CN117137492B - Coronary artery blood flow abnormality detection system, storage medium, and terminal - Google Patents
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Abstract
The invention discloses a coronary artery blood flow abnormality detection system, a storage medium and a terminal, and relates to the technical field of image processing. The detection system comprises: and a data acquisition module: the method comprises the steps of obtaining initial data of a magnetocardiogram, preprocessing the initial data of the magnetocardiogram, and obtaining one-dimensional butterfly images corresponding to P wave, QRS wave, ST wave and T wave bands; and an image construction module: the method comprises the steps of respectively obtaining a two-dimensional isomagnetic map, a current density map, a gradient field, a rotation field and a divergence field corresponding to a P wave, a QRS wave, a ST wave and a T wave band; and the feature extraction module is used for: extracting characteristic parameters related to coronary artery blood flow abnormality; and a feature screening module: the method comprises the steps of preprocessing, evaluating and selecting the quantity of extracted characteristic parameters to obtain a characteristic subset; and a judgment and comparison module: and judging whether coronary blood flow of the tested person is normal or not according to the feature subset. The invention has high sensitivity and high accuracy in coronary artery blood flow abnormality detection.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a coronary artery blood flow abnormality detection system, a storage medium and a terminal.
Background
Myocardial ischemia is a common heart disease, the morbidity and mortality rate of which rises year by year in recent years, and coronary artery blood flow abnormality is an important index for distinguishing myocardial ischemia, and qualitative and positioning identification of coronary artery blood flow abnormality is of great value for understanding the incidence of myocardial ischemia, predicting the disease development and guiding the treatment scheme.
At present, coronary angiography (Coronary angiography, CAG) and CT vascular imaging (CTA) are commonly used clinically to judge coronary conditions, and according to the results QFR and CTFFR analysis of the CAG and CTA, the blood flow conditions of the coronary arteries are judged, but the traditional methods are invasive, high in cost and long in time, and the X-rays or angiography agents bring great side effects to patients. In particular, for patients suffering from myocardial ischemia with renal insufficiency, the limitations of the conventional detection means are more stringent. Therefore, the establishment of a set of noninvasive, non-contact, noninvasive method and system for identifying coronary blood flow anomalies through noninvasive, non-invasive, qualitative and positioning of human bodies has important clinical value.
In addition, there are also features of signals of the magnetocardiogram, which assist in positioning the myocardial ischemia lesion according to feature parameters sensitive to the myocardial ischemia position, but the extracted feature parameters are only specific to the myocardial ischemia lesion, and the accuracy and sensitivity are poor.
Disclosure of Invention
The invention provides a coronary artery blood flow abnormality detection system with high sensitivity and high accuracy, a storage medium and a terminal.
In order to solve the technical problems, the invention provides the following technical scheme:
a coronary artery blood flow abnormality detection system comprising:
and a data acquisition module: the method comprises the steps of obtaining initial data of a magnetocardiogram, preprocessing the initial data of the magnetocardiogram, and obtaining one-dimensional butterfly images corresponding to P wave, QRS wave, ST wave and T wave bands;
and an image construction module: the method comprises the steps of respectively acquiring a two-dimensional isomagnetic map, a current density map, a gradient field, a rotation field and a divergence field corresponding to a P wave, a QRS wave, an ST wave and a T wave band based on the acquired one-dimensional butterfly map and the positions of each magnetocardiogram channel;
and the feature extraction module is used for: the method is used for extracting characteristic parameters related to coronary artery blood flow abnormality from a one-dimensional butterfly diagram, a two-dimensional isomagnetic diagram, a current density diagram, a gradient field, a rotation field and a divergence field corresponding to a P wave, a QRS wave, an ST wave and a T wave band respectively;
and a feature screening module: the method comprises the steps of preprocessing, evaluating and selecting the quantity of extracted characteristic parameters to obtain a characteristic subset;
and a judgment and comparison module: judging whether coronary blood flow of the tested person is normal or not according to the feature subset;
the characteristic parameters comprise basic classes, extreme value classes, vector classes and gravity center classes.
Further, the image construction module includes:
a first image construction module: the method comprises the steps of respectively drawing two-dimensional isomagnetic graphs corresponding to P wave, QRS wave, ST wave and T wave bands based on the obtained one-dimensional butterfly graph and the positions of each magnetocardiogram, and respectively calculating current density graphs corresponding to the P wave, the QRS wave, the ST wave and the T wave bands based on the two-dimensional isomagnetic graphs;
and a second image construction module: the gradient value and the rotation value of the vector signal at each position in the current density diagram corresponding to the P wave, the QRS wave, the ST wave and the T wave band are respectively calculated according to the gradient formula and the rotation formula, and the gradient field and the rotation field corresponding to the P wave, the QRS wave, the ST wave and the T wave band are respectively drawn according to the gradient value and the rotation value;
and a third image construction module: the method is used for calculating and obtaining the divergence value of the vector signal of each position in the gradient fields corresponding to the P wave, the QRS wave, the ST wave and the T wave band according to the divergence formula, and drawing and obtaining the divergence fields corresponding to the P wave, the QRS wave, the ST wave and the T wave band according to the divergence value.
Further, the characteristic parameters of the basic class include a field value range, an amplitude ratio sum difference of a positive field and a negative field, and an area ratio sum difference of a positive field and a negative field;
the characteristic parameters of the extremum class comprise the positions of the positive field maximum value and the positions of the negative field maximum value and the maximum value;
the characteristic parameters of the vector class comprise vector directions and amplitudes of the maximum value of the value field pointing to the maximum value of the positive value field, rectangular perimeter and area surrounded by vectors of the maximum value of the negative value field pointing to the maximum value of the positive value field, amplitude, direction and position of the maximum vector, rectangular perimeter and area surrounding the maximum vector, direction and amplitude of the total vector and rectangular perimeter and area surrounding the total vector;
the characteristic parameters of the gravity center class comprise a positive field gravity center position, a positive field maximum position, a distance and a direction of a positive field gravity center position connecting line, a negative field gravity center position, a negative field maximum position, a distance and a direction of a negative field gravity center position connecting line and a distance and a direction of a positive and negative gravity center position connecting line.
Further, the feature subset includes:
in the divergence field corresponding to the T wave:
the ratio between the vector direction of the negative value field maximum value of the point T2 and the vector direction of the positive value field maximum value of the point T1, the ratio between the connecting line direction of the negative value field gravity center and the positive value field gravity center of the point T2 and the connecting line direction of the positive value field gravity center and the positive value field gravity center of the point T2, and the ratio between the area ratio between the positive value field and the negative value field of the point T3 and the area ratio between the positive value field and the negative value field of the point T wave top point;
the variation coefficient of the amplitude difference between the positive value field and the negative value field in the rotation field corresponding to the T wave;
the variance of the rectangular area surrounding the maximum gradient vector in the gradient field corresponding to the vertex to the end point of the T wave;
the m-th histogram of vector magnitude of the negative value field maximum point to the positive value field maximum in the two-dimensional isomagnetic map corresponding to the QRS wave 1 M-th of histogram of vector magnitude in which negative field maximum points to positive field maximum in two-dimensional isomagnetic map in which each bin value corresponds to a T wave 2 The ratio between the bin values, the ratio between the minimum value of the vector direction of the maximum value of the negative value field pointing to the maximum value of the positive value field in the two-dimensional isomagnetic diagram corresponding to the QRS wave and the minimum value of the vector direction of the maximum value of the negative value field pointing to the maximum value of the positive value field in the two-dimensional isomagnetic diagram corresponding to the T wave;
the ratio of the transformation index of the total gradient vector direction in the gradient field corresponding to the QRS wave to the transformation index of the total gradient vector direction in the gradient field corresponding to the T wave;
the mth of the histogram of the amplitude difference between the positive field and the negative field in the two-dimensional equal magnetic field corresponding to the ST-segment wave 3 M-th histogram of amplitude difference between positive field and negative field in two-dimensional equal magnetic field with bin value corresponding to T wave 4 The ratio between the individual bin values;
in the corresponding divergence field of the QRS wave:
a peak factor of the amplitude ratio of the Renyi entropy, positive field and negative field of the vector distance of the positive and negative gravity center connection line;
in the one-dimensional butterfly graph corresponding to the P wave and the T wave respectively:
nth (N) 1 Absolute mean of waveform of channel, nth 2 Absolute maximum of waveform of the channel;
variance of minimum value of positive value field in two-dimensional isomagnetic map corresponding to P wave starting point to vertex;
n in one-dimensional butterfly graph corresponding to QT interval 3 Mth of LBP binarized statistical histogram of channel 5 And a bin value.
A storage medium storing a computer program which, when executed by a processor, implements a coronary blood flow abnormality detection method,
the coronary artery blood flow abnormality detection method comprises the following steps:
acquiring initial data of a magnetocardiogram, preprocessing the initial data of the magnetocardiogram, and acquiring one-dimensional butterfly images corresponding to P wave, QRS wave, ST wave and T wave bands;
based on the obtained magnetocardiogram data set and the positions of each magnetocardiogram channel, respectively obtaining a two-dimensional isomagnetic map, a current density map, a gradient field, a rotation field and a divergence field corresponding to a P wave, a QRS wave, a ST wave and a T wave band;
extracting characteristic parameters related to coronary artery blood flow from a one-dimensional butterfly diagram, a two-dimensional isomagnetic diagram, a current density diagram, a gradient field, a rotation field and a divergence field corresponding to a P wave, a QRS wave, a ST wave and a T wave band respectively;
preprocessing, evaluating and selecting the quantity of the extracted characteristic parameters to obtain a characteristic subset;
judging whether coronary blood flow is normal or not according to the feature subset;
wherein the characteristic parameters include: basic class, extremum class, vector class, gravity center class.
Further, the steps of respectively acquiring the two-dimensional isomagnetic map, the current density map, the gradient field, the rotation field and the divergence field corresponding to the P wave, the QRS wave, the ST wave and the T wave band based on the acquired one-dimensional butterfly map and the positions of each magnetocardiogram channel include:
respectively drawing two-dimensional isomagnetic maps corresponding to P wave, QRS wave, ST wave and T wave bands based on the obtained one-dimensional butterfly map and the positions of each magnetocardiogram channel, and respectively calculating current density maps corresponding to the P wave, the QRS wave, the ST wave and the T wave bands based on the two-dimensional isomagnetic maps;
respectively calculating a gradient value and a rotation value of a vector signal at each position in a current density diagram corresponding to the P wave, the QRS wave, the ST wave and the T wave band according to the gradient formula and the rotation formula, and respectively drawing a gradient field and a rotation field corresponding to the P wave, the QRS wave, the ST wave and the T wave band according to the gradient value and the rotation value;
calculating and obtaining the divergence value of the vector signal of each position in the gradient fields corresponding to the P wave, the QRS wave, the ST wave and the T wave band according to the divergence formula, and drawing and obtaining the divergence fields corresponding to the P wave, the QRS wave, the ST wave and the T wave band according to the divergence value.
Further, the characteristic parameters of the basic class include a field value range, an amplitude ratio and an amplitude difference of a positive field and a negative field, and an area ratio and an area difference of a positive field and a negative field;
the characteristic parameters of the extremum class comprise the positions of the positive field maximum value and the positions of the negative field maximum value and the maximum value;
the characteristic parameters of the vector class comprise vector directions and amplitudes of the maximum value of the value field pointing to the maximum value of the positive value field, rectangular perimeter and area surrounded by vectors of the maximum value of the negative value field pointing to the maximum value of the positive value field, amplitude, direction and position of the maximum vector, rectangular perimeter and area surrounding the maximum vector, direction and amplitude of the total vector and rectangular perimeter and area surrounding the total vector;
the characteristic parameters of the gravity center class comprise a positive field gravity center position, a positive field maximum position, a distance and a direction of a positive field gravity center position connecting line, a negative field gravity center position, a negative field maximum position, a distance and a direction of a negative field gravity center position connecting line and a distance and a direction of a positive and negative gravity center position connecting line.
Further, the feature subset includes:
in the divergence field corresponding to the T wave:
the ratio between the vector direction of the negative value field maximum value of the point T2 and the vector direction of the positive value field maximum value of the point T1, the ratio between the connecting line direction of the negative value field gravity center and the positive value field gravity center of the point T2 and the connecting line direction of the positive value field gravity center and the positive value field gravity center of the point T2, and the ratio between the area ratio between the positive value field and the negative value field of the point T3 and the area ratio between the positive value field and the negative value field of the point T wave top point;
the variation coefficient of the amplitude difference between the positive value field and the negative value field in the rotation field corresponding to the T wave;
the variance of the rectangular area surrounding the maximum gradient vector in the gradient field corresponding to the vertex to the end point of the T wave;
the m-th histogram of vector magnitude of the negative value field maximum point to the positive value field maximum in the two-dimensional isomagnetic map corresponding to the QRS wave 1 M-th of histogram of vector magnitude in which negative field maximum points to positive field maximum in two-dimensional isomagnetic map in which each bin value corresponds to a T wave 2 The ratio between the bin values, the ratio between the minimum value of the vector direction of the maximum value of the negative value field pointing to the maximum value of the positive value field in the two-dimensional isomagnetic diagram corresponding to the QRS wave and the minimum value of the vector direction of the maximum value of the negative value field pointing to the maximum value of the positive value field in the two-dimensional isomagnetic diagram corresponding to the T wave;
the ratio of the transformation index of the total gradient vector direction in the gradient field corresponding to the QRS wave to the transformation index of the total gradient vector direction in the gradient field corresponding to the T wave;
the mth of the histogram of the amplitude difference between the positive field and the negative field in the two-dimensional equal magnetic field corresponding to the ST-segment wave 3 M-th histogram of amplitude difference between positive field and negative field in two-dimensional equal magnetic field with bin value corresponding to T wave 4 The ratio between the individual bin values;
in the corresponding divergence field of the QRS wave:
a peak factor of the amplitude ratio of the Renyi entropy, positive field and negative field of the vector distance of the positive and negative gravity center connection line;
in the one-dimensional butterfly graph corresponding to the P wave and the T wave respectively:
nth (N) 1 Absolute mean of waveform of channel, nth 2 Absolute maximum of waveform of the channel;
variance of minimum value of positive value field in two-dimensional isomagnetic map corresponding to P wave starting point to vertex;
n in one-dimensional butterfly graph corresponding to QT interval 3 Mth of LBP binarized statistical histogram of channel 5 And a bin value.
A terminal comprising a processor and a memory, the memory being for storing a computer program, the processor being for executing the computer program stored by the memory to cause the terminal to execute the computer program on any one of the above mentioned storage media.
Compared with the prior art, the coronary artery blood flow abnormality detection system, the storage medium and the terminal have the following beneficial effects:
1. according to the invention, based on the SERF effect extremely weak magnetic detection technology, the initial data of the magnetocardiogram is obtained, the magnetocardiogram is subjected to wave band segmentation, one-dimensional butterfly images corresponding to P wave, QRS wave, ST wave and T wave bands are respectively obtained, the two-dimensional isomagnetogram and the current density image are analyzed, the rotation field, gradient field and divergence field of the current density image are further obtained, the distribution rule of characteristic parameters is discovered, more comprehensive characteristic information is provided, the characteristic parameters sensitive to coronary artery blood flow can be extracted from more angles, and the accuracy of the coronary artery blood flow detection result is further improved;
2. the invention can comprehensively identify the characteristic parameters related to coronary artery blood flow by constructing the gradient field, the rotation field and the divergence field of the current density map, and constructs the coronary artery blood flow abnormality judging and comparing module, the diagnosis accuracy and the sensitivity can reach more than 90 percent, and the invention has important clinical value for qualitatively and positionally identifying whether the coronary artery blood flow abnormality is abnormal or not.
Drawings
FIG. 1 is a schematic diagram of a coronary blood flow abnormality detection system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a coronary blood flow abnormality detection system according to another embodiment of the present invention;
FIG. 3 is a flow chart of an embodiment of a storage medium of the present invention;
FIG. 4 is a flow chart of another embodiment of a storage medium of the present invention;
fig. 5 is a schematic structural diagram of an embodiment of a terminal of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages to be solved more apparent, the following detailed description will be given with reference to the accompanying drawings and specific embodiments.
Term interpretation:
p wave: one component of the electrocardiogram waveform represents the depolarization process of the left and right atria;
QRS wave: one component of an electrocardiogram waveform represents the depolarization process of the left and right ventricles;
ST segment wave: one waveform end of the electrocardiogram waveform represents the repolarization platform period of the left ventricle and the right ventricle;
t wave band: one component of an electrocardiogram waveform represents the repolarization process of the left and right ventricles.
In one aspect, the present invention provides a coronary artery blood flow abnormality detection system, as shown in fig. 1-2, comprising:
the data acquisition module 100: the method comprises the steps of obtaining initial data of a magnetocardiogram, preprocessing the initial data of the magnetocardiogram, and obtaining one-dimensional butterfly images corresponding to P wave, QRS wave, ST wave and T wave bands;
in an embodiment of the present invention, the data acquisition module 100 may acquire the original data of the multi-channel magnetocardiogram through the SERF effect multi-channel optical pump magnetometer magnetocardiogram.
In an embodiment of the present invention, the preprocessing of the original magnetocardiogram data in the data acquisition module 100 mainly includes filtering and noise reduction, superposition averaging and band segmentation.
The filtering and noise reduction treatment is to filter the interference of the magnetocardiogram signals by using a high-pass filter, a low-pass filter and a power frequency wave trap to the original data of the magnetocardiogram, remove noise interference and ensure the accuracy of the position and degree of the subsequent detection of coronary blood flow abnormality. The superposition average processing is to perform R wave positioning on the filtered magnetocardiogram data, intercept the beat of the magnetocardiogram according to the obtained R wave position information, remove the bias between the beats of the magnetocardiogram and then perform superposition average; and then, carrying out band segmentation on the data after superposition and averaging, and obtaining one-dimensional butterfly patterns corresponding to the P wave, the QRS wave, the ST wave and the T wave bands.
In an embodiment of the present invention, the data acquisition module 100 may also process the original magnetocardiogram data by using filtering noise reduction, superposition average processing and band segmentation methods known in the prior art, which are not described herein.
Image construction module 200: the method comprises the steps of respectively acquiring a two-dimensional isomagnetic map, a current density map, a gradient field, a rotation field and a divergence field corresponding to a P wave, a QRS wave, an ST wave and a T wave band based on the acquired one-dimensional butterfly map and the positions of each magnetocardiogram channel;
in an embodiment of the present invention, the image construction module 200 specifically includes the following:
the first image construction module 201: the method comprises the steps of respectively drawing two-dimensional isomagnetic maps corresponding to P wave, QRS wave, ST wave and T wave bands based on the obtained one-dimensional butterfly maps and the positions of each magnetocardiogram, and respectively calculating current density maps corresponding to the P wave, the QRS wave, the ST wave and the T wave bands based on the two-dimensional isomagnetic maps;
in an embodiment of the present invention, the first image construction module 201 may calculate the current densities of all points on the two-dimensional isomagnetic map according to a current density calculation formula, so as to obtain a current density map corresponding to the P wave, QRS wave, ST wave and T wave bands, where (x, y) represents the coordinate value of a certain point above the chest on the two-dimensional isomagnetic map.
The second image construction module 202: the gradient value and the rotation value of the vector signal at each position in the current density diagram corresponding to the P wave, the QRS wave, the ST wave and the T wave band are respectively calculated according to the gradient formula and the rotation formula, and the gradient field and the rotation field corresponding to the P wave, the QRS wave, the ST wave and the T wave band are respectively drawn according to the gradient value and the rotation value;
in an embodiment of the present invention, the gradient field drawing method corresponding to each band of the second image construction module 202 is as follows: calculating a gradient value of the vector signal at each position in the current density map by using a gradient formula, wherein F represents a current vector at a point (x, y) in the current density map,F x (x,y)the representation function F is a partial derivative of x,F y (x,y)the representation function F is a partial derivative of y, a unit vector on the x-axis, and a unit vector on the y-axis. The partial derivatives in its definition are calculated using finite differences. For the internal data points in the current density plot, the partial derivatives are calculated using the center difference. For data points along the edges of the current density plot, the partial derivatives are calculated using single-sided (forward) differential; and finally, drawing gradient fields corresponding to the P wave, the QRS wave, the ST wave and the T wave band according to the gradient values calculated by each vector signal.
The corresponding rotation field drawing method of each wave band is as follows: the rotation value of the vector signal at each position in the current density map is calculated using the rotation formula curl (F) = dFy/dx-dFx/dy, where F represents the current vector at a point (x, y) in the current density map, dFy/dx represents the partial derivative of the y component in the current vector F with respect to the x-axis direction, and dFx/dy represents the partial derivative of the x component in the current vector F with respect to the y-axis direction. Calculating the partial derivatives in its definition using finite differences, calculating the partial derivatives using central differences for internal data points in the current density map, and calculating the partial derivatives using single-sided (forward) differences for data points along edges in the current density map; and finally, drawing the corresponding rotation fields of the P wave, the QRS wave, the ST wave and the T wave band according to the rotation value calculated by each vector signal position.
The third image construction module 203: the method is used for calculating and obtaining the divergence value of the vector signal of each position in the gradient fields corresponding to the P wave, the QRS wave, the ST wave and the T wave band according to the divergence formula, and drawing and obtaining the divergence fields corresponding to the P wave, the QRS wave, the ST wave and the T wave band according to the divergence value.
In an embodiment of the present invention, the method for drawing the divergence field corresponding to each band in the third image construction module 203 is as follows: the value of the divergence of the vector signal for each position in the gradient field is calculated using the divergence formula div (f) = dfx/dx+ dfy/dy, where f represents the gradient vector for a point (x, y) in the gradient field, dfx/dx represents the partial derivative of the x-axis vector of f with respect to x, and dfy/dy represents the partial derivative of the y-axis vector of f with respect to y. The partial derivatives in its definition are calculated using finite differences. For internal data points in the gradient field, the partial derivatives are calculated using the center difference. For data points along the edges in the gradient field, the partial derivatives are calculated using single-sided (forward) differencing; and finally, drawing the corresponding divergence fields of the P wave, the QRS wave, the ST wave and the T wave band according to the calculated divergence value of each position.
Feature extraction module 300: the method is used for extracting characteristic parameters related to coronary artery blood flow abnormality from a one-dimensional butterfly diagram, a two-dimensional isomagnetic diagram, a current density diagram, a gradient field, a rotation field and a divergence field corresponding to a P wave, a QRS wave, an ST wave and a T wave band respectively;
in an embodiment of the present invention, the extracted feature parameters include a basic class, an extremum class, a vector class, and a gravity center class, wherein:
the basic characteristic parameters reflect main change information in a two-dimensional isomagnetic map, a current density map, a gradient field, a curl field and a divergence field corresponding to each wave band, and specifically comprise an amplitude ratio and an amplitude difference of a field value range, a positive value field and a negative value field, and an area ratio and an area difference of the positive value field and the negative value field.
The extremum class characteristic parameters reflect extremum information in a two-dimensional isomagnetic map, a current density map, a gradient field, a rotation field and a divergence field corresponding to each wave band, and specifically comprise positions of a maximum value of a positive field and a maximum value, and positions of a maximum value of a negative field and a maximum value.
The vector characteristic parameters reflect vector related information in a two-dimensional isomagnetic map, a current density map, a gradient field, a curl field and a divergence field corresponding to each wave band, and concretely comprise vector directions and amplitudes of a positive value field maximum value and a negative value field maximum value, rectangular perimeter and area surrounded by vectors of the positive value field maximum value and the negative value field maximum value, amplitude, directions and positions of the maximum vector, rectangular perimeter and area surrounding the maximum vector, direction and amplitude of a total vector and rectangular perimeter and area surrounding the total vector.
The gravity center type characteristic parameters reflect gravity center related information in a two-dimensional isomagnetic map, a current density map, a gradient field, a curl field and a divergence field corresponding to each wave band, and concretely comprise a positive value field gravity center position, a distance and a direction of a connecting line of a positive value field maximum value position and the positive value field gravity center position, a negative value field gravity center position, a distance and a direction of a connecting line of a negative value field maximum value position and the negative value field gravity center position, and a distance and a direction of a connecting line of a positive gravity center position and a negative gravity center position.
Feature screening module 400: the method comprises the steps of preprocessing, evaluating and selecting the quantity of extracted characteristic parameters to obtain a characteristic subset;
in an embodiment of the present invention, the feature filtering module 400 first performs preprocessing on the extracted feature parameters, including missing value processing, outlier processing, data normalization, and the like. And secondly, evaluating characteristic parameters: the contribution of each feature parameter is evaluated according to the correlation of the feature parameter with the target, the correlation among the feature parameters, the importance of the feature parameters, and the like. Finally, selecting characteristic parameters: feature selection is performed according to the evaluation result, and generally includes three methods of filtering, wrapping and embedding.
The evaluation ranking is carried out according to the importance index of the characteristic parameters, and the parameters ranked in the first 1/10 of the total quantity of the characteristic parameters are generally selected as the characteristic subset used for modeling.
In an embodiment of the present invention, when the number of feature parameters is 150, parameters with importance level ranked 1/10 are selected as feature subsets for modeling after preprocessing and evaluation according to the 150 feature parameters, and the 15 feature subsets include:
in the corresponding divergence field of the T wave:
the ratio between the vector direction of the negative value field maximum value of the point T2 and the vector direction of the positive value field maximum value of the point T1, the ratio between the connecting line direction of the negative value field gravity center and the positive value field gravity center of the point T2 and the connecting line direction of the positive value field gravity center and the positive value field gravity center of the point T2, and the ratio between the area ratio between the positive value field and the negative value field of the point T3 and the area ratio between the positive value field and the negative value field of the point T wave top point;
the entire T wave Duan Junyun N is equally divided, and the T1 point and the T2 point represent the start point and the end point of a band of N equal division except the T wave start point and the T wave end point, respectively.
The variation coefficient of the amplitude difference between the positive value field and the negative value field in the rotation field corresponding to the T wave;
variance of cuboid area surrounding the maximum gradient vector in the gradient field corresponding to the vertex to the end point of the T wave;
the ratio between the 7 th bin value of the histogram of vector magnitude of the positive value field maximum value pointed to by the negative value field maximum value in the two-dimensional isomagnetic map corresponding to the QRS wave and the 7 th bin value of the histogram of vector magnitude of the positive value field maximum value pointed to by the negative value field maximum value in the two-dimensional isomagnetic map corresponding to the T wave;
the ratio between the minimum value of the vector direction of the maximum value of the negative value field pointing to the maximum value of the positive value field in the two-dimensional isomagnetic diagram corresponding to the QRS wave and the minimum value of the vector direction of the maximum value of the negative value field pointing to the maximum value of the positive value field in the two-dimensional isomagnetic diagram corresponding to the T wave;
the ratio of the transformation index of the total gradient vector direction in the gradient field corresponding to the QRS wave to the transformation index of the total gradient vector direction in the gradient field corresponding to the T wave;
the ratio between the 3 rd bin value of the histogram of the amplitude difference between the positive field and the negative field in the two-dimensional equal magnetic field corresponding to the ST wave and the 3 rd bin value of the histogram of the amplitude difference between the positive field and the negative field in the two-dimensional equal magnetic field corresponding to the T wave;
in the corresponding divergence field of the QRS wave:
a peak factor of the amplitude ratio of the Renyi entropy, positive field and negative field of the vector distance of the positive and negative gravity center connection line;
in the one-dimensional butterfly graph corresponding to the P wave and the T wave respectively:
absolute average value of waveform of 9 th channel, absolute maximum value of waveform of 7 th channel;
variance of minimum value of positive value field in two-dimensional isomagnetic map corresponding to P wave starting point to vertex;
the 5 th bin value of the LBP binarized statistical histogram of the 10 th channel in the one-dimensional butterfly map corresponding to the QT interval.
The judgment comparison module 500: judging whether coronary blood flow of the tested person is normal or not according to the feature subset;
in an embodiment of the present invention, the judging and comparing module 500 can judge whether the coronary artery blood flow of the tested person is normal by adopting a machine learning method, which is specifically as follows:
using patient data of known coronary blood flow and coronary blood flow anomalies as an original data set, training a machine learning model, such as a support vector machine (Support Vector Machine with a linear kernel) based on linear kernels, using the above-mentioned extracted 15 feature subsets, the model outputting labels of normal and abnormal classes, which can be represented by 0 for normal class, 1 for abnormal class, using evaluation indexes such as accuracy, precision, recall, AUC curve, etc., to evaluate classification model performance.
When the feature subset of the tested person inputs the trained machine learning model, the coronary artery blood flow is considered to be abnormal when the output label class is 1, and the coronary artery blood flow of the tested person is considered to be normal when the output label class is 0.
In another aspect, the present invention also provides a storage medium, as shown in fig. 3-4, storing a computer program, which when executed by a processor, implements a method for detecting coronary artery blood flow anomalies. The coronary artery blood flow abnormality detection method comprises the following steps:
step S1: acquiring initial data of a magnetocardiogram, preprocessing the initial data of the magnetocardiogram, and acquiring one-dimensional butterfly images corresponding to P wave, QRS wave, ST wave and T wave bands;
step S2: based on the obtained one-dimensional butterfly graph and the positions of each magnetocardiogram channel, respectively obtaining two-dimensional isomagnetograms, current density graphs, gradient fields, rotation fields and divergence fields corresponding to P wave, QRS wave, ST wave and T wave bands;
step S3: extracting characteristic parameters related to coronary artery blood flow abnormality from a one-dimensional butterfly diagram, a two-dimensional isomagnetic diagram, a current density diagram, a gradient field, a rotation field and a divergence field corresponding to a P wave, a QRS wave, an ST wave and a T wave band respectively;
step S4: preprocessing, evaluating and selecting the quantity of the extracted characteristic parameters to obtain a characteristic subset;
step S5: judging whether coronary blood flow of the tested person is normal or not according to the feature subset;
the characteristic parameters comprise basic classes, extreme value classes, vector classes and gravity center classes.
In an embodiment of the present invention, the step S2 preferably includes the following steps:
step S21: respectively drawing two-dimensional isomagnetic maps corresponding to the P wave, the QRS wave, the ST wave and the T wave band based on the obtained one-dimensional butterfly map and the positions of each magnetocardiogram channel; respectively calculating current density maps corresponding to P wave, QRS wave, ST wave and T wave bands based on the two-dimensional isomagnetic map;
step S22: respectively calculating a gradient value and a rotation value of a vector signal at each position in a current density diagram corresponding to the P wave, the QRS wave, the ST wave and the T wave band according to the gradient formula and the rotation formula, and respectively drawing a gradient field and a rotation field corresponding to the P wave, the QRS wave, the ST wave and the T wave band according to the gradient value and the rotation value;
step S23: calculating and obtaining the divergence value of the vector signal of each position in the gradient fields corresponding to the P wave, the QRS wave, the ST wave and the T wave band according to the divergence formula, and drawing and obtaining the divergence fields corresponding to the P wave, the QRS wave, the ST wave and the T wave band according to the divergence value.
Preferably, the storage medium may be a ROM, RAM, magnetic disk or optical disk, or other various media capable of storing program codes.
In addition, the present invention further provides a terminal, as shown in fig. 5, including a processor 600 and a memory 700, where the memory 600 is used to store a computer program, and the processor 700 is used to execute the computer program stored in the memory 600, so that the terminal performs any one of the coronary artery blood flow abnormality detection methods.
In summary, the invention obtains the magnetocardiogram original data based on SERF effect extremely weak magnetic detection technology, segments the magnetocardiogram original data to obtain one-dimensional butterfly images corresponding to P wave, QRS wave, ST segment and T wave bands respectively, analyzes two-dimensional isomagnetic images and current density images of the magnetocardiogram original data to obtain a rotation field, a gradient field and a divergence field of the current density images, discovers the distribution rule of characteristic parameters, provides more comprehensive characteristic information, can extract characteristic parameters sensitive to coronary artery blood flow from more angles, and further improves the accuracy of coronary artery blood flow detection results;
the invention can comprehensively identify the characteristic parameters related to coronary artery blood flow by constructing the gradient field, the rotation field and the divergence field of the current density map, and constructs the coronary artery blood flow abnormality judging and comparing module, the diagnosis accuracy and the sensitivity can reach more than 90 percent, and the invention has important clinical value for qualitatively and positionally identifying whether the coronary artery blood flow abnormality is abnormal or not.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the present invention.
Claims (5)
1. In a coronary artery blood flow abnormality detection system, comprising:
and a data acquisition module: the method comprises the steps of obtaining initial data of a magnetocardiogram, preprocessing the initial data of the magnetocardiogram, and obtaining one-dimensional butterfly images corresponding to P wave, QRS wave, ST wave and T wave bands;
and an image construction module: the method comprises the steps of respectively acquiring a two-dimensional isomagnetic map, a current density map, a gradient field, a rotation field and a divergence field corresponding to a P wave, a QRS wave, an ST wave and a T wave band based on the acquired one-dimensional butterfly map and the positions of each magnetocardiogram channel;
and the feature extraction module is used for: the method is used for extracting characteristic parameters related to coronary artery blood flow abnormality from a one-dimensional butterfly diagram, a two-dimensional isomagnetic diagram, a current density diagram, a gradient field, a rotation field and a divergence field corresponding to a P wave, a QRS wave, an ST wave and a T wave band respectively;
and a feature screening module: the method comprises the steps of preprocessing, evaluating and selecting the quantity of extracted characteristic parameters to obtain a characteristic subset;
and a judgment and comparison module: judging whether coronary blood flow of the tested person is normal or not according to the feature subset;
the characteristic parameters comprise basic classes, extreme value classes, vector classes and gravity center classes;
the image construction module includes:
a first image construction module: the method comprises the steps of respectively drawing two-dimensional isomagnetic graphs corresponding to P wave, QRS wave, ST wave and T wave bands based on the obtained one-dimensional butterfly graph and the positions of each magnetocardiogram, and respectively calculating current density graphs corresponding to the P wave, the QRS wave, the ST wave and the T wave bands based on the two-dimensional isomagnetic graphs;
and a second image construction module: the gradient value and the rotation value of the vector signal at each position in the current density diagram corresponding to the P wave, the QRS wave, the ST wave and the T wave band are respectively calculated according to the gradient formula and the rotation formula, and the gradient field and the rotation field corresponding to the P wave, the QRS wave, the ST wave and the T wave band are respectively drawn according to the gradient value and the rotation value;
and a third image construction module: the method comprises the steps of calculating and obtaining the divergence value of vector signals at each position in gradient fields corresponding to P wave, QRS wave, ST wave and T wave bands according to a divergence formula, and drawing and obtaining the divergence fields corresponding to the P wave, the QRS wave, the ST wave and the T wave bands according to the divergence value;
the characteristic parameters of the basic class comprise a field value range, an amplitude ratio sum difference of a positive field and a negative field, and an area ratio sum difference of the positive field and the negative field;
the characteristic parameters of the extremum class comprise the positions of the positive field maximum value and the positions of the negative field maximum value and the maximum value;
the characteristic parameters of the vector class comprise vector directions and amplitudes of the maximum value of the value field pointing to the maximum value of the positive value field, rectangular perimeter and area surrounded by vectors of the maximum value of the negative value field pointing to the maximum value of the positive value field, amplitude, direction and position of the maximum vector, rectangular perimeter and area surrounding the maximum vector, direction and amplitude of the total vector and rectangular perimeter and area surrounding the total vector;
the characteristic parameters of the gravity center class comprise a positive field gravity center position, a positive field maximum position, a distance and a direction of a positive field gravity center position connecting line, a negative field gravity center position, a negative field maximum position, a distance and a direction of a negative field gravity center position connecting line and a distance and a direction of a positive and negative gravity center position connecting line.
2. The coronary blood flow anomaly detection system of claim 1, wherein the feature subset comprises:
in the divergence field corresponding to the T wave:
the ratio between the vector direction of the negative value field maximum value of the point T2 and the vector direction of the positive value field maximum value of the point T1, the ratio between the connecting line direction of the negative value field gravity center and the positive value field gravity center of the point T2 and the connecting line direction of the positive value field gravity center and the positive value field gravity center of the point T2, and the ratio between the area ratio between the positive value field and the negative value field of the point T3 and the area ratio between the positive value field and the negative value field of the point T wave top point;
the variation coefficient of the amplitude difference between the positive value field and the negative value field in the rotation field corresponding to the T wave;
the variance of the rectangular area surrounding the maximum gradient vector in the gradient field corresponding to the vertex to the end point of the T wave;
the m-th histogram of vector magnitude of the negative value field maximum point to the positive value field maximum in the two-dimensional isomagnetic map corresponding to the QRS wave 1 M-th of histogram of vector magnitude in which negative field maximum points to positive field maximum in two-dimensional isomagnetic map in which each bin value corresponds to a T wave 2 The ratio between the bin values, the ratio between the minimum value of the vector direction of the maximum value of the negative value field pointing to the maximum value of the positive value field in the two-dimensional isomagnetic diagram corresponding to the QRS wave and the minimum value of the vector direction of the maximum value of the negative value field pointing to the maximum value of the positive value field in the two-dimensional isomagnetic diagram corresponding to the T wave;
the ratio of the transformation index of the total gradient vector direction in the gradient field corresponding to the QRS wave to the transformation index of the total gradient vector direction in the gradient field corresponding to the T wave;
the mth of the histogram of the amplitude difference between the positive field and the negative field in the two-dimensional equal magnetic field corresponding to the ST-segment wave 3 M-th histogram of amplitude difference between positive field and negative field in two-dimensional equal magnetic field with bin value corresponding to T wave 4 A bin value;
in the corresponding divergence field of the QRS wave:
a peak factor of the amplitude ratio of the Renyi entropy, positive field and negative field of the vector distance of the positive and negative gravity center connection line;
in the one-dimensional butterfly graph corresponding to the P wave and the T wave respectively:
nth (N) 1 Absolute mean of waveform of channel, nth 2 Absolute maximum of waveform of the channel;
variance of minimum value of positive value field in two-dimensional isomagnetic map corresponding to P wave starting point to vertex;
n in one-dimensional butterfly graph corresponding to QT interval 3 Mth of LBP binarized statistical histogram of channel 5 And a bin value.
3. A storage medium storing a computer program which, when executed by a processor, implements a coronary artery blood flow abnormality detection method;
the coronary artery blood flow abnormality detection method comprises the following steps:
acquiring initial data of a magnetocardiogram, preprocessing the initial data of the magnetocardiogram, and acquiring one-dimensional butterfly images corresponding to P wave, QRS wave, ST wave and T wave bands;
based on the obtained one-dimensional butterfly graph and the positions of each magnetocardiogram channel, respectively obtaining a two-dimensional isomagnetogram, a current density graph, a gradient field, a rotation field and a divergence field corresponding to a P wave, a QRS wave, a ST wave and a T wave band;
extracting characteristic parameters related to coronary artery blood flow from a one-dimensional butterfly diagram, a two-dimensional isomagnetic diagram, a current density diagram, a gradient field, a rotation field and a divergence field corresponding to a P wave, a QRS wave, a ST wave and a T wave band respectively;
preprocessing, evaluating and selecting the quantity of the extracted characteristic parameters to obtain a characteristic subset;
judging whether coronary blood flow is normal or not according to the feature subset;
wherein the characteristic parameters include: basic class, extremum class, vector class, gravity center class;
the steps of respectively acquiring a two-dimensional isomagnetic map, a current density map, a gradient field, a rotation field and a divergence field corresponding to a P wave, a QRS wave, an ST wave and a T wave band based on the acquired one-dimensional butterfly map and the positions of each magnetocardiogram channel comprise:
respectively drawing two-dimensional isomagnetic maps corresponding to P wave, QRS wave, ST wave and T wave bands based on the obtained one-dimensional butterfly map and the positions of each magnetocardiogram channel, and respectively calculating current density maps corresponding to the P wave, the QRS wave, the ST wave and the T wave bands based on the two-dimensional isomagnetic maps;
respectively calculating a gradient value and a rotation value of a vector signal at each position in a current density diagram corresponding to the P wave, the QRS wave, the ST wave and the T wave band according to the gradient formula and the rotation formula, and respectively drawing a gradient field and a rotation field corresponding to the P wave, the QRS wave, the ST wave and the T wave band according to the gradient value and the rotation value;
calculating and obtaining the divergence value of the vector signal of each position in the gradient fields corresponding to the P wave, the QRS wave, the ST wave and the T wave band according to the divergence formula, and drawing and obtaining the divergence fields corresponding to the P wave, the QRS wave, the ST wave and the T wave band according to the divergence value;
the characteristic parameters of the basic class comprise a field value range, an amplitude ratio and an amplitude difference of a positive field and a negative field, and an area ratio and an area difference of the positive field and the negative field;
the characteristic parameters of the extremum class comprise the positions of the positive field maximum value and the positions of the negative field maximum value and the maximum value;
the characteristic parameters of the vector class comprise vector directions and amplitudes of the maximum value of the value field pointing to the maximum value of the positive value field, rectangular perimeter and area surrounded by vectors of the maximum value of the negative value field pointing to the maximum value of the positive value field, amplitude, direction and position of the maximum vector, rectangular perimeter and area surrounding the maximum vector, direction and amplitude of the total vector and rectangular perimeter and area surrounding the total vector;
the characteristic parameters of the gravity center class comprise a positive field gravity center position, a positive field maximum position, a distance and a direction of a positive field gravity center position connecting line, a negative field gravity center position, a negative field maximum position, a distance and a direction of a negative field gravity center position connecting line and a distance and a direction of a positive and negative gravity center position connecting line.
4. A storage medium according to claim 3, wherein the feature subset comprises:
in the divergence field corresponding to the T wave:
the ratio between the vector direction of the negative value field maximum value of the point T2 and the vector direction of the positive value field maximum value of the point T1, the ratio between the connecting line direction of the negative value field gravity center and the positive value field gravity center of the point T2 and the connecting line direction of the positive value field gravity center and the positive value field gravity center of the point T2, and the ratio between the area ratio between the positive value field and the negative value field of the point T3 and the area ratio between the positive value field and the negative value field of the point T wave top point;
the variation coefficient of the amplitude difference between the positive value field and the negative value field in the rotation field corresponding to the T wave;
the variance of the rectangular area surrounding the maximum gradient vector in the gradient field corresponding to the vertex to the end point of the T wave;
the m-th histogram of vector magnitude of the negative value field maximum point to the positive value field maximum in the two-dimensional isomagnetic map corresponding to the QRS wave 1 M-th of histogram of vector magnitude in which negative field maximum points to positive field maximum in two-dimensional isomagnetic map in which each bin value corresponds to a T wave 2 The ratio between the bin values, the ratio between the minimum value of the vector direction of the maximum value of the negative value field pointing to the maximum value of the positive value field in the two-dimensional isomagnetic diagram corresponding to the QRS wave and the minimum value of the vector direction of the maximum value of the negative value field pointing to the maximum value of the positive value field in the two-dimensional isomagnetic diagram corresponding to the T wave;
the ratio of the transformation index of the total gradient vector direction in the gradient field corresponding to the QRS wave to the transformation index of the total gradient vector direction in the gradient field corresponding to the T wave;
the mth of the histogram of the amplitude difference between the positive field and the negative field in the two-dimensional equal magnetic field corresponding to the ST-segment wave 3 M-th histogram of amplitude difference between positive field and negative field in two-dimensional equal magnetic field with bin value corresponding to T wave 4 The ratio between the individual bin values;
in the corresponding divergence field of the QRS wave:
a peak factor of the amplitude ratio of the Renyi entropy, positive field and negative field of the vector distance of the positive and negative gravity center connection line;
in the one-dimensional butterfly graph corresponding to the P wave and the T wave respectively:
nth (N) 1 Absolute mean of waveform of channel, nth 2 Absolute maximum of waveform of the channel;
variance of minimum value of positive value field in two-dimensional isomagnetic map corresponding to P wave starting point to vertex;
n in one-dimensional butterfly graph corresponding to QT interval 3 Mth of LBP binarized statistical histogram of channel 5 And a bin value.
5. A terminal comprising a processor and a memory, the memory for storing a computer program, the processor for executing the computer program stored in the memory to cause the terminal to execute the computer program on the storage medium of any one of claims 3-4.
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| 冠状动脉临界病变的评价及治疗;申倩南;刘俊;;心血管病学进展(第02期);全文 * |
| 冠状动脉粥样硬化性心脏病患者特征性的心磁图表现;权薇薇;陆国平;李英梅;沈越;袁;戚文航;;诊断学理论与实践(第06期);全文 * |
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