MCG dataI=1,2,3 ...
N, it is the sequence number that number of cycles is calculated in MCG data；The QRS wave base value in a calculating cycle of pretreated MCG data
BjIt is the MCG data values in preset base value time range, preset base value time range refers to T2-TStart time in cycle=0.33T；Wherein,K is base value BjNumber,For base value BjAverage value.
According to the variance of QRS wave base value in each calculating cycle of pretreated MCG dataCalculate all meters
Calculate the average base value variance of pretreated MCG data in the cycleInclude with the step of maximum base value variance：
Calculate the average base value variance of pretreated MCG data in each calculating cycleN is MCG numbers
According to middle calculating number of cycles；
The maximum base value variance of pretreated MCG data is QRS wave base value variance in all calculating cyclesMaximum
Value.
Calculate the average signal-to-noise ratio of pretreated MCG dataAnd SNRminThe step of include：
Calculate the SNR of pretreated MCG data in each calculating cyclei, i=1,2,3 ... N, it is to be calculated in MCG data
The sequence number of number of cycles；RiFor the R peak values of pretreated i-th of calculating cycle of MCG data,For
The base value variance of pretreated i-th of calculating cycle of MCG data；
According to the SNR of pretreated MCG data in each calculating cyclei, calculate being averaged for pretreated MCG data
Signal to noise ratio
The SNR of pretreated MCG dataminFor SNR in all calculating cyclesiMinimum value.
S5, according to it is multiple it is corresponding with the quality assessment parameter evaluate threshold value, differentiate belonging to multiple quality assessment parameters
Grade.In the present embodiment, the affiliated grade of the multiple quality assessment parameter includes first level, second level and the third level
Not, i.e., well, typically, it is poor.Refering to table 1, multiple evaluation threshold values corresponding with the quality assessment parameter and affiliated grade deck watch.
Table 1：Multiple evaluation threshold values corresponding with the quality assessment parameter and affiliated grade deck watch
In the present embodiment, the specific value of the evaluation threshold value such as table 2 in the table 1.
Table 2：Evaluate the specific value of threshold value
S6, mcg-signalses quality is assessed according to the affiliated grade of multiple quality assessment parameters.In the present embodiment, mcg-signalses
The grade of quality includes first level, second level, and third level, i.e., mcg-signalses credit rating preferably, mcg-signalses matter
Amount grade is general, mcg-signalses credit rating is poor.In the present embodiment, step S6 determines mcg-signalses by ballot mode
The grade of quality.Referring to Fig. 3, it is shown as step S6 schematic flow sheet.As shown in figure 3, the step S6 includes：
S61, judge whether the number for belonging to the quality assessment parameter of first level is more than and be used to represent mcg-signalses quality
Grade for first level number threshold value (in the present embodiment, represent mcg-signalses quality grade be excellent number threshold value
For 4, or the half of quality assessment parameter sum)；If, then it represents that the credit rating of the mcg-signalses is first level, if
It is no, step S62 is performed, whether the number for continuing to judge to belong to the quality assessment parameter of third level is more than for representing that heart magnetic is believed
For the number threshold value of third level, (in the present embodiment, the grade for representing mcg-signalses quality is of difference to the grade of number quality
Number threshold value is 4, or the half of quality assessment parameter sum)；If, then it represents that the credit rating of the mcg-signalses is the third level
Not, if not, then it represents that the grade of the mcg-signalses quality is second level (representing that the grade of the mcg-signalses quality is general).Please
Refering to Fig. 4, the mcg-signalses and quality assessment result schematic diagram of different noise contents are shown as.As shown in figure 4, by upper commentary
After estimating, the credit rating of first mcg-signals preferably, the grade of second mcg-signals be it is general, the 3rd mcg-signals
Grade is poor.
Mcg-signalses method for evaluating quality described in the present embodiment overcomes existing signal quality evaluating method and assesses heart magnetic
The shortcomings that signal quality reliability is not high, there is provided it is a kind of it is reliable, suitable for the system and method for mcg-signalses quality evaluation.
The invention considers mcg-signalses feature, and seven kinds of parameters are proposed from MCG and ECG data：MCG is average related to ECG's
Coefficient, ratio, MCG R variances, average base value variance, maximum base value variance, average SNR, minimum SNR etc. can be used, and utilize throwing
Ticket mode provides qualitative gradings to MCG signal quality, improves the reliability of mcg-signalses quality evaluation, and the speed of service
It hurry up, can be applied to the real-time analysis of signal quality during MCG online acquisitions.
Embodiment two
The present embodiment provides a kind of mcg-signalses quality evaluation system 1, is commented referring to Fig. 5, being shown as mcg-signalses quality
Estimate theory structure schematic diagram of the system in an embodiment.As shown in figure 5, the mcg-signalses quality evaluation system 1 includes：In advance
Processing module 11, identification module 12, segmentation module 13, computing module 14 and evaluation module 15.
Pretreatment module 11 be used for collect be used for record mcg-signalses caused by cardiac electrical activity MCG data and
ECG data for recording electric signal caused by cardiac electrical activity is pre-processed.Specifically, the pretreatment module 11 is to adopting
Collect be used for record the MCG data of mcg-signalses caused by cardiac electrical activity and for recording telecommunications caused by cardiac electrical activity
Number ECG data carry out 100Hz LPF and 50Hz trap processing.
The identification module 12 being connected with the pretreatment module 11 is used to identify pretreated MCG data and ECG data
The R peaks of middle QRS wave.In the present embodiment, by carrying out once differentiation, non-linear to pretreated MCG data and ECG data
Conversion and again subdifferential, the point not for 0 is picked out from differentiated data result g ' (n) again, be designated as the R peaks of QRS wave.At this
In embodiment, the R peaks for identifying QRS wave are based on the period divisions and quality assessment parameter of follow-up MCG data and ECG data
Calculate.
The segmentation module 13 being connected with the identification module 12 is used to carry out period divisions to pretreated MCG data.
The segmentation module 13 includes the first cutting unit 131 and the second cutting unit 132.
First cutting unit 131 is specifically used for：
Calculate the average period of pretreated MCG data(because an ECG data can correspond to a MCG data, because
This, ECG data and MCG data average periods are identicals).
It is as a reference point at the time of with corresponding to the R peaks in the pretreated MCG data of identification, towards the reference point it
Before take 0.4 cycle, towards 0.6 cycle is taken after the reference point, to carry out the cycle point to the pretreated MCG data
Cut, and reformulate calculating cycle, split the pretreated MCG data according to the calculating cycle.
Second cutting unit 132 is specifically used for：
Calculate the average period of pretreated ECG data(because an ECG data can correspond to a MCG data, because
This, ECG data and MCG data average periods are identicals).
In the present embodiment, the average period of pretreated the MCG data and ECG data of the identificationRtiAt the time of for corresponding to i-th of R peak in pretreated MCG data/ECG data, Rti+1
At the time of for corresponding to i+1 R peaks in pretreated MCG data/ECG data；N1For the pretreated MCG numbers of identification
According to the number of R ripples in/ECG data.In the present embodiment, in pretreated MCG data R ripples number=pretreated
The number of R ripples in ECG data.
It is as a reference point at the time of with corresponding to the R peaks in the pretreated MCG data of identification, towards the reference point it
Before take 0.4 average period, towards 0.6 average period is taken after the reference point, with to described pretreated and ECG data
Period divisions are carried out, and reformulate calculating cycle, split the pretreated ECG data according to the calculating cycle.
The computing module 14 being connected with the segmentation module 13 is used to, based on the MCG data after period divisions, calculate multiple
It is associated with the MCG data, to assess the quality assessment parameter of mcg-signalses quality.In the present embodiment, the matter
Amount assesses parameter also including associated with the MCG data and ECG data, to assess the quality evaluation of mcg-signalses quality
Parameter.Specifically, it is described associated with the MCG data and ECG data, to assess the quality evaluation of mcg-signalses quality
Parameter includes the average correlation coefficient of pretreated MCG data and ECG data and available ratio in each calculating cycle
Ratio.In the present embodiment, the computing module 14 includes the first computing unit 141, and the second computing unit 142, the 3rd calculates
Unit 143, the 4th computing unit 144, the 5th computing unit 145, the 6th computing unit 146, and the 7th computing unit 147.
In the present embodiment, first computing unit 141 being connected with second cutting unit 132 is used to calculate often
The average correlation coefficient of pretreated MCG data and ECG data in individual calculating cycleSpecific calculating process is as follows：
Calculate the absolute correlation coefficient of pretreated MCG data and ECG data in each calculating cycle | ri|, i=1,
2,3 ... N, i are the sequence number that number of cycles is calculated in MCG data；
Wherein,
xj, j=1,2,3 ..., M are the sequence number of MCG data in a calculating cycle, and M is MCG numbers in a calculating cycle
According to total number；For MCG data x in a calculating cyclejAverage value；yj, j=1,2,3 ..., M is a calculating cycle
The sequence number of interior ECG data；For y in a cyclejAverage value；
Calculate the average correlation coefficient of pretreated MCG data and ECG data in each calculating cycleN
To calculate number of cycles in MCG data.
In the present embodiment, second computing unit 142 is used to calculate that ratio Ratio can be used, and specific calculating process is such as
Under：
Foundation judgment criterion, judge the absolute correlation of pretreated MCG data and ECG data in each calculating cycle
Number | ri| it is good or poor, the judgment criterion isWherein, σ be differentiate coefficient correlation good job threshold value, zi
To differentiate result；
Count the number of the absolute correlation coefficient of pretreated MCG data and ECG data in all calculating cycles preferably
G, calculating can use ratioN is to calculate number of cycles in MCG data.
In the present embodiment, it is associated with the MCG data, to assess the quality assessment parameter of mcg-signalses quality
R variances including pretreated MCG data, the average base value variance of pretreated MCG data, pretreated MCG numbers
According to maximum base value variance, pretreated MCG data average signal-to-noise ratioAnd/or pretreated MCG data
SNRmin。
Specifically, the 3rd computing unit 143 is used for the R variances for calculating pretreated MCG data.Specifically calculated
Journey includes：
Count the variance R at the R peaks of N number of calculating cycle in pretreated MCG datavar,Ri
For i-th of R peak value in pretreated MCG data,For the average value of all R peak values.
The step of calculating the average base value variance of pretreated MCG data and maximum base value variance includes：
Calculate the variance of QRS wave base value in a calculating cycle of pretreated MCG dataI=1,2,3 ...
N, it is the sequence number that number of cycles is calculated in MCG data；The QRS wave base value in a calculating cycle of pretreated MCG data
BjIt is the MCG data values in preset base value time range, preset base value time range refers to T2-TStart time in cycle=0.33T；Wherein,K is base value BjNumber,For base value BjAverage value.
4th computing unit 144, the 5th computing unit 145 are used for according to pretreated MCG data at one
The variance of QRS wave base value in calculating cycleCalculate the average base value side of pretreated MCG data in each calculating cycle
DifferenceWith maximum base value variance.
4th computing unit, the 144 specific calculating process includes：
Calculate the average base value variance of pretreated MCG data in each calculating cycleN is MCG numbers
According to middle calculating number of cycles；
5th computing unit 145 is used to select the maximum base value variance of pretreated MCG data to be all calculating
QRS wave base value variance in cycleMaximum.
6th computing unit 146 and the 7th computing unit 147 are used for the average letter for calculating pretreated MCG data
Make an uproar ratioAnd SNRmin.6th computing unit, the 146 specific calculating process includes：
Calculate the SNR of pretreated MCG data in each calculating cyclei, i=1,2,3 ... N, it is to be calculated in MCG data
The sequence number of number of cycles；RiFor the R peak values of pretreated i-th of calculating cycle of MCG data,For
The base value variance of pretreated i-th of calculating cycle of MCG data；
According to the SNR of pretreated MCG data in each calculating cyclei, calculate being averaged for pretreated MCG data
Signal to noise ratio
7th computing unit 147 is used for the SNR for selecting pretreated MCG dataminFor in all calculating cycles
SNRiMinimum value.
It is used for the evaluation module 15 of the computing module 14 connection according to multiple corresponding with the quality assessment parameter
Threshold value is evaluated, differentiates the affiliated grade of multiple quality assessment parameters.In the present embodiment, belonging to the multiple quality assessment parameter etc.
Level include first level, second level and third level, i.e., well, typically, it is poor.The evaluation module 15 is additionally operable to according to multiple
The affiliated grade of quality assessment parameter assesses mcg-signalses quality.In the present embodiment, the grade of mcg-signalses quality includes first
Rank, second level, and third level, i.e. mcg-signalses credit rating preferably, mcg-signalses credit rating be general, heart magnetic letter
Number credit rating is poor.
The present embodiment also provides a kind of server 2, and the server 2 is connected with electrocardiograph and magnetocardiograph, refers to figure
6, it is shown as theory structure schematic diagram of the server in an embodiment.As shown in fig. 6, the server 2 includes the above-mentioned heart
Magnetic signal quality evaluation system 1.
In summary, mcg-signalses method for evaluating quality of the present invention, system, and server overcome existing signal
Method for evaluating quality assesses the shortcomings that mcg-signalses reliability is not high, there is provided a kind of reliable, suitable for mcg-signalses
The system and method for quality evaluation.The invention considers mcg-signalses feature, and seven seed ginsengs are proposed from MCG and ECG data
Number：MCG and ECG average correlation coefficient, ratio can be used, be MCG R variances, average base value variance, maximum base value variance, average
SNR, minimum SNR etc., and qualitative gradings are provided to MCG signal quality using ballot mode, improve mcg-signalses quality and comment
The reliability estimated, and the speed of service is fast, can be applied to the real-time analysis of signal quality during MCG online acquisitions.So the present invention has
Effect overcomes various shortcoming of the prior art and has high industrial utilization.
The above-described embodiments merely illustrate the principles and effects of the present invention, not for the limitation present invention.It is any ripe
Know the personage of this technology all can carry out modifications and changes under the spirit and scope without prejudice to the present invention to above-described embodiment.Cause
This, those of ordinary skill in the art is complete without departing from disclosed spirit and institute under technological thought such as
Into all equivalent modifications or change, should by the present invention claim be covered.
Claims (11)
1. a kind of mcg-signalses method for evaluating quality, it is characterised in that the mcg-signalses method for evaluating quality includes following step
Suddenly：
The MCG data for being used to record mcg-signalses caused by cardiac electrical activity collected are pre-processed；
The R peaks of QRS wave in pretreated MCG data are identified, and period divisions are carried out to pretreated MCG data；
Based on the MCG data after period divisions, calculating is multiple associated with the MCG data, to assess mcg-signalses matter
The quality assessment parameter of amount；
According to it is multiple it is corresponding with the quality assessment parameter evaluate threshold value, differentiate the affiliated grade of multiple quality assessment parameters；
Mcg-signalses quality is assessed according to the affiliated grade of multiple quality assessment parameters.
2. mcg-signalses method for evaluating quality according to claim 1, it is characterised in that：The mcg-signalses quality evaluation
Method also includes：
The ECG data for being used to record electric signal caused by cardiac electrical activity collected is pre-processed；
The R peaks of QRS wave in pretreated ECG data are identified, and period divisions are carried out to pretreated ECG data；
Based on the ECG data after period divisions, calculating include it is multiple associated with the MCG data and ECG data, to comment
Estimate the quality assessment parameter of mcg-signalses quality.
3. mcg-signalses method for evaluating quality according to claim 2, it is characterised in that：To pretreated MCG data
The step of carrying out period divisions with ECG data includes：
Calculate the average period of pretreated MCG data and ECG data；The pretreated MCG data and ECG of the identification
The average period of dataRtiIt is right for i-th of R peak in pretreated MCG data/ECG data
At the time of answering, Rti+1At the time of for corresponding to i+1 R peaks in pretreated MCG data/ECG data；N1For the pre- of identification
The number of R ripples in MCG data/ECG data after processing；
It is as a reference point at the time of with corresponding to the R peaks in the pretreated MCG and ECG data of identification, towards the reference point
0.4 average period is taken before, 0.6 average period is taken afterwards towards the reference point, with respectively to the pretreated MCG
Data and ECG data carry out period divisions to form calculating cycle, split the pretreated MCG numbers according to the calculating cycle
According to and ECG data.
4. mcg-signalses method for evaluating quality according to claim 2, it is characterised in that：It is described with the MCG data and
The associated quality assessment parameter of ECG data includes the average correlation coefficient of pretreated MCG data and ECG data；
Calculate the average correlation coefficient of pretreated MCG data and ECG dataThe step of include：
Calculate the absolute correlation coefficient of pretreated MCG data and ECG data in each calculating cycle | ri|, i=1,2,3 ...,
N, i are the sequence number that number of cycles is calculated in MCG data；
Wherein, xj,j
=1,2,3 ..., M, xjFor the MCG data in a calculating cycle, j is MCG data sequence numbers, and M is MCG in a calculating cycle
The total number of data；For MCG data x in a calculating cyclejAverage value；yj, j=1,2,3 ..., M, yjFor a calculating
ECG data in cycle, j are the sequence number of ECG data；For y in a cyclejAverage value；
Calculate the MCG data of all calculating cycles and the average correlation coefficient of ECG data after pre-processingN is MCG numbers
According to the total number of middle calculating cycle.
5. mcg-signalses method for evaluating quality according to claim 4, it is characterised in that：It is described with the MCG data and
The associated quality assessment parameter of ECG data also includes available ratio Ratio；Calculating can be included with the step of ratio Ratio：
Foundation judgment criterion, judge the absolute correlation coefficient of pretreated MCG data and ECG data in each calculating cycle | ri
| it is good or poor, the judgment criterion isWherein, σ be differentiate coefficient correlation good job threshold value, ziTo sentence
Other result；
The number G of the absolute correlation coefficient of pretreated MCG data and ECG data in all calculating cycles preferably is counted, is counted
Calculation can use ratioN is to calculate number of cycles in MCG data.
6. mcg-signalses method for evaluating quality according to claim 1, it is characterised in that：It is described with the MCG data phase
The quality assessment parameter of association also includes the R variances of pretreated MCG data；
The step of R variances for calculating pretreated MCG data, includes：
Count the variance R at the R peaks of N number of calculating cycle in pretreated MCG datavar,RiTo be pre-
I-th of R peak value in MCG data after processing,For the average value of all R peak values.
7. mcg-signalses method for evaluating quality according to claim 1, it is characterised in that：It is described with the MCG data phase
Average base value variance and maximum base value variance of the quality assessment parameter of association also including pretreated MCG data；
The step of calculating the average base value variance of pretreated MCG data and maximum base value variance includes：
Calculate the variance of QRS wave base value in a calculating cycle of pretreated MCG dataI=1,2,3 ... N, it is
The sequence number of number of cycles is calculated in MCG data；The QRS wave base value B in a calculating cycle of pretreated MCG datajIt is
MCG data values in preset base value time range, preset base value time range refer to T2-TStart time in cycle=0.33T；Wherein,K is base value BjNumber,For base value BjAverage value；
According to the variance of pretreated MCG data QRS wave base value in a calculating cycleCalculate all calculating cycles
The average base value variance of interior pretreated MCG dataWith maximum base value variance；Wherein, average base value varianceN is to calculate number of cycles in MCG data；
The maximum base value variance of pretreated MCG data is QRS wave base value variance in all calculating cyclesMaximum.
8. mcg-signalses method for evaluating quality according to claim 1, it is characterised in that：It is described with the MCG data phase
The quality assessment parameter of association also includes the average signal-to-noise ratio of pretreated MCG dataAnd SNRmin；
Calculate the average signal-to-noise ratio of pretreated MCG dataAnd SNRminThe step of include：
Calculate the signal to noise ratio snr of pretreated MCG data in each calculating cyclei, i=1,2,3 ... N, fallen into a trap for MCG data
Calculate the sequence number of number of cycles；RiFor the R peak values of pretreated i-th of calculating cycle of MCG data,
For the base value variance of pretreated i-th of calculating cycle of MCG data；
According to the SNR of pretreated MCG data in each calculating cyclei, calculate the average noises of pretreated MCG data
Than
The SNR of pretreated MCG dataminFor SNR in all calculating cyclesiMinimum value.
9. mcg-signalses method for evaluating quality according to claim 1, it is characterised in that：The multiple quality assessment parameter
Affiliated grade includes first level, second level and third level；The grade of mcg-signalses quality includes first level, and second
Rank, and third level；Described the step of assessing mcg-signalses quality according to the affiliated grade of multiple quality assessment parameters, includes：
Judge whether the number for belonging to the quality assessment parameter of first level is more than the grade for being used to represent mcg-signalses quality and is
The number threshold value of first level；If, then it represents that the credit rating of the mcg-signalses is first level, if it is not, continuing to judge category
Whether it is more than for representing the grade of mcg-signalses quality as third level in the number of the quality assessment parameter of third level
The threshold value of number；If, then it represents that the credit rating of the mcg-signalses is third level, if not, then it represents that the mcg-signalses matter
The grade of amount is second level.
10. a kind of mcg-signalses quality evaluation system, it is characterised in that the mcg-signalses quality evaluation system includes：
Pretreatment module, it is pre- for being carried out to the MCG data for being used to record mcg-signalses caused by cardiac electrical activity collected
Processing；
Split module, enter for the R peaks of QRS wave in pretreated MCG data will to be identified, and to pretreated MCG data
Row period divisions；
Computing module, for based on the MCG data after period divisions, calculating to be multiple associated with the MCG data, to comment
The quality assessment parameter of valency mcg-signalses quality；
Evaluation module, for the evaluation threshold value according to multiple quality assessment parameters, differentiate the affiliated grade of multiple quality assessment parameters；
And mcg-signalses quality is assessed according to the affiliated grade of multiple quality assessment parameters.
11. a kind of server, it is characterised in that including mcg-signalses quality evaluation system as described in claim 9.
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