CN108577832B - Computer readable storage medium for atrial fibrillation signal identification - Google Patents
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Abstract
The invention discloses an atrial fibrillation distinguishing method, which comprises the following steps: positioning waveform characteristic points of original electrocardiogram data, providing R wave positions and form classification of QRS complex waves, and judging RR interval information of continuous 50 or more supraventricular beats; generating three kinds of characteristic data of aggregative property measurement, diagonal region dominance measurement and non-diagonal region linear correlation measurement from the aspect of geometric distribution characteristics of a Lorentz scatter diagram; and (4) synthesizing the characteristic data, and giving out the judgment whether atrial fibrillation occurs or not according to a threshold value method. The atrial fibrillation recognition method based on the geometric distribution characteristics of the Lorentz scatter diagram of the ventricular RR interphase can effectively assist doctors in distinguishing.
Description
Technical Field
The invention relates to a computer readable storage medium for atrial fibrillation signal identification, in particular to an automatic identification method for identifying whether atrial fibrillation occurs in electrocardiogram data or not, which belongs to the technical field of data processing.
Background
Because rapid atrial fibrillation can induce ventricular velocity and ventricular fibrillation to cause hemodynamic disturbance, chronic atrial fibrillation can cause tachycardia to cause cardiac organic matter, serious harmful consequences such as thrombus and the like are caused along with complications, and a noninvasive and simple judgment method for the atrial fibrillation is needed. From the generated physiological mechanism, the atrial fibrillation electrocardiogram signal is a changeable arrhythmia signal which is formed by one or more excitation waves along changeable conduction paths to cause continuous multiple waves. Clinical electrocardiogram performance is divided into two aspects: each lead P wave disappears and is replaced by f waves with inconsistent shapes and amplitudes and irregular intervals; the QRS amplitude varies greatly but the morphology is roughly the same, and the RR intervals are unequal. The judgment of P-wave disappearance and irregular f-wave appearance requires a large amount of complicated calculation on electrocardiographic data and is easily interfered by noise. In view of the application limitation, the current atrial fibrillation detection technology usually uses only the irregularity of RR intervals to judge atrial fibrillation.
The existing atrial fibrillation detection method based on the RR interval has higher discrimination on sinus rhythm, but due to the difference of different individuals and various diseases, the RR interval is possibly irregular in different degrees, so that the atrial fibrillation is poorer in discrimination in heart rate abnormality data. The existing method measures the irregularity by adopting statistical distribution and entropy information, and long-range data is usually needed for calculating the characteristic theory, which is not favorable for the real-time diagnosis requirement.
Disclosure of Invention
The invention aims to solve the technical problem of providing a computer-readable storage medium for atrial fibrillation signal identification, wherein an atrial fibrillation signal identification algorithm based on supraventricular RR intervals is stored on the computer-readable storage medium, and irregularity measurement of a Lorentz scatter diagram of short-range supraventricular RR intervals on geometric distribution is utilized to judge whether more than 50 supraventricular RR intervals are atrial fibrillation signals or not, so that a doctor can be effectively assisted in judging.
The invention adopts the following technical scheme for solving the technical problems:
the invention provides a computer-readable storage medium for atrial fibrillation signal recognition, having a computer program stored thereon, which when executed by a processor implements the steps of:
step 1, positioning waveform characteristic points according to original multi-lead electrocardiogram data, giving out morphological classification of R wave positions and QRS complex waves, judging ventricular beats and supraventricular beats, and generating R wave position information data with ventricular beats and supraventricular beat marks;
step 2, generating an supraventricular RR interval for R wave position information data mainly based on supraventricular beats;
step 3, performing geometric distribution characteristic measurement on the Lorentz scatter diagram of the supraventricular RR interval to generate three kinds of characteristic data: the method comprises the following steps of (1) measuring an aggregation metric index, a diagonal region dominance metric index and a non-diagonal region linear correlation metric index;
and 4, integrating the aggregative measurement index of the supraventricular RR interval, the dominant measurement index of the diagonal region and the linear correlation measurement index of the non-diagonal region, and identifying whether the atrial fibrillation signal is present according to a threshold method.
As a further technical scheme of the invention, in the step 1, before waveform characteristic point positioning is carried out, the original multi-lead electrocardiogram data is firstly filtered to remove power frequency noise, high frequency noise and baseline drift interference.
As a further technical solution of the present invention, the step 2 specifically comprises:
2.1, calculating the proportion of the supraventricular beats according to the R wave position information data for marking the ventricular beats and the supraventricular beats;
2.2, when the supraventricular beat ratio is greater than k%, executing step 2.3; otherwise, the ventricular beat is frequent and can not be distinguished;
2.3, counting RR interval data meeting the condition that two adjacent heartbeats are supraventricular beats to obtain an RR interval sequence (RR)1,…,RRN+1) (ii) a When the number N of the supraventricular RR intervals is more than or equal to 50, obtaining a point set { P on the Lorentz scatter diagram1,…,PNElse, otherwise, it cannot be distinguished, wherein, PiHas the coordinates of (RR)i,RRi+1)。
As a further technical solution of the present invention, in the step 3:
aggregative metric
Wherein NumofPoint (P)iΔ R) is the number of points contained in the neighborhood of the ith point at a given radius Δ R;
the dominance measurement index NumofDiag of the diagonal area is the ratio of points contained in the main diagonal area to the total number of points of the point set;
the linear correlation metric index LinearNoDiag of the off-diagonal region is the correlation coefficient of the off-diagonal region point.
As a further technical solution of the present invention, step 4 specifically is:
for data to be judged, firstly, judging whether the aggregative measurement index Aggregation _ max is larger than a threshold Th _ AM, if so, judging whether the data is an atrial fibrillation signal, otherwise, further judging whether the diagonal region dominance measurement index NumofDiag is larger than a threshold Th _ D, if so, judging whether the data is the atrial fibrillation signal, otherwise, further judging whether the non-diagonal region linear correlation index LinearNoDiag is larger than a threshold Th _ L, if so, judging the data is not the atrial fibrillation signal, otherwise, judging the data is the atrial fibrillation signal.
As a further technical scheme of the invention, the main diagonal area is an area with a diagonal distance smaller than delta R.
As a further technical scheme of the invention, the method for calculating the linear correlation metric index LinearNoDiag of the non-diagonal region comprises the following steps:
(1) respectively taking out the upper point and the lower point outside the diagonal region to obtain a point set { Up1,…,UpsAnd { Low }1,…,LowrS and r are respectively the number of points above and below the outside of the diagonal area;
(2) separately compute the set of points { Up1,…,UpsAnd { Low }1,…,LowrCorrelation coefficient ρ ofUp、ρLow，
Wherein, cov (X)Up,YUp)、cov(XLow,YLow) Are respectively { Up1,…,UpsAnd { Low }1,…,LowrCovariance of coordinates of midpoints, DXUp、DYUpAre respectively { Up1,…,UpsThe variance of the abscissa and ordinate of the midpoint, DXLow、DYLowAre respectively { Low1,…,LowrThe variance of the abscissa and the ordinate of the midpoint;
(3) non-diagonal region linear correlation metric index LinearNoDiag ═ max (ρ)Up,ρLow)。
Compared with the prior art, the invention adopting the technical scheme has the following technical effects: the technical key point of the invention is to refine the measurement of irregularity of the supraventricular RR interval from the aspect of the geometric distribution characteristic of a Lorentz scatter diagram; and (3) giving the standard of atrial fibrillation signal identification by integrating the aggregative measurement index of the supraventricular RR interval, the dominant measurement index of the diagonal region and the linear correlation measurement index of the non-diagonal region. The invention can assist the doctor in distinguishing.
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FIG. 1 is a schematic diagram of the present invention;
fig. 2 is a detailed flow chart of the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings:
as shown in fig. 1, the present invention provides a method for judging atrial fibrillation, which comprises the following steps:
step 1, positioning waveform characteristic points according to original multi-lead electrocardiogram data, giving out morphological classification of R wave positions and QRS complex waves, judging ventricular beats and supraventricular beats, and generating R wave position information data with ventricular beats and supraventricular beat marks;
step 2, generating an supraventricular RR interval for R wave position information data mainly based on supraventricular beats;
step 3, performing geometric distribution characteristic measurement on the Lorentz scatter diagram of the supraventricular RR interval to generate three kinds of characteristic data: the method comprises the following steps of (1) measuring an aggregation metric index, a diagonal region dominance metric index and a non-diagonal region linear correlation metric index;
and 4, integrating the aggregative measurement index of the supraventricular RR interval, the dominant measurement index of the diagonal region and the linear correlation measurement index of the non-diagonal region, and identifying whether the atrial fibrillation signal is present according to a threshold method.
As shown in fig. 2, the detailed steps of the present invention are:
step 1, R wave localization and form classification of QRS complex
After the original multi-lead electrocardiogram data is filtered to remove power frequency noise, high frequency noise and baseline drift interference, waveform characteristic points are positioned, the R wave position and the form classification of QRS complex waves are given, and ventricular pulsation and supraventricular pulsation are judged.
Step 2, generating supraventricular RR intervals (RR) from data mainly based on supraventricular beats1,…,RRN+1)
For R-wave information data with ventricular beat and supraventricular beat markers, the percentage of supraventricular beats is first calculated. When the ratio of the supraventricular beats is more than or equal to k%, the RR that two adjacent heartbeats are the supraventricular beats is countedInterval data to obtain RR interval sequence (RR)1,…,RRN+1) (ii) a When the number N of the supraventricular RR intervals is more than or equal to 50, obtaining a point set { P on the Lorentz scatter diagram1,…,PNIn which P isiHas the coordinates of (RR)i,RRi+1). When the supraventricular beat ratio is less than or equal to k%, indicating that the ventricular beats frequently; when the number of supraventricular RR intervals is less than 50, the information is too little to be judged.
And 3, generating three kinds of characteristic data for the supraventricular RR interval information from the aspect of geometric distribution characteristics of a Lorentz scatter diagram: aggregation metric, diagonal region dominance metric, off-diagonal region linear correlation metric.
In order to correct the distance under the condition of heart rate overspeed and heart rate bradycardia, the weighted distance between two points is adopted in the calculation:
the weight is:
wherein, Pi、PjI and j points on the Lorentz scattergram.
1) Aggregative metric
Aggregation _ max is the ratio of the maximum number of points in the set of points that contain a point within the neighborhood of a given radius Δ R to the total number of points in the set of points:
wherein NumofPoint (P)iΔ R) is the number of points included in the neighborhood of the given radius Δ R for the ith point.
2) The diagonal region dominance measure (NumofDiag) is the ratio of the number of points contained within the main diagonal region to the total number of points in the set of points. Where the main diagonal region is a region having a diagonal distance less than Δ R, again using weighted distances.
3) Obtaining linear correlation of off-diagonal regions by calculating correlation coefficients of off-diagonal region points (LinearNoDiag)
(1) Respectively taking out the upper point and the lower point outside the diagonal region to obtain a point set { Up1,…,UpsAnd { Low }1,…,LowrS and r are respectively the number of points above and below the outside of the diagonal area;
(2) separately compute the set of points { Up1,…,UpsAnd { Low }1,…,LowrCorrelation coefficient ρ ofUp、ρLow，
Wherein, cov (X)Up,YUp)、cov(XLow,YLow) Are respectively { Up1,…,UpsAnd { Low }1,…,LowrCovariance of coordinates of midpoints, DXUp、DYUpAre respectively { Up1,…,UpsThe variance of the abscissa and ordinate of the midpoint, DXLow、DYLowAre respectively { Low1,…,LowrThe variance of the abscissa and the ordinate of the midpoint;
(3) non-diagonal region linear correlation metric index LinearNoDiag ═ max (ρ)Up,ρLow)。
Step 4, judging the threshold value of atrial fibrillation signals
And (4) integrating the aggregative measurement index of the supraventricular RR interval, the dominant measurement index of the diagonal region and the linear correlation measurement index of the non-diagonal region, and identifying whether the signal is an atrial fibrillation signal according to a threshold value method. For data to be judged, firstly, judging whether the Aggregation metric index Aggregation _ max is larger than a threshold Th _ AM, if so, judging whether the data is an atrial fibrillation signal, otherwise, further judging whether the diagonal region dominance metric index NumofDiag is larger than a threshold Th _ D, if so, judging whether the data is the atrial fibrillation signal, otherwise, further judging whether the non-diagonal region linear correlation index LinearNoDiag is larger than a threshold Th _ L, if so, judging that the data is the atrial fibrillation signal, otherwise, considering that the geometric distribution characteristics are irregular, and if so, judging that the data is the atrial fibrillation signal. In this embodiment, Th _ AM is 10%, Th _ D is 60%, and Th _ L is 0.8.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can understand that the modifications or substitutions within the technical scope of the present invention are included in the scope of the present invention, and therefore, the scope of the present invention should be subject to the protection scope of the claims.
Claims (4)
1. A computer-readable storage medium for atrial fibrillation signal recognition, having a computer program stored thereon, wherein the computer program, when executed by a processor, performs the steps of:
step 1, positioning waveform characteristic points according to original multi-lead electrocardiogram data, giving out morphological classification of R wave positions and QRS complex waves, judging ventricular beats and supraventricular beats, and generating R wave position information data with ventricular beats and supraventricular beat marks;
step 2, generating an supraventricular RR interval for R wave position information data mainly based on supraventricular beats; the method specifically comprises the following steps:
2.1, calculating the proportion of the supraventricular beats according to the R wave position information data for marking the ventricular beats and the supraventricular beats;
2.2, when the supraventricular beat ratio is greater than k%, executing step 2.3; otherwise, the ventricular beat is frequent and can not be distinguished;
2.3, counting RR interval data meeting the condition that two adjacent heartbeats are supraventricular beats to obtain an RR interval sequence (RR)1,…,RRN+1) (ii) a When the number N of the supraventricular RR intervals is more than or equal to 50, obtaining a point set { P on the Lorentz scatter diagram1,…,PNElse, otherwise, it cannot be distinguished, wherein, PiHas the coordinates of (RR)i,RRi+1)；
Step 3, performing geometric distribution characteristic measurement on the Lorentz scatter diagram of the supraventricular RR interval to generate three kinds of characteristic data: the method comprises the following steps of (1) measuring an aggregation metric index, a diagonal region dominance metric index and a non-diagonal region linear correlation metric index; wherein:
aggregative metric
Wherein NumofPoint (P)iΔ R) is the number of points contained in the neighborhood of the ith point at a given radius Δ R;
the dominance measurement index NumofDiag of the diagonal area is the ratio of points contained in the main diagonal area to the total number of points of the point set;
the linear correlation measurement index LinearNoDiag of the off-diagonal region is the correlation coefficient of the off-diagonal region point;
step 4, integrating the aggregative measurement index of the supraventricular RR interval, the dominant measurement index of the diagonal region and the linear correlation measurement index of the non-diagonal region, and identifying whether the atrial fibrillation signal is detected according to a threshold method, wherein the method specifically comprises the following steps:
for data to be judged, firstly, judging whether the aggregative measurement index Aggregation _ max is larger than a threshold Th _ AM, if so, judging whether the data is an atrial fibrillation signal, otherwise, further judging whether the diagonal region dominance measurement index NumofDiag is larger than a threshold Th _ D, if so, judging whether the data is the atrial fibrillation signal, otherwise, further judging whether the non-diagonal region linear correlation index LinearNoDiag is larger than a threshold Th _ L, if so, judging the data is not the atrial fibrillation signal, otherwise, judging the data is the atrial fibrillation signal.
2. The computer-readable storage medium of claim 1, wherein in step 1, the original multi-lead electrocardiograph data is filtered to remove power frequency noise, high frequency noise and baseline wander interference before the waveform feature points are located.
3. The computer-readable storage medium of claim 1, wherein the main diagonal region is a region having a diagonal distance less than Δ R.
4. The computer-readable storage medium for atrial fibrillation signal identification according to claim 1, wherein the off-diagonal region linear correlation metric LinearNoDiag is calculated by:
(1) respectively taking out the upper point and the lower point outside the diagonal region to obtain a point set { Up1,…,UpsAnd { Low }1,…,LowrS and r are respectively the number of points above and below the outside of the diagonal area;
(2) separately compute the set of points { Up1,…,UpsAnd { Low }1,…,LowrCorrelation coefficient ρ ofUp、ρLow，
Wherein, cov (X)Up,YUp)、cov(XLow,YLow) Are respectively { Up1,…,UpsAnd { Low }1,…,LowrCovariance of coordinates of midpoints, DXUp、DYUpAre respectively { Up1,…,UpsThe variance of the abscissa and ordinate of the midpoint, DXLow、DYLowAre respectively { Low1,…,LowrThe variance of the abscissa and the ordinate of the midpoint;
(3) non-diagonal region linear correlation metric index LinearNoDiag ═ max (ρ)Up,ρLow)。
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