CN107753012B - Magnetocardiogram signal quality evaluation method, system and server - Google Patents

Magnetocardiogram signal quality evaluation method, system and server Download PDF

Info

Publication number
CN107753012B
CN107753012B CN201610692907.8A CN201610692907A CN107753012B CN 107753012 B CN107753012 B CN 107753012B CN 201610692907 A CN201610692907 A CN 201610692907A CN 107753012 B CN107753012 B CN 107753012B
Authority
CN
China
Prior art keywords
data
mcg
preprocessed
mcg data
quality
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201610692907.8A
Other languages
Chinese (zh)
Other versions
CN107753012A (en
Inventor
孔祥燕
鲁丽
杨康
王美玲
陈威
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shanghai Institute of Microsystem and Information Technology of CAS
Original Assignee
Shanghai Institute of Microsystem and Information Technology of CAS
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shanghai Institute of Microsystem and Information Technology of CAS filed Critical Shanghai Institute of Microsystem and Information Technology of CAS
Priority to CN201610692907.8A priority Critical patent/CN107753012B/en
Publication of CN107753012A publication Critical patent/CN107753012A/en
Application granted granted Critical
Publication of CN107753012B publication Critical patent/CN107753012B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/242—Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents
    • A61B5/243—Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents specially adapted for magnetocardiographic [MCG] signals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis

Landscapes

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

Abstract

The invention provides a method, a system and a server for evaluating quality of magnetocardiogram signals, wherein the method for evaluating the quality of the magnetocardiogram signals comprises the following steps: preprocessing the collected MCG data for recording magnetocardiogram signals generated by the heart electrical activity; identifying the R peak of a QRS wave in the preprocessed MCG data, and carrying out period segmentation on the preprocessed MCG data; calculating a plurality of quality evaluation parameters associated with the MCG data to evaluate the quality of the magnetocardiogram signal based on the MCG data after the period division; judging the grades of the quality evaluation parameters according to a plurality of evaluation threshold values corresponding to the quality evaluation parameters; and evaluating the quality of the magnetocardiogram signal according to the grades of the plurality of quality evaluation parameters. The method overcomes the defect of low evaluation reliability of the existing signal quality evaluation method, improves the reliability of the quality evaluation of the magnetocardiogram signal, has high operation speed, and can be applied to the real-time analysis of the signal quality during MCG on-line acquisition.

Description

Magnetocardiogram signal quality evaluation method, system and server
Technical Field
The invention belongs to the field of biomedical signal analysis, relates to an evaluation method and an evaluation system, and particularly relates to a quality evaluation method, a quality evaluation system and a quality evaluation server for magnetocardiogram signals.
Background
The Magnetocardiograph (MCG) can record the evolution process of the magnetic field generated by the electrical activity of the heart over time, contains important information for evaluating the heart function, and can be used for diagnosing heart diseases such as coronary heart disease, myocardial infarction, arrhythmia and the like. However, the magnetocardiogram signal is very weak and is easily interfered by the external environment, and the quality of the magnetocardiogram signal can be weakened by the opened subway, the elevator moving up and down, and the like.
The quality of the magnetocardiogram signals is directly related to the reliability of the late-stage heart disease diagnosis, so that the quality of the magnetocardiogram signals needs to be evaluated, and if the evaluation result of the magnetocardiogram data acquired at this time is poor, an operator of the magnetocardiogram system is recommended to acquire the magnetocardiogram data again.
In the prior art, the signal quality evaluation method only divides the heart sound signals into five grades by one parameter (quality factor), and the classification reliability is not high due to insufficient classification basis;
or the prior art also includes a method for evaluating signal quality by qualitatively analyzing each of a plurality of noise sources in a physiological signal to obtain a corresponding plurality of qualitative ratings, however, the frequency range of magnetocardiogram signals is 0-100Hz, and the frequency range of some low-frequency noise sources, such as muscle noise sources (from unintentional muscle contraction of a patient due to anxiety), motion artifact noise sources (from patient movement), etc., is also within 0-100Hz, so that it is difficult to effectively extract these noises from magnetocardiogram data, and the reliability of the signal quality evaluation result is lowered, and thus, the method is not suitable for evaluating magnetocardiogram signal quality.
Therefore, it is an urgent technical problem to be solved by the practitioners in the art to provide a method, a system, and a server for evaluating quality of magnetocardiogram signals, so as to solve the defect in the prior art that quality of magnetocardiogram signals cannot be accurately evaluated according to characteristics of magnetocardiogram signals, and reliability of quality judgment results of magnetocardiogram signals is low.
Disclosure of Invention
In view of the above drawbacks of the prior art, an object of the present invention is to provide a method, a system, and a server for evaluating quality of magnetocardiogram signals, which are used to solve the problem that the reliability of quality judgment result of magnetocardiogram signals is not high due to the fact that quality of magnetocardiogram signals cannot be accurately evaluated according to characteristics of magnetocardiogram signals in the prior art.
To achieve the above and other related objects, an aspect of the present invention provides a quality evaluation method for magnetocardiogram signals, including the steps of: preprocessing the collected MCG data for recording magnetocardiogram signals generated by the heart electrical activity; identifying the R peak of a QRS wave in the preprocessed MCG data, and carrying out period segmentation on the preprocessed MCG data; calculating a plurality of quality evaluation parameters associated with the MCG data to evaluate the quality of the magnetocardiogram signal based on the MCG data after the period division; judging the grades of the quality evaluation parameters according to a plurality of evaluation threshold values corresponding to the quality evaluation parameters; and evaluating the quality of the magnetocardiogram signal according to the grades of the plurality of quality evaluation parameters.
In an embodiment of the present invention, the method for evaluating quality of magnetocardiogram signals further includes: preprocessing acquired ECG data used to record electrical signals generated by electrical activity of the heart; identifying the R peak of a QRS wave in the preprocessed ECG data, and carrying out cycle segmentation on the preprocessed ECG data; based on the cycle segmented ECG data, a quality assessment parameter is calculated comprising a plurality of quality assessment parameters associated with the MCG data and the ECG data for assessing the quality of the magnetocardiogram signal.
In an embodiment of the present invention, the step of performing cycle segmentation on the preprocessed MCG data and ECG data includes: calculating an average period of the preprocessed MCG data and ECG data; wherein the identified average period of the pre-processed MCG data and ECG data
Figure BDA0001084084810000021
RtiIs the time corresponding to the ith R peak in the preprocessed MCG data/ECG data, Rti+1The time corresponding to the i +1 th R peak in the preprocessed MCG data/ECG data; n is a radical of1The number of the identified R waves in the preprocessed MCG data/ECG data; taking the time corresponding to the R peak in the identified preprocessed MCG data and ECG data as a reference point, taking 0.4 average cycles before the reference point, and taking 0.6 average cycles after the reference point, so as to respectively perform cycle segmentation on the preprocessed MCG data and the ECG data to form a computing cycle, and segmenting the preprocessed MCG data and the ECG data according to the computing cycle.
In an embodiment of the invention, the quality assessment parameter associated with the MCG data and the ECG data includes an average correlation coefficient of the preprocessed MCG data and the ECG data; calculating average correlation coefficient of the preprocessed MCG data and ECG data
Figure BDA0001084084810000022
Comprises the following steps: calculating absolute correlation coefficient | r of preprocessed MCG data and ECG data in each calculation period i1,2,3 … N, i is the data of MCGCalculating the serial number of the periods;
Figure BDA0001084084810000023
wherein the content of the first and second substances,
xj,j＝1,2,3,...,M，xjthe data of the MCG in one calculation period is obtained, j is the serial number of the MCG data, and M is the total number of the MCG data in one calculation period;
Figure BDA0001084084810000025
for MCG data x in one calculation periodjAverage value of (d); y isj,j＝1,2,3,...,M，yjIs ECG data in a calculation period, and j is an ECG data serial number;
Figure BDA0001084084810000026
is y in one cyclejAverage value of (d); calculating the average correlation coefficient of the preprocessed MCG data and ECG data in all calculation periods
Figure BDA0001084084810000024
And N is the number of calculation cycles in the MCG data.
In an embodiment of the present invention, the quality assessment parameters associated with the MCG data and the ECG data further include a Ratio; the step of calculating the available proportion Ratio includes: judging the absolute correlation coefficient | r of the preprocessed MCG data and the ECG data in each calculation period according to a judgment criterioniIf is good or bad, the judgment criterion is
Figure BDA0001084084810000031
Wherein, sigma is a threshold value for judging the good and bad correlation coefficient, and ziIs the result of the discrimination; counting the number G of good absolute correlation coefficients of the preprocessed MCG data and the preprocessed ECG data in all calculation periods, and calculating the available proportion
Figure BDA0001084084810000032
And N is the number of calculation cycles in the MCG data.
In an embodiment of the present inventionWherein the quality assessment parameters associated with the MCG data further comprise an R variance of the pre-processed MCG data; the step of calculating the R variance of the preprocessed MCG data comprises: counting the variance R of R peaks of N calculation periods in the preprocessed MCG datavar，
Figure BDA0001084084810000033
RiFor the ith R peak in the pre-processed MCG data,
Figure BDA0001084084810000034
is the average of all R peaks.
In an embodiment of the present invention, the quality evaluation parameters associated with the MCG data further include a mean variance and a maximum variance of the preprocessed MCG data; the step of calculating the mean and maximum variance of the preprocessed MCG data comprises: calculating the variance of QRS wave base values in a calculation period of the preprocessed MCG data
Figure BDA00010840848100000313
i is 1,2,3 … N, which is the serial number of the calculated cycles in the MCG data; QRS wave base value B of preprocessed MCG data in one calculation periodjIs MCG data value within a preset base value time range, wherein the preset base value time range is T2-TMoment of start of cycle0.33T; wherein the content of the first and second substances,
Figure BDA0001084084810000035
k is a base value BjThe number of the (c) is,
Figure BDA0001084084810000036
is a base value BjAverage value of (d); variance of QRS wave base value in a calculation period according to preprocessed MCG data
Figure BDA0001084084810000037
Calculating the average base value variance of the preprocessed MCG data in all calculation periods
Figure BDA0001084084810000038
And maximum variance of the contributions: wherein the mean variance of the contributions
Figure BDA0001084084810000039
N is the number of calculation periods in MCG data; the maximum base value variance of the preprocessed MCG data is the QRS base value variance in all calculation periods
Figure BDA00010840848100000310
Is measured.
In an embodiment of the invention, the quality evaluation parameter associated with the MCG data further includes an average signal-to-noise ratio of the preprocessed MCG data
Figure BDA00010840848100000311
And SNRmin(ii) a Calculating average signal-to-noise ratio of preprocessed MCG data
Figure BDA00010840848100000312
And SNRminComprises the following steps: calculating the SNR of the preprocessed MCG data in each calculation periodiI is 1,2,3 … N, which is the serial number of the calculated cycles in the MCG data;
Figure BDA0001084084810000041
Rifor the R peak value of the ith calculation cycle of the preprocessed MCG data,
Figure BDA0001084084810000042
calculating the variance of the base value of the ith calculation period of the preprocessed MCG data; according to SNR of the preprocessed MCG data in each calculation periodiCalculating the average signal-to-noise ratio of the preprocessed MCG data
Figure BDA0001084084810000043
SNR of preprocessed MCG dataminFor all SNR calculation periodsiIs measured.
In an embodiment of the present invention, the grades of the quality evaluation parameters include a first grade, a second grade, and a third grade; the levels of magnetocardiogram signal quality include a first level, a second level, and a third level; the step of evaluating the quality of the magnetocardiogram signal according to the grade of the plurality of quality evaluation parameters comprises the following steps: judging whether the number of the quality evaluation parameters belonging to the first level is larger than a number threshold value of the level for representing the quality of the magnetocardiogram signal as the first level; if so, indicating that the quality grade of the magnetocardiogram signal is a first grade, and if not, continuously judging whether the number of the quality evaluation parameters belonging to a third grade is larger than a threshold value of the number for indicating that the grade of the quality of the magnetocardiogram signal is the third grade; if yes, the quality grade of the magnetocardiogram signal is represented as a third grade, and if not, the quality grade of the magnetocardiogram signal is represented as a second grade.
Another aspect of the present invention provides a quality evaluation system for magnetocardiogram signals, including: the preprocessing module is used for preprocessing the acquired MCG data used for recording the magnetocardiogram signals generated by the cardiac electrical activity; the segmentation module is used for identifying the R peak of the QRS wave in the preprocessed MCG data and periodically segmenting the preprocessed MCG data; the calculation module is used for calculating a plurality of quality evaluation parameters which are related to the MCG data and used for evaluating the quality of the magnetocardiogram signals based on the MCG data after the period division; the evaluation module is used for judging the grades of the quality evaluation parameters according to the evaluation threshold values of the quality evaluation parameters; and evaluating the quality of the magnetocardiogram signal according to the grades of the plurality of quality evaluation parameters.
The invention further provides a server which comprises the magnetocardiogram signal quality evaluation system.
As described above, the magnetocardiogram signal quality evaluation method, system and server of the present invention have the following advantages:
the quality evaluation method, the system and the server of the magnetocardiogram signal overcome the defect of low reliability of the quality evaluation of the magnetocardiogram signal by the existing signal quality evaluation method, and provide a reliable system and a method which are suitable for the quality evaluation of the magnetocardiogram signal. The invention comprehensively considers the magnetocardiogram signal characteristics, and proposes seven parameters from MCG and ECG data: the method has the advantages that the method comprises the steps of average correlation coefficient, the available proportion of MCG and ECG, R variance, average base value variance, maximum base value variance, average SNR, minimum SNR and the like of MCG, and utilizes a voting mode to give qualitative rating to the signal quality of MCG, so that the reliability of magnetocardiogram signal quality evaluation is improved, the operation speed is high, and the method can be applied to real-time analysis of the signal quality during MCG online acquisition.
Drawings
Fig. 1 is a flow chart illustrating a magnetocardiogram signal quality evaluation method according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart of step S3 in the method for evaluating quality of magnetocardiogram signals according to the present invention.
Fig. 3 is a schematic flow chart of step S6 in the method for evaluating quality of magnetocardiogram signals according to the present invention.
FIG. 4 is a diagram showing the magnetocardiogram signals with different noise contents and the quality evaluation results according to the present invention.
Fig. 5 is a schematic structural diagram of a magnetocardiogram signal quality evaluation system according to an embodiment of the present invention.
Fig. 6 is a schematic structural diagram of a server according to an embodiment of the present invention.
Description of the element reference numerals
1 magnetocardiogram signal quality evaluation system
11 preprocessing module
12 identification module
13 division module
14 calculation module
15 evaluation module
131 first dividing unit
132 second dividing unit
141 first calculation unit
142 second computing unit
143 third calculation unit
144 fourth calculation unit
145 fifth calculation unit
146 sixth calculation unit
147 seventh calculation Unit
S1-S6
S31-S33
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
Example one
The embodiment provides a quality evaluation method of magnetocardiogram signals, which comprises the following steps:
preprocessing the collected MCG data for recording magnetocardiogram signals generated by the heart electrical activity;
identifying the R peak of a QRS wave in the preprocessed MCG data, and carrying out period segmentation on the preprocessed MCG data;
calculating a plurality of quality evaluation parameters associated with the MCG data to evaluate the quality of the magnetocardiogram signal based on the MCG data after the period division;
judging the grades of the quality evaluation parameters according to the evaluation threshold values of the quality evaluation parameters;
and evaluating the quality of the magnetocardiogram signal according to the grades of the plurality of quality evaluation parameters.
The magnetocardiogram signal quality evaluation method according to the present embodiment will be described in detail with reference to the drawings. Before the quality evaluation method of the magnetocardiogram signals according to the present embodiment is performed, it is necessary to acquire the magnetocardiogram signals for recording the electrical activity of the heart and the ECG data for recording the electrical signals generated by the electrical activity of the heart by the magnetocardiogram apparatus and the electrocardiograph apparatus, respectively. Fig. 1 is a schematic flow chart of a magnetocardiogram signal quality evaluation method according to an embodiment. As shown in fig. 1, the quality evaluation method of magnetocardiogram signals specifically includes the following steps:
s1, pre-processing the acquired MCG data for recording magnetocardiogram signals generated by the cardiac electrical activity and the ECG data for recording electrical signals generated by the cardiac electrical activity. Specifically, the MCG data acquired for recording the magnetocardiogram signals generated by the electrical activity of the heart and the ECG data acquired for recording the electrical signals generated by the electrical activity of the heart are subjected to 100Hz low pass filtering and 50Hz notching.
S2, identifying the R peak of the QRS wave in the preprocessed MCG data and ECG data. In this embodiment, the first differentiation, the nonlinear transformation, and the second differentiation are performed on the MCG data and the ECG data after the preprocessing, and a point other than 0 is selected from the second differentiation data result g' (n) and is represented as the R peak of the QRS wave. In this embodiment, identifying the R peak of the QRS wave is a calculation of cycle segmentation and quality assessment parameters for subsequent MCG data and ECG data.
And S3, carrying out cycle segmentation on the preprocessed MCG data. Please refer to fig. 2, which is a flowchart illustrating the step S3. As shown in fig. 2, the step S3 specifically includes the following steps:
s31, calculating average period of the preprocessed MCG data and ECG data
Figure BDA0001084084810000071
(since one ECG data will correspond to one MCG data, the ECG data and MCG data averaging period are the same).
In this embodiment, the identified pre-processed MCG data andaverage period of ECG data
Figure BDA0001084084810000072
RtiIs the time corresponding to the ith R peak in the preprocessed MCG data/ECG data, Rti+1The time corresponding to the i +1 th R peak in the preprocessed MCG data/ECG data; n is a radical of1The number of R-waves in the identified pre-processed MCG data/ECG data. In this embodiment, the number of R-waves in the preprocessed MCG data is equal to the number of R-waves in the preprocessed ECG data.
And S32, taking the time corresponding to the R peak in the preprocessed MCG data and the ECG data as a reference point, taking 0.4 average cycles before the reference point and taking 0.6 average cycles after the reference point to respectively perform cycle segmentation on the preprocessed MCG data and the ECG data, recombining into a calculation cycle, and segmenting the preprocessed MCG data and the ECG data according to the calculation cycle.
S4, calculating a plurality of quality assessment parameters associated with the MCG data for assessing the quality of the magnetocardiogram signal based on the MCG data and the ECG data after the period division. In this embodiment, the quality assessment parameters further include quality assessment parameters associated with the MCG data and ECG data to assess quality of magnetocardiogram signals. Specifically, the quality evaluation parameters associated with the MCG data and the ECG data to evaluate quality of magnetocardiogram signals include an average correlation coefficient and an available Ratio of the MCG data and the ECG data preprocessed in each calculation cycle.
In the present embodiment, the average correlation coefficient of the preprocessed MCG data and ECG data is calculated for each calculation cycle
Figure BDA0001084084810000073
Comprises the following steps:
calculating absolute correlation coefficient | r of preprocessed MCG data and ECG data in each calculation periodiI is 1,2,3 … N, i is the serial number of the number of calculation cycles in the MCG data;
Figure BDA0001084084810000074
wherein the content of the first and second substances,
xj,j＝1,2,3,...,M，xjthe method comprises the steps that MCG data in a calculation period are obtained, j is the serial number of the MCG data, and M is the total number of the MCG data in the calculation period;
Figure BDA0001084084810000075
for MCG data x in one calculation periodjAverage value of (d); y isj,j＝1,2,3,...,M，yjIs ECG data in a calculation period, and j is an ECG data serial number;
Figure BDA0001084084810000076
is y in one cyclejAverage value of (d);
calculating the average correlation coefficient of the preprocessed MCG data and ECG data in all calculation periods
Figure BDA0001084084810000081
And N is the number of calculation cycles in the MCG data.
In this embodiment, the step of calculating the available proportion Ratio includes:
judging the absolute correlation coefficient | r of the preprocessed MCG data and the ECG data in each calculation period according to a judgment criterioniIf is good or bad, the judgment criterion is
Figure BDA0001084084810000082
Wherein, sigma is a threshold value for judging the good and bad correlation coefficient, and ziIs the result of the discrimination;
counting the number G of good absolute correlation coefficients of the preprocessed MCG data and the preprocessed ECG data in all calculation periods, and calculating the available proportion
Figure BDA0001084084810000083
And N is the number of calculation cycles in the MCG data.
In this embodiment, the quality assessment parameters associated with the MCG data to assess quality of magnetocardiogram signals include pre-processingR variance of the MCG data, mean variance of the pre-processed MCG data, maximum variance of the pre-processed MCG data, mean signal-to-noise ratio of the pre-processed MCG data
Figure BDA0001084084810000084
And/or SNR of the preprocessed MCG datamin。
Specifically, the step of calculating the R variance of the preprocessed MCG data includes:
counting the variance R of R peaks of N calculation periods in the preprocessed MCG datavar，
Figure BDA0001084084810000085
RiFor the ith R peak in the pre-processed MCG data,
Figure BDA0001084084810000086
is the average of all R peaks.
The step of calculating the mean and maximum variance of the preprocessed MCG data comprises:
calculating the variance of QRS wave base values in a calculation period of the preprocessed MCG data
Figure BDA00010840848100000811
i is 1,2,3 … N, which is the serial number of the calculated cycles in the MCG data; QRS wave base value B of preprocessed MCG data in one calculation periodjIs MCG data value within a preset base value time range, wherein the preset base value time range is T2-TMoment of start of cycle0.33T; wherein the content of the first and second substances,
Figure BDA0001084084810000087
k is a base value BjThe number of the (c) is,
Figure BDA0001084084810000088
is a base value BjAverage value of (a).
Variance of QRS wave base value in each calculation period according to preprocessed MCG data
Figure BDA0001084084810000089
Calculating the average base value variance of the preprocessed MCG data in all calculation periods
Figure BDA00010840848100000810
And the step of maximum variance of the basis values comprises:
calculating the average base value variance of the preprocessed MCG data in each calculation period
Figure BDA0001084084810000091
N is the number of calculation periods in MCG data;
the maximum base value variance of the preprocessed MCG data is the QRS base value variance in all calculation periods
Figure BDA0001084084810000092
Is measured.
Calculating average signal-to-noise ratio of preprocessed MCG data
Figure BDA0001084084810000093
And SNRminComprises the following steps:
calculating SNR of the preprocessed MCG data in each calculation periodiI is 1,2,3 … N, which is the serial number of the calculated cycles in the MCG data;
Figure BDA0001084084810000094
Rifor the R peak value of the ith calculation cycle of the preprocessed MCG data,
Figure BDA0001084084810000095
calculating the variance of the base value of the ith calculation period of the preprocessed MCG data;
according to SNR of the preprocessed MCG data in each calculation periodiCalculating the average signal-to-noise ratio of the preprocessed MCG data
Figure BDA0001084084810000096
After pretreatmentSNR of MCG dataminFor all SNR calculation periodsiIs measured.
And S5, judging the grades of the quality evaluation parameters according to the evaluation threshold values corresponding to the quality evaluation parameters. In this embodiment, the grades of the quality evaluation parameters include a first grade, a second grade, and a third grade, i.e., good, normal, and bad. Referring to table 1, a plurality of rating thresholds corresponding to the quality assessment parameters and an associated rating comparison table.
Table 1: a plurality of rating thresholds corresponding to the quality evaluation parameters and the corresponding grade comparison table
Figure BDA0001084084810000097
Figure BDA0001084084810000101
In this embodiment, the evaluation threshold values in table 1 are specifically shown in table 2.
Table 2: specific value of evaluation threshold
Figure BDA0001084084810000102
And S6, evaluating the quality of the magnetocardiogram signal according to the grades of the quality evaluation parameters. In this embodiment, the quality levels of the magnetocardiogram signals include a first level, a second level, and a third level, i.e., good quality level, normal quality level, and bad quality level. In the present embodiment, step S6 determines the level of magnetocardiogram signal quality by voting. Please refer to fig. 3, which is a flowchart illustrating the step S6. As shown in fig. 3, the step S6 includes:
s61, determining whether the number of quality evaluation parameters belonging to the first level is greater than the number threshold for indicating that the level of magnetocardiogram signal quality is the first level (in this embodiment, the number threshold indicating that the level of magnetocardiogram signal quality is superior is 4, or half of the total number of quality evaluation parameters); if so, it indicates that the quality level of the magnetocardiogram signal is the first level, otherwise, step S62 is executed to continuously determine whether the number of quality evaluation parameters belonging to the third level is greater than the number threshold for indicating that the level of the magnetocardiogram signal quality is the third level (in this embodiment, the number threshold for indicating that the level of the magnetocardiogram signal quality is poor is 4, or half of the total number of the quality evaluation parameters); if yes, the quality grade of the magnetocardiogram signal is represented as a third grade, and if no, the quality grade of the magnetocardiogram signal is represented as a second grade (the grade representing the quality of the magnetocardiogram signal is general). Please refer to fig. 4, which is a diagram illustrating the magnetocardiogram signals with different noise contents and the quality evaluation result. As shown in fig. 4, after the above evaluation, the quality level of the first magnetocardiogram signal is good, the level of the second magnetocardiogram signal is normal, and the level of the third magnetocardiogram signal is poor.
The quality evaluation method for the magnetocardiogram signals overcomes the defect that the existing signal quality evaluation method is not high in reliability in quality evaluation of the magnetocardiogram signals, and provides a reliable system and method suitable for quality evaluation of the magnetocardiogram signals. The invention comprehensively considers the magnetocardiogram signal characteristics, and proposes seven parameters from MCG and ECG data: the method has the advantages that the method comprises the steps of average correlation coefficient, the available proportion of MCG and ECG, R variance, average base value variance, maximum base value variance, average SNR, minimum SNR and the like of MCG, and utilizes a voting mode to give qualitative rating to the signal quality of MCG, so that the reliability of magnetocardiogram signal quality evaluation is improved, the operation speed is high, and the method can be applied to real-time analysis of the signal quality during MCG online acquisition.
Example two
Fig. 5 shows a schematic structural diagram of a quality evaluation system for magnetocardiogram signals in an embodiment. As shown in fig. 5, the magnetocardiogram signal quality evaluation system 1 includes: a preprocessing module 11, a recognition module 12, a segmentation module 13, a calculation module 14, and an evaluation module 15.
The preprocessing module 11 is used for preprocessing the acquired MCG data for recording magnetocardiogram signals generated by the cardiac electrical activity and the ECG data for recording electrical signals generated by the cardiac electrical activity. Specifically, the preprocessing module 11 performs 100Hz low-pass filtering and 50Hz notching on the collected MCG data for recording the magnetocardiogram signals generated by the cardiac electrical activity and the ECG data for recording the electrical signals generated by the cardiac electrical activity.
The identification module 12 connected to the preprocessing module 11 is used for identifying the R peak of the QRS wave in the preprocessed MCG data and ECG data. In this embodiment, the first differentiation, the nonlinear transformation, and the second differentiation are performed on the MCG data and the ECG data after the preprocessing, and a point other than 0 is selected from the second differentiation data result g' (n) and is represented as the R peak of the QRS wave. In this embodiment, identifying the R peak of the QRS wave is a calculation of cycle segmentation and quality assessment parameters for subsequent MCG data and ECG data.
And the segmentation module 13 connected with the identification module 12 is used for periodically segmenting the preprocessed MCG data. The splitting module 13 includes a first splitting unit 131 and a second splitting unit 132.
The first dividing unit 131 is specifically configured to:
calculating the average period of the preprocessed MCG data
Figure BDA0001084084810000111
(since one ECG data will correspond to one MCG data, the ECG data and MCG data averaging period are the same).
Taking the time corresponding to the R peak in the pre-processed MCG data as a reference point, taking 0.4 period before the reference point and taking 0.6 period after the reference point, carrying out period division on the pre-processed MCG data, recombining into a calculation period, and dividing the pre-processed MCG data according to the calculation period.
The second dividing unit 132 is specifically configured to:
calculating an average period of the pre-processed ECG data
Figure BDA0001084084810000112
(since one ECG data corresponds to one MCG data, the ECG data and the MCG dataThe data averaging period is the same).
In this embodiment, the identified average period of the pre-processed MCG data and ECG data
Figure BDA0001084084810000121
RtiIs the time corresponding to the ith R peak in the preprocessed MCG data/ECG data, Rti+1The time corresponding to the i +1 th R peak in the preprocessed MCG data/ECG data; n is a radical of1The number of R-waves in the identified pre-processed MCG data/ECG data. In this embodiment, the number of R-waves in the preprocessed MCG data is equal to the number of R-waves in the preprocessed ECG data.
Taking the time corresponding to the R peak in the identified preprocessed MCG data as a reference point, taking 0.4 average cycles before the reference point, and taking 0.6 average cycles after the reference point, so as to perform cycle segmentation on the preprocessed and ECG data, and recombining into a calculation cycle, and segmenting the preprocessed ECG data according to the calculation cycle.
The computation module 14 connected to the segmentation module 13 is configured to compute a plurality of quality assessment parameters associated with the MCG data to assess quality of magnetocardiogram signals based on the MCG data after the period segmentation. In this embodiment, the quality assessment parameters further include quality assessment parameters associated with the MCG data and ECG data to assess quality of magnetocardiogram signals. Specifically, the quality evaluation parameters associated with the MCG data and the ECG data to evaluate quality of magnetocardiogram signals include an average correlation coefficient and an available Ratio of the MCG data and the ECG data preprocessed in each calculation cycle. In the present embodiment, the calculating module 14 includes a first calculating unit 141, a second calculating unit 142, a third calculating unit 143, a fourth calculating unit 144, a fifth calculating unit 145, a sixth calculating unit 146, and a seventh calculating unit 147.
In this embodiment, the first calculating unit 141 connected to the second dividing unit 132 is used for calculating the average correlation coefficient of the preprocessed MCG data and ECG data in each calculating period
Figure BDA0001084084810000122
The specific calculation process is as follows:
calculating absolute correlation coefficient | r of preprocessed MCG data and ECG data in each calculation periodiI is 1,2,3 … N, i is the serial number of the number of calculation cycles in the MCG data;
Figure BDA0001084084810000123
wherein the content of the first and second substances,
xjj is 1,2, 3., M is the serial number of the MCG data in one calculation period, and M is the total number of the MCG data in one calculation period;
Figure BDA0001084084810000124
for MCG data x in one calculation periodjAverage value of (d); y isjJ is 1,2, 3.. M is the serial number of the ECG data in one calculation cycle;
Figure BDA0001084084810000125
is y in one cyclejAverage value of (d);
calculating the average correlation coefficient of the preprocessed MCG data and the ECG data in each calculation period
Figure BDA0001084084810000126
And N is the number of calculation cycles in the MCG data.
In this embodiment, the second calculating unit 142 is configured to calculate the available Ratio, and the specific calculation process is as follows:
judging the absolute correlation coefficient | r of the preprocessed MCG data and the ECG data in each calculation period according to a judgment criterioniIf is good or bad, the judgment criterion is
Figure BDA0001084084810000131
Wherein, sigma is a threshold value for judging the good and bad correlation coefficient, and ziIs the result of the discrimination;
counting the preprocessed M in all computing periodsThe number G of good absolute correlation coefficients between CG data and ECG data, and the available ratio is calculated
Figure BDA0001084084810000132
And N is the number of calculation cycles in the MCG data.
In the embodiment, the quality evaluation parameters associated with the MCG data for evaluating the quality of magnetocardiogram signals include R variance of the preprocessed MCG data, mean variance of the basis values of the preprocessed MCG data, maximum variance of the basis values of the preprocessed MCG data, and mean signal-to-noise ratio of the preprocessed MCG data
Figure BDA0001084084810000133
And/or SNR of the preprocessed MCG datamin。
Specifically, the third calculating unit 143 is configured to calculate an R variance of the preprocessed MCG data. The specific calculation process comprises the following steps:
counting the variance R of R peaks of N calculation periods in the preprocessed MCG datavar，
Figure BDA0001084084810000134
RiFor the ith R peak in the pre-processed MCG data,
Figure BDA0001084084810000135
is the average of all R peaks.
The step of calculating the mean and maximum variance of the preprocessed MCG data comprises:
calculating the variance of QRS wave base values in a calculation period of the preprocessed MCG data
Figure BDA0001084084810000136
i is 1,2,3 … N, which is the serial number of the calculated cycles in the MCG data; QRS wave base value B of preprocessed MCG data in one calculation periodjIs MCG data value within a preset base value time range, wherein the preset base value time range is T2-TMoment of start of cycle0.33T; wherein the content of the first and second substances,
Figure BDA0001084084810000137
k is a base value BjThe number of the (c) is,
Figure BDA0001084084810000138
is a base value BjAverage value of (a).
The fourth calculating unit 144 and the fifth calculating unit 145 are used for calculating the variance of the QRS base value in one calculating period according to the preprocessed MCG data
Figure BDA0001084084810000139
Calculating the average base value variance of the preprocessed MCG data in each calculation period
Figure BDA00010840848100001310
And a maximum variance of the contributions.
The specific calculation process of the fourth calculation unit 144 includes:
calculating the average base value variance of the preprocessed MCG data in each calculation period
Figure BDA0001084084810000141
N is the number of calculation periods in MCG data;
the fifth calculating unit 145 is configured to select the maximum variance of the base values of the preprocessed MCG data as the variance of the QRS base values in all calculating periods
Figure BDA0001084084810000142
Is measured.
The sixth calculating unit 146 and the seventh calculating unit 147 are used for calculating the average signal-to-noise ratio of the preprocessed MCG data
Figure BDA0001084084810000143
And SNRmin. The specific calculation process of the sixth calculation unit 146 includes:
calculating SNR of the preprocessed MCG data in each calculation periodiI is 1,2,3 … N, which is the serial number of the calculated cycles in the MCG data;
Figure BDA0001084084810000144
Rifor the R peak value of the ith calculation cycle of the preprocessed MCG data,
Figure BDA0001084084810000145
calculating the variance of the base value of the ith calculation period of the preprocessed MCG data;
according to SNR of the preprocessed MCG data in each calculation periodiCalculating the average signal-to-noise ratio of the preprocessed MCG data
Figure BDA0001084084810000146
The seventh calculating unit 147 is used for selecting SNR of the preprocessed MCG dataminFor all SNR calculation periodsiIs measured.
The evaluation module 15 connected to the calculation module 14 is configured to determine the grades of the quality evaluation parameters according to a plurality of rating threshold values corresponding to the quality evaluation parameters. In this embodiment, the grades of the quality evaluation parameters include a first grade, a second grade, and a third grade, i.e., good, normal, and bad. The evaluation module 15 is further configured to evaluate quality of the magnetocardiogram signal according to the grade of the plurality of quality evaluation parameters. In this embodiment, the quality levels of the magnetocardiogram signals include a first level, a second level, and a third level, i.e., good quality level, normal quality level, and bad quality level.
The embodiment further provides a server 2, the server 2 is connected to the electrocardiograph and the magnetocardiogram apparatus, please refer to fig. 6, which is a schematic diagram of a schematic structure of the server in an embodiment. As shown in fig. 6, the server 2 includes the magnetocardiogram signal quality evaluation system 1.
In summary, the magnetocardiogram signal quality evaluation method, system and server provided by the invention overcome the defect of low reliability of the existing signal quality evaluation method for evaluating magnetocardiogram signal quality, and provide a reliable system and method suitable for magnetocardiogram signal quality evaluation. The invention comprehensively considers the magnetocardiogram signal characteristics, and proposes seven parameters from MCG and ECG data: the method has the advantages that the method comprises the steps of average correlation coefficient, the available proportion of MCG and ECG, R variance, average base value variance, maximum base value variance, average SNR, minimum SNR and the like of MCG, and utilizes a voting mode to give qualitative rating to the signal quality of MCG, so that the reliability of magnetocardiogram signal quality evaluation is improved, the operation speed is high, and the method can be applied to real-time analysis of the signal quality during MCG online acquisition. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.

Claims (9)

1. A quality evaluation method for magnetocardiogram signals is characterized by comprising the following steps:
preprocessing the collected MCG data and ECG data for recording the magnetocardiogram signals generated by the heart electrical activity;
identifying R peaks of QRS waves in the preprocessed MCG data and the ECG data, and carrying out period segmentation on the preprocessed MCG data;
calculating a plurality of quality evaluation parameters associated with the MCG data and the ECG data for evaluating quality of magnetocardiogram signals based on the MCG data and the ECG data after cycle segmentation; the quality assessment parameters associated with the MCG data and ECG data comprise average correlation coefficients of the pre-processed MCG data and ECG data; wherein the average correlation coefficient of the preprocessed MCG data and ECG data is calculated
Figure FDA0002510450250000016
Comprises the following steps:
calculating absolute correlation coefficient | r of preprocessed MCG data and ECG data in each calculation periodiI is 1,2,3 … N, i is the serial number of the number of calculation cycles in the MCG data;
Figure FDA0002510450250000011
wherein x isj,j＝1,2,3,...,M，xjThe method comprises the steps that MCG data in a calculation period are obtained, j is the serial number of the MCG data, and M is the total number of the MCG data in the calculation period;
Figure FDA0002510450250000012
for MCG data x in one calculation periodjAverage value of (d); y isj,j＝1,2,3,...,M，yjIs ECG data in a calculation cycle, j is the serial number of the ECG data;
Figure FDA0002510450250000013
is y in one cyclejAverage value of (d);
calculating the average correlation coefficient of MCG data and ECG data of all calculation periods after preprocessing
Figure FDA0002510450250000014
N is the total number of calculation periods in MCG data;
judging the grades of the quality evaluation parameters according to a plurality of evaluation threshold values corresponding to the quality evaluation parameters;
and evaluating the quality of the magnetocardiogram signal according to the grades of the plurality of quality evaluation parameters.
2. The magnetocardiogram signal quality evaluation method according to claim 1, wherein: the step of performing a periodic segmentation of the preprocessed MCG data and ECG data comprises:
calculating an average period of the preprocessed MCG data and ECG data; the identified average period of the pre-processed MCG data and ECG data
Figure FDA0002510450250000015
RtiIs the time corresponding to the ith R peak in the preprocessed MCG data/ECG data, Rti+1The time corresponding to the i +1 th R peak in the preprocessed MCG data/ECG data; n is a radical of1The number of the identified R waves in the preprocessed MCG data/ECG data;
taking the time corresponding to the R peak in the identified preprocessed MCG and ECG data as a reference point, taking 0.4 average cycles before the reference point, and taking 0.6 average cycles after the reference point, so as to respectively perform cycle segmentation on the preprocessed MCG data and the ECG data to form a computing cycle, and segmenting the preprocessed MCG data and the ECG data according to the computing cycle.
3. The magnetocardiogram signal quality evaluation method according to claim 1, wherein: the quality assessment parameters associated with the MCG data and ECG data further comprise an available Ratio; the step of calculating the available proportion Ratio includes:
judging the absolute correlation coefficient | r of the preprocessed MCG data and the ECG data in each calculation period according to a judgment criterioniIf is good or bad, the judgment criterion is
Figure FDA0002510450250000021
Wherein, sigma is a threshold value for judging the good and bad correlation coefficient, and ziIs the result of the discrimination;
counting the number G of good absolute correlation coefficients of the preprocessed MCG data and the preprocessed ECG data in all calculation periods, and calculating the available proportion
Figure FDA0002510450250000022
And N is the number of calculation cycles in the MCG data.
4. The magnetocardiogram signal quality evaluation method according to claim 1, wherein: the quality assessment parameters associated with the MCG data further include an R variance of the pre-processed MCG data;
the step of calculating the R variance of the preprocessed MCG data comprises:
counting the variance R of R peaks of N calculation periods in the preprocessed MCG datavar，
Figure FDA0002510450250000023
RiFor the ith R peak in the pre-processed MCG data,
Figure FDA0002510450250000024
is the average of all R peaks.
5. The magnetocardiogram signal quality evaluation method according to claim 1, wherein: the quality assessment parameters associated with the MCG data further comprise a mean variance and a maximum variance of the preprocessed MCG data;
the step of calculating the mean and maximum variance of the preprocessed MCG data comprises:
calculating the variance of QRS wave base values in a calculation period of the preprocessed MCG data
Figure FDA0002510450250000025
Calculating the serial number of the periods in the MCG data; QRS wave base value B of preprocessed MCG data in one calculation periodjIs MCG data value in a preset base value time range, wherein the preset base value time range is from one period starting time to 0.33T, and T is a calculation period; wherein the content of the first and second substances,
Figure FDA0002510450250000031
k is a base value BjThe number of the (c) is,
Figure FDA0002510450250000032
is a base value BjAverage value of (d);
according to the variance of the QRS wave base value in a calculation period of the preprocessed MCG data
Figure FDA0002510450250000033
Calculating the average base value variance of the preprocessed MCG data in all calculation periods
Figure FDA0002510450250000034
And a maximum variance of the contributions; wherein the mean variance of the contributions
Figure FDA0002510450250000035
N is the number of calculation periods in MCG data;
the maximum base value variance of the preprocessed MCG data is the QRS base value variance in all calculation periods
Figure FDA0002510450250000036
Is measured.
6. The magnetocardiogram signal quality evaluation method according to claim 1, wherein: the quality assessment parameters associated with the MCG data further include an average signal-to-noise ratio of the pre-processed MCG data
Figure FDA0002510450250000037
And SNRmin；
Calculating average signal-to-noise ratio of preprocessed MCG data
Figure FDA0002510450250000038
And SNRminComprises the following steps:
calculating the SNR of the preprocessed MCG data in each calculation periodiI is 1,2,3 … N, which is the serial number of the calculated cycles in the MCG data;
Figure FDA0002510450250000039
Rifor the R peak value of the ith calculation cycle of the preprocessed MCG data,
Figure FDA00025104502500000310
calculating the variance of the base value of the ith calculation period of the preprocessed MCG data;
according to SNR of the preprocessed MCG data in each calculation periodiCalculating the average signal-to-noise ratio of the preprocessed MCG data
Figure FDA00025104502500000311
SNR of preprocessed MCG dataminFor all SNR calculation periodsiIs measured.
7. The magnetocardiogram signal quality evaluation method according to claim 1, wherein: the grades of the quality evaluation parameters comprise a first grade, a second grade and a third grade; the levels of magnetocardiogram signal quality include a first level, a second level, and a third level; the step of evaluating the quality of the magnetocardiogram signal according to the grade of the plurality of quality evaluation parameters comprises the following steps:
judging whether the number of the quality evaluation parameters belonging to the first level is larger than a number threshold value of the level for representing the quality of the magnetocardiogram signal as the first level; if so, indicating that the quality grade of the magnetocardiogram signal is a first grade, and if not, continuously judging whether the number of the quality evaluation parameters belonging to a third grade is larger than a threshold value of the number for indicating that the grade of the quality of the magnetocardiogram signal is the third grade; if yes, the quality grade of the magnetocardiogram signal is represented as a third grade, and if not, the quality grade of the magnetocardiogram signal is represented as a second grade.
8. A magnetocardiogram signal quality evaluation system, comprising:
the preprocessing module is used for preprocessing the acquired MCG data and ECG data for recording the magnetocardiogram signals generated by the cardiac electrical activity;
the segmentation module is used for identifying the R peak of the QRS wave in the preprocessed MCG data and the ECG data and carrying out periodic segmentation on the preprocessed MCG data;
computing module for basisCalculating a plurality of quality evaluation parameters which are related to the MCG data and used for evaluating the quality of the magnetocardiogram signals in the MCG data and the ECG data after the period division; the quality assessment parameters associated with the MCG data and ECG data comprise average correlation coefficients of the pre-processed MCG data and ECG data; wherein the calculation module calculates an absolute correlation coefficient | r of the preprocessed MCG data and the ECG data in each calculation cycleiI is 1,2,3 … N, i is the serial number of the number of calculation cycles in the MCG data;
Figure FDA0002510450250000041
wherein x isj,j＝1,2,3,...,M，xjThe method comprises the steps that MCG data in a calculation period are obtained, j is the serial number of the MCG data, and M is the total number of the MCG data in the calculation period;
Figure FDA0002510450250000042
for MCG data x in one calculation periodjAverage value of (d); y isj,j＝1,2,3,...,M，yjIs ECG data in a calculation cycle, j is the serial number of the ECG data;
Figure FDA0002510450250000043
is y in one cyclejAverage value of (d);
calculating the average correlation coefficient of MCG data and ECG data of all calculation periods after preprocessing
Figure FDA0002510450250000044
N is the total number of calculation periods in MCG data;
the evaluation module is used for judging the grades of the quality evaluation parameters according to the evaluation threshold values of the quality evaluation parameters; and evaluating the quality of the magnetocardiogram signal according to the grades of the plurality of quality evaluation parameters.
9. A server, characterized by comprising a magnetocardiogram signal quality evaluation system as claimed in claim 8.
CN201610692907.8A 2016-08-19 2016-08-19 Magnetocardiogram signal quality evaluation method, system and server Active CN107753012B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201610692907.8A CN107753012B (en) 2016-08-19 2016-08-19 Magnetocardiogram signal quality evaluation method, system and server

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201610692907.8A CN107753012B (en) 2016-08-19 2016-08-19 Magnetocardiogram signal quality evaluation method, system and server

Publications (2)

Publication Number Publication Date
CN107753012A CN107753012A (en) 2018-03-06
CN107753012B true CN107753012B (en) 2020-10-09

Family

ID=61262176

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201610692907.8A Active CN107753012B (en) 2016-08-19 2016-08-19 Magnetocardiogram signal quality evaluation method, system and server

Country Status (1)

Country Link
CN (1) CN107753012B (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109998546A (en) * 2019-01-25 2019-07-12 芯海科技（深圳）股份有限公司 A kind of evaluation method of human body impedance signal's mass
CN111973172B (en) * 2020-08-28 2021-10-08 北京航空航天大学 Cardiac structure imaging system and method based on MCG and ECG fusion
CN113317793B (en) * 2021-06-11 2023-02-17 宁波大学 Magnetocardiogram high-frequency signal analysis method, storage medium, and electronic device
CN113974576B (en) * 2021-12-23 2022-04-22 北京航空航天大学杭州创新研究院 Sleep quality monitoring system and monitoring method based on magnetocardiogram

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103020472B (en) * 2012-12-27 2015-12-09 中国科学院深圳先进技术研究院 Based on physiological signal quality evaluation method and the system of restrained split-flow
CN103431856B (en) * 2013-08-30 2015-09-16 深圳市理邦精密仪器股份有限公司 Method and the device of cardiac diagnosis lead is selected in multi-lead synchronous electrocardiosignal
CN105726013A (en) * 2016-01-27 2016-07-06 浙江铭众科技有限公司 Electrocardiogram monitoring system with electrocardiosignal quality discrimination function

Also Published As

Publication number Publication date
CN107753012A (en) 2018-03-06

Similar Documents

Publication Publication Date Title
Sangaiah et al. An intelligent learning approach for improving ECG signal classification and arrhythmia analysis
CN111107785B (en) Detection of atrial fibrillation using short single lead ECG recordings
CN107753012B (en) Magnetocardiogram signal quality evaluation method, system and server
US9949653B2 (en) Patient signal analysis based on vector analysis
CN107837082A (en) Electrocardiogram automatic analysis method and device based on artificial intelligence self study
Gavrovska et al. Classification of prolapsed mitral valve versus healthy heart from phonocardiograms by multifractal analysis
WO2007130997A2 (en) Method and device for filtering, segmenting, compressing and classifying oscillatory signals
CN110477906B (en) Electrocardiosignal QRS wave start and stop point positioning method
Kumar et al. Detection and classification of ECG noises using decomposition on mixed codebook for quality analysis
CN109745035B (en) Electrocardiosignal waveform detection method
CN109948396B (en) Heart beat classification method, heart beat classification device and electronic equipment
US20220110590A1 (en) System and Method for Assessing Physiological Signal Quality
CN114469124B (en) Method for identifying abnormal electrocardiosignals in movement process
CN110840443B (en) Electrocardiosignal processing method, electrocardiosignal processing device and electronic equipment
Bruun et al. Automatic atrial fibrillation detection: A novel approach using discrete wavelet transform and heart rate variability
CN114190944A (en) Robust emotion recognition method based on electroencephalogram signals
CN109044347B (en) Method, device and system for identifying junctional escape of electrocardiowave image and electronic equipment
CN115486849A (en) Electrocardiosignal quality evaluation method, device and equipment
CN100342820C (en) Method and apparatus for detecting, and analysing heart rate variation predication degree index
CN110811673A (en) Heart sound analysis system based on probabilistic neural network model
Kropf et al. A robust algorithm for fetal QRS detection using non-invasive maternal abdomenal ECGs
CN108836312B (en) Clutter rejection method and system based on artificial intelligence
Chen Complexity-measure-based sequential hypothesis testing for real-time detection of lethal cardiac arrhythmias
CN111345815A (en) Method, device, equipment and storage medium for detecting QRS wave in electrocardiosignal
CN117281529B (en) Electrocardiogram waveform data intelligent processing method

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant