CN111493858B - Single-guide-joint specific main wave identification and positioning method based on cluster analysis - Google Patents
Single-guide-joint specific main wave identification and positioning method based on cluster analysis Download PDFInfo
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Abstract
A single-guide-specific main wave identification and positioning method based on cluster analysis comprises the following steps: a) Pre-treating; b) Based on the selected characteristics, performing adaptive clustering analysis on the main wave; c) And comprehensively analyzing to determine the type and specific position of the specific main wave. The invention determines the type and the position of the single-guide specific main wave by using a multi-stage and multi-level clustering analysis tool, thereby providing reference for further diagnosis of doctors. The method is an unsupervised learning method, does not need label data, can identify and position specific waveforms of individual electrocardiograms, and is easy to understand, easy to realize and high in precision.
Description
Technical Field
The invention provides a cluster analysis-based single-lead specific main wave identification and positioning method, and relates to the field of electrocardiogram intelligent diagnosis.
Background
Electrocardiographic examination is a common item of physical examination, and if a patient is suspected to have arrhythmia symptoms, the patient usually goes to a hospital to make an electrocardiogram, but the problem is difficult to find in the electrocardiogram of minutes or tens of minutes in the hospital, and at this time, a doctor may wear a wearable electrocardiogram detector for two weeks or more, which may generate an electrocardiogram of hundreds of hours, and the doctor takes a second and a second for examination, which is very time-consuming.
Currently, researchers have been working on intelligent diagnosis of cardiac arrhythmias. For example, the arrhythmia is diagnosed by early machine learning methods such as a support vector machine and a KNN; with the recent rise of deep learning, researchers have proposed a new method for diagnosing arrhythmia using deep learning: for example, the Wu Endar belt Tostanford machine learning group abroad proposes to use CNN to detect the arrhythmia of cardiologist level, and the national Gao-rock and the like use CNN to research the intelligent diagnosis method of multi-lead arrhythmia. We have also studied the intelligent diagnosis of multi-lead arrhythmia in conjunction with two-dimensional CNN or LSTM and applied for related invention patents. However, these methods are supervised learning methods, and particularly for intelligent diagnosis of arrhythmia, there are inevitable limitations: firstly, a large amount of labeled training data for a certain arrhythmia type is needed; secondly, arrhythmia between individuals often has respective inherent characteristics, and a large number of models trained by the individuals are used for diagnosing a certain individual, so that the adaptability is questionable.
Therefore, based on the latest research result, a single-lead specific dominant wave identification and positioning method based on cluster analysis is provided, and the type and the position of the lead specific dominant wave are determined by applying a cluster analysis tool in multiple stages and multiple levels, so that reference is provided for further diagnosis of doctors. The method is an unsupervised learning method, does not need label data, and can identify and position individual electrocardiograms, thereby better solving two problems of the conventional supervised learning method, being easy to understand and realize and having higher precision.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and invents a single-guide specific main wave identification and positioning method based on cluster analysis.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a single-lead specific main wave identification and positioning method based on cluster analysis comprises the following steps: a) Pre-treating; b) Based on the selected characteristics, performing adaptive clustering analysis on the main wave; c) And comprehensively analyzing to determine the type and specific position of the specific main wave. The pretreatment method of a) comprises the following steps:
step 1: obtaining the approximate position of each main wave on the single-lead data based on a conventional analysis method; the analysis method is a P-T calculation method or a QRS wave starting point and end point positioning method based on regularized least square regression learning;
and 2, step: removing baseline drift and high-frequency error of original data by using MODWT; the MODWT is a highly redundant non-orthogonal transform, the sample capacity can be any value, the displacement invariance is realized, sym4 wavelets are selected through comprehensive comparison analysis, the layer 6 is analyzed, d2, d3, d4, d5 and d6 are selected as reconstruction signals, and therefore baseline drift and high-frequency errors are removed;
and step 3: calibrating, namely determining the specific position and time limit of the existing main wave, and acquiring a candidate main wave position which is possibly missed, wherein the calibrating and determining the specific position and time limit of the existing main wave are divided into two steps:
(1) For all initial dominant wave positions: and replacing the original initial main wave position by an absolute maximum value point in a small interval near a certain initial main wave position, and obtaining the main wave main body direction according to all main wave directions. For individual main wave directions inconsistent with the main direction, the original direction is changed or kept according to the amplitude sequencing of the main wave directions in the direction and the amplitude condition of the main wave in the opposite direction, so that the specific position of the main wave is determined, and each main wave time limit is determined based on the slope change condition and the QRS normal time limit range;
(2) Outside the current main wave position refractory period, finding out absolute value maximum value points in a certain interval near each point as candidate missing main wave positions, and providing preparation for targeted supplement according to a clustering analysis result in a later period;
preferably, the method comprises the following steps: the b) based on the selected characteristics, the method for carrying out the self-adaptive cluster analysis on the main wave comprises the following steps:
step 1: based on the main wave morphological characteristics, the main wave is subjected to self-adaptive cluster analysis, possible missing candidate main waves are supplemented, and possible specific main wave positions are found out:
(1) Obtaining waveform data in a certain range at two ends of each main wave point, subtracting the corresponding amplitude of the main wave point, and then taking an absolute value to obtain waveform data to be gathered;
(2) Adopting a self-adaptive clustering method for waveform data to be clustered to obtain the classification number corresponding to the highest average evaluation index, and carrying out clustering analysis on the classification number to obtain the category evaluation index corresponding to each classification;
(3) Determining whether to enter a shape clustering module according to the overall evaluation index, each classification evaluation index and each classification average time limit: when the average time limit is larger, the evaluation index is required to be larger; on the contrary, when the average time limit is smaller, the evaluation index requirement can be properly lowered, and the specific parameters are determined according to the actual analysis data;
(4) When the waveform can be divided, the missing main wave position of the supplement candidate is considered, firstly, the centers of various types and the minimum and maximum distances between each point in the types and the center are calculated; secondly, calculating the distance between each candidate main wave position and the center of each class, taking the class with the minimum distance as a candidate class, and inspecting whether the distance is within the maximum distance range in the class, wherein the distance is slightly larger but has small difference or the distance from one class is far smaller than the distance from the adjacent class, so that the candidate main wave position is classified into the class; finally, recording the remaining candidate main wave positions;
(5) Classifying, namely determining two types of specific main wave types, wherein the number of the types is two, the average time limit corresponding to a certain type is the largest, and the given requirements on the main wave time limit of wide deformity are met, and then taking the type as the specific main wave type; if the number of categories is more than two, sorting the average time limit of each category from large to small, and taking all categories of which the average time limit meets the requirement as specific main waves;
(6) Individual calibrations are performed for the specific dominant wave in the forward direction. Performing cluster analysis on the forward main wave to determine whether the forward main wave can be divided into two parts; if the main wave has the forward specific main wave, determining whether to remove the main wave according to whether the forward main wave can be divided into two parts and the time limit of the individual main wave, and if the main wave does not have the forward specific main wave, determining whether to supplement the individual main wave according to whether the forward main wave can be divided into two parts and the time limit of the individual main wave;
(7) The waveform is not separable, the special condition that the negative main wave exists is considered, and the negative main wave meets the time limit requirement, and the main wave is determined as the specific main wave;
(8) If the specific main wave does not meet the requirement, the specific main wave based on morphological clustering analysis is empty;
step 2: based on the main wave interval characteristics, carrying out self-adaptive cluster analysis on the main wave interval, supplementing possible missing candidate main waves and finding out possible specific main wave positions:
(1) Acquiring first-order differential data of a main wave position, namely interval data;
(2) Judging whether interval data are uniform or not according to interval range, if so, the interval is not separable, and if not, the following steps are continued;
(3) Obtaining the classification number corresponding to the highest average evaluation index by adopting a self-adaptive clustering method, and carrying out clustering analysis according to the classification number to obtain the evaluation index corresponding to each classification;
(4) If the average evaluation index is greater than a given value or the evaluation index in a certain category is greater than a given value, the interval can be divided;
(5) When the interval is available, the missing main wave position of the supplement candidate is considered, firstly, the centers of various classes and the minimum and maximum distances between each point in the classes and the center are calculated; secondly, calculating the distance between the front interval and the rear interval of each candidate main wave and the center of each class, taking the class with the minimum distance as a candidate class, and inspecting whether the distance is within the range of the maximum distance in the class, wherein the distance is slightly larger but has small difference or the distance from one class is far smaller than the distance from the adjacent class, and classifying the front interval and the rear interval of the position of the candidate main wave into the class; finally, recording the remaining candidate main wave positions;
(6) Classifying, and determining the main wave class of the specific interval, taking the rapid premature beat as an example: the number of categories is two, the interval corresponding to a certain category is the minimum and meets the given interval requirement, the category is taken as a specific interval category, and the interval positions plus 1 are taken as the corresponding specific main wave positions; if the number of categories is more than two, sorting the average intervals from small to large, taking the interval positions corresponding to all categories with the average interval meeting the requirements plus 1 as the specific main wave position, and the escape and conduction block types are just opposite;
(7) If the evaluation index is smaller than the given value, the specific main wave based on interval clustering analysis is empty;
preferably, the method comprises the following steps: the c) comprehensive analysis and the method for determining the specific main wave type and the specific position comprise the following steps: determining the type and position of the specific main wave based on the main wave morphology clustering analysis result and the main wave interval clustering result;
the result is as follows: when the morphological clustering analysis result and the interval clustering analysis result do not exist, the specific main wave is an empty set;
and a second result: when the morphological clustering analysis result and the interval clustering analysis result have one time, taking the corresponding specific main wave type and specific position;
and a third result: and when both the morphological clustering analysis result and the interphase clustering analysis result exist, taking the intersection of the morphological clustering analysis result and the interphase clustering analysis result as a final result.
Drawings
FIG. 1 is a flow chart of the implementation steps of a single-guide specific dominant wave identification and positioning method based on cluster analysis according to the present invention;
FIG. 2 is a diagram of an example of the specific dominant wave positions identified by the electrocardiogram of "occasional ventricular premature beats";
FIG. 3 is an example of the specific dominant wave positions identified by the electrocardiogram of "frequent ventricular premature beats";
FIG. 4 is a diagram showing an example of the specific dominant wave position identified by the electrocardiogram of "sporadic atrial premature beat";
FIG. 5 is a diagram of an example of the specific dominant wave position identified by the electrocardiogram of "frequent atrial premature beats".
Detailed Description
The technical scheme of the present invention will be further described in detail with reference to the accompanying drawings, and fig. 1 shows a method for identifying and positioning a single-waveguide specific main wave based on cluster analysis, which includes the following steps:
a) Pretreatment:
step 1: obtaining the approximate position of each main wave on the single-lead data based on a conventional analysis method;
currently, there are very many methods for positioning the main wave of an electrocardiogram, such as the classical P-T algorithm or the QRS wave starting point and ending point positioning method based on regularized least square regression learning (patent number: 201610369281.7) self-developed by the present company
And 2, step: removing baseline drift and high-frequency error of original data by using MODWT;
MODWT is a highly redundant non-orthogonal transform, can have arbitrary sample volumes, has displacement invariance, and is well suited for processing electrocardiographic data. In actual use, by comprehensive comparison and analysis, the sym4 wavelet is selected, the layer 6 is analyzed, and d2, d3, d4, d5 and d6 are selected as reconstruction signals, so that baseline drift and high-frequency errors are removed;
and step 3: calibrating, namely determining the specific position and time limit of the existing main wave and obtaining the candidate main wave position which is possibly omitted;
because of the inherent defects of single lead coupling, mutual calibration between leads cannot be performed like multi-lead coupling, and the primary wave initially positioned may have the phenomena of direction reversal (for example, the primary wave should take an R wave to become an S wave), omission and the like, which all affect the later-stage clustering analysis result. For this purpose, it is carried out in two steps:
(1) For all initial dominant wave positions: replacing an original initial main wave position with an absolute value maximum point in a small interval near a certain initial main wave position, obtaining main wave main body directions according to all main wave directions, changing or keeping the original direction of individual main wave directions inconsistent with the main body directions according to amplitude sequencing of the main wave directions and main wave amplitude conditions in the opposite direction, determining the specific position of the main wave, and determining each main wave time limit (note: distance between two peak valley points) based on the slope change condition and QRS normal time limit range;
(2) Outside the refractory period of the existing main wave position, finding out the maximum value point of the absolute value in a certain interval near each point, using the maximum value point as a candidate missing main wave position, and providing preparation for targeted supplement according to a clustering analysis result in a later period;
b) Based on the selected characteristics, performing adaptive cluster analysis on the main wave:
step 1: based on the main wave morphological characteristics, the main wave is subjected to self-adaptive cluster analysis, possible missing candidate main waves are supplemented, and possible specific main wave positions are found out:
(1) Obtaining waveform data within a certain range at two ends of each main wave point (note: because the normal main wave range is about 0.06s-0.08s, according to the actual analysis experience, the interval is about 0.07 s), subtracting the corresponding amplitude of the main wave point, and then taking an absolute value (note: considering the wide and large malformed main wave in the positive and negative directions) to obtain the waveform data to be gathered;
(2) Adopting a self-adaptive clustering method for waveform data to be clustered to obtain the classification number corresponding to the highest average evaluation index, and carrying out clustering analysis on the classification number to obtain the category evaluation index corresponding to each classification;
(3) Determining whether to enter a shape clustering module according to the overall evaluation index, each classification evaluation index and each classification average time limit: when the average time limit is larger, the evaluation index is required to be larger; conversely, when the average time limit is smaller, the evaluation index requirement can be lowered appropriately. The specific parameters are determined according to actual analysis data;
(4) When the waveform can be divided, the missing main wave position of the supplement candidate is considered, firstly, the centers of various types and the minimum and maximum distances between each point in the types and the center are calculated; secondly, calculating the distance between each candidate main wave position and the center of each class, taking the class with the minimum distance as a candidate class, inspecting whether the distance is within the maximum distance range in the class, and if the distance is within the range or slightly larger but has small difference or the distance from one class is far smaller than the distance from the adjacent class, classifying the candidate main wave position into the class; finally, recording the rest candidate main wave positions;
(5) And classifying and determining the specific main wave category. If the number of the categories is two, the average time limit corresponding to a certain category is the maximum, and the given requirement of the broad deformity main wave time limit is met, the category is taken as a specific main wave category; if the number of the categories is more than two, sorting the average time limit of each category from large to small, and taking all categories of which the average time limits meet the requirements as specific main waves;
(6) Individual calibrations are performed on the specific dominant wave in the forward direction. And carrying out cluster analysis on the forward main wave to determine whether the forward main wave can be halved. If the main wave has the forward specific main wave, determining whether to remove the main wave according to whether the forward main wave can be divided into two parts and the time limit of the individual main wave, and if the main wave does not have the forward specific main wave, determining whether to supplement the individual main wave according to whether the forward main wave can be divided into two parts and the time limit of the individual main wave;
(7) If the waveform is inseparable, considering the special condition of having a negative main wave, if the negative main wave meets the time limit requirement, determining the main wave as a specific main wave;
(8) If the requirement is not met, the specific main wave based on the morphological clustering analysis is empty;
step 2: based on the main wave interval characteristics, carrying out self-adaptive cluster analysis on the main wave interval, supplementing possible missing candidate main waves, and finding out possible specific main wave positions:
(1) Obtaining first-order difference data of main wave positions, namely interval data;
(2) Judging whether interval data is uniform according to interval range, if so, the interval is not separable, and if not, continuing to perform the following steps;
(3) Adopting a self-adaptive clustering method to obtain the classification number corresponding to the highest average evaluation index, and carrying out clustering analysis according to the classification number to obtain the evaluation index corresponding to each classification;
(4) If the average evaluation index is greater than a given value, such as 0.85, or the evaluation index within a class is greater than a given value, such as 0.98, the interval may be divided;
(5) Missing dominant wave positions of the complementary candidates are considered when the interval can be divided. Firstly, calculating various centers and the minimum and maximum distances between each point in each class and the center; secondly, calculating the distance between the front interval and the rear interval of each candidate main wave and the center of each class, taking the class with the minimum distance as a candidate class, investigating whether the distance is within the range of the maximum distance in the class, and classifying the front interval and the rear interval of the position of the candidate main wave as the class if the distance is slightly larger but has small difference or the distance from one class is far smaller than the distance from the adjacent class; finally, recording the rest candidate main wave positions;
(6) And classifying and determining the dominant wave class of the specific interval. Taking a rapid premature beat as an example: if the number of the categories is two, the interval corresponding to a certain category is the minimum and meets the given interval requirement, the category is taken as a specific interval category, and the interval positions plus 1 are taken as the corresponding specific main wave positions; if the number of the categories is more than two, sorting the average intervals of the categories from small to large, and adding 1 to interval positions corresponding to all categories of which the average intervals meet the requirements to obtain the specific main wave position. Escape and conduction block types are just opposite;
(7) If the evaluation index is smaller than the given value, the specific main wave based on interval clustering analysis is empty;
c) Comprehensively analyzing, and determining the type and specific position of the specific main wave: determining the type and position of a specific main wave based on the main wave morphology clustering analysis result and the main wave interval clustering result;
the result is as follows: when the morphological clustering analysis result and the interval clustering analysis result do not exist, the specific main wave is an empty set;
and a second result: when the morphological clustering analysis result and the interphase clustering analysis result exist one time, the corresponding specific main wave type and the specific position are selected;
and a third result: and when both the morphological clustering analysis result and the interphase clustering analysis result exist, taking the intersection of the morphological clustering analysis result and the interphase clustering analysis result as a final result.
Examples
Description of the embodiments
In order to test the effectiveness of the method, specific waveform recognition and positioning tests are carried out on the 130 residual single-lead electrocardiograms, and the accuracy reaches over 95 percent. The figure takes the identification and positioning of four single-lead electrocardiogram data specific waveforms as an example;
basic parameters of data: duration: 60 seconds, frequency: 250Hz;
during positioning, in order to better reflect the robustness of the program, the operation is carried out in the same set of parameters. The specific parameters are set as follows: a refractory period: 0.3 second; morphological clustering interval: 0.07 second; morphological clustering overall evaluation threshold: 0.85; morphological clustering classification evaluation threshold: 0.95; morphological clustering wide malformation class average time limit threshold: 0.12 second; interval clustering leveling threshold: 0.2 second; interval clustering overall evaluation threshold: 0.85; interval clustering classification evaluation threshold: 0.95; interval clustering premature beat type interval threshold: 90 times/sec.
The result of the calculation
Specific dominant waves identifying localization are indicated by red "∘":
1) One example of the specific main wave identification and location of the electrocardiogram of 'accidental ventricular premature beat' is shown in figure 2;
2) One example of the electrocardiogram specific dominant wave identification and location of frequent ventricular premature beats is shown in fig. 3;
3) One example of the specific dominant wave identification and location of the electrocardiogram of occasional atrial premature beats is shown in fig. 4;
4) An example of the specific main wave identification and location of the electrocardiogram of frequent atrial premature beats is shown in fig. 5;
as can be seen from FIGS. 2-5, the specific waveforms of each electrocardiogram are identified and located accurately.
The above embodiments are only for illustrating the technical idea of the present invention, and the technical idea of the present invention is not limited thereto, and any modifications made on the basis of the technical solution according to the technical idea of the present invention fall within the protective scope of the present invention.
Claims (2)
1. A single-lead specific main wave identification and positioning method based on cluster analysis comprises the following steps: a) Pre-treating; b) Based on the selected characteristics, performing adaptive clustering analysis on the main wave; c) Comprehensive analysis is carried out to determine the type and specific position of the specific main wave, and the method is characterized in that: the pretreatment method of a) comprises the following steps:
step 1: obtaining approximate positions of main waves on single-lead data based on a conventional analysis method, wherein the analysis method is a P-T calculation method or a QRS wave starting point and end point positioning method based on regularized least square regression learning;
step 2: removing baseline drift and high-frequency errors of original data by using MODWT (modified wavelet transform), wherein the MODWT is highly redundant non-orthogonal transform, the sample capacity can be any value, and has displacement invariance, selecting sym4 wavelet through comprehensive comparison analysis, analyzing to the 6 th layer, and selecting d2, d3, d4, d5 and d6 as reconstruction signals, thereby removing the baseline drift and the high-frequency errors;
and step 3: calibrating, namely determining the specific position and time limit of the existing main wave, and acquiring a candidate main wave position which is possibly missed, wherein the calibrating and determining the specific position and time limit of the existing main wave are divided into two steps:
for all initial dominant wave positions: replacing an original initial main wave position by an absolute maximum value point in a small interval near a certain initial main wave position, obtaining main wave main body directions according to all main wave directions, changing or keeping the original direction of the main wave directions which are respectively inconsistent with the main body directions according to amplitude sequencing of the main wave directions in the main direction and the amplitude condition of the main wave in the opposite direction, determining the specific position of the main wave, and determining each main wave time limit based on the slope change condition and the QRS normal time limit range;
outside the refractory period of the existing main wave position, finding out an absolute value maximum value point in a certain interval near each point as a candidate missing main wave position, and providing preparation for targeted supplement according to a clustering analysis result in a later period;
the b) self-adaptive clustering analysis method for the main wave based on the selected characteristics comprises the following steps:
step 1: based on the main wave morphological characteristics, the main wave is subjected to self-adaptive cluster analysis, possible missing candidate main waves are supplemented, and possible specific main wave positions are found out:
obtaining waveform data in a certain range at two ends of each main wave point, subtracting the corresponding amplitude of the main wave point, and then taking an absolute value to obtain waveform data to be gathered;
adopting a self-adaptive clustering method for waveform data to be clustered to obtain the classified number corresponding to the highest average evaluation index, and carrying out clustering analysis on the classified number to obtain a category evaluation index corresponding to each classification;
determining whether to enter a shape clustering module according to the overall evaluation index, each classification evaluation index and each classification average time limit: when the average time limit is larger, the evaluation index is required to be larger; conversely, when the average time limit is smaller, the evaluation index requirement can be properly lowered, and the specific parameters are determined according to the actual analysis data;
when the waveform can be divided, the missing main wave position of the supplement candidate is considered, firstly, the centers of various types and the minimum and maximum distances between each point in the types and the center are calculated; secondly, calculating the distance between each candidate main wave position and the center of each class, taking the class with the minimum distance as a candidate class, and inspecting whether the distance is within the maximum distance range in the class, wherein the distance is slightly larger but has small difference or the distance from one class is far smaller than the distance from the adjacent class, so that the candidate main wave position is classified into the class; finally, recording the remaining candidate main wave positions;
classifying, namely determining two types of specific main wave types, wherein the number of the types is two, the average time limit corresponding to a certain type is the largest, and the given requirements on the main wave time limit of wide deformity are met, and then taking the type as the specific main wave type; if the number of categories is more than two, sorting the various average time limits from large to small, and taking all categories of which the average time limits meet the requirements as specific main waves;
carrying out individual calibration on the forward specific main wave, carrying out cluster analysis on the forward main wave, and determining whether the forward main wave can be divided into two parts or not; if the main wave has the forward specific main wave, determining whether to remove the main wave according to whether the forward main wave can be divided into two parts and the time limit of the individual main wave, and if the main wave does not have the forward specific main wave, determining whether to supplement the individual main wave according to whether the forward main wave can be divided into two parts and the time limit of the individual main wave;
the waveform is inseparable, the special condition with the negative main wave is considered, and the negative main wave meets the time limit requirement, and the main wave is determined to be the specific main wave;
if the specific main wave does not meet the requirement, the specific main wave based on morphological clustering analysis is empty;
and 2, step: based on the main wave interval characteristics, carrying out self-adaptive cluster analysis on the main wave interval, supplementing possible missing candidate main waves, and finding out possible specific main wave positions:
obtaining first-order difference data of main wave positions, namely interval data;
judging whether interval data is uniform according to interval range, if so, the interval is not separable, and if not, continuing to perform the following steps;
adopting a self-adaptive clustering method to obtain the classification number corresponding to the highest average evaluation index, and carrying out clustering analysis according to the classification number to obtain the evaluation index corresponding to each classification;
if the average evaluation index is greater than a given value or the evaluation index in a certain class is greater than a given value, the interval can be divided;
when the interval is available, the missing main wave position of the supplement candidate is considered, firstly, the centers of various classes and the minimum and maximum distances between each point in the classes and the center are calculated; secondly, calculating the distance between the front interval and the rear interval of each candidate main wave and the center of each class, taking the class with the minimum distance as a candidate class, and inspecting whether the distance is within the range of the maximum distance in the class, wherein the distance is slightly larger but has small difference or the distance from one class is far smaller than the distance from the adjacent class, and classifying the front interval and the rear interval of the position of the candidate main wave into the class; finally, recording the remaining candidate main wave positions;
classifying, and determining the main wave class of the specific interval, taking the rapid premature beat as an example: the number of categories is two, the interval corresponding to a certain category is the minimum, and the given interval requirement is met, the category is taken as a specific interval category, and the interval positions plus 1 are taken as the corresponding specific main wave positions; if the number of categories is more than two, sorting the average intervals from small to large, taking the interval positions corresponding to all categories with the average interval meeting the requirements plus 1 as the specific main wave position, and the escape and conduction block types are just opposite;
and if the evaluation index is less than the given value, the specific main wave based on interval clustering analysis is empty.
2. The method for identifying and positioning the single-guide specific main wave based on the cluster analysis according to claim 1, wherein: the c) comprehensive analysis and the method for determining the specific main wave type and the specific position comprise the following steps: determining the type and position of a specific main wave based on the main wave morphology clustering analysis result and the main wave interval clustering result;
the result is as follows: when the morphological clustering analysis result and the interval clustering analysis result do not exist, the specific main wave is an empty set;
and a second result: when the morphological clustering analysis result and the interphase clustering analysis result exist one time, the corresponding specific main wave type and the specific position are selected;
and a third result: and when both the morphological clustering analysis result and the interphase clustering analysis result exist, taking the intersection of the morphological clustering analysis result and the interphase clustering analysis result as a final result.
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