CN116784860B - Electrocardiosignal characteristic extraction system based on morphological heart beat template clustering - Google Patents
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Abstract
The invention discloses an electrocardiosignal characteristic extraction system based on morphological heart beat template clustering, which comprises the following steps: the device comprises a data acquisition module, an extraction module, a data processing module and a classification analysis module; the data acquisition module is used for acquiring electrocardiographic data in a motion state; the extraction module is used for extracting the QRS complex based on the electrocardiograph data to obtain an extracted signal, enhancing the extracted signal and extracting an R peak of the enhanced extracted signal; the data processing module is used for processing the extracted signals based on the R peak to obtain QRS complex heart beats and heart beat characteristic parameters; the classification analysis module is used for classifying the QRS complex heart beats based on the heart beat characteristic parameters to obtain a template library; and carrying out matching analysis on the target heart beat waveform and templates in the template library to obtain an updated template library, and processing electrocardiosignals based on the updated template library. The method has the advantages of high time efficiency of extracting the characteristic value and no need of consuming a large amount of time and calculation resources.
Description
Technical Field
The invention belongs to the technical fields of medical and health, biomedicine and electronic information, and particularly relates to an electrocardiosignal characteristic extraction system based on morphological heart beat template clustering.
Background
Cardiovascular disease is an important disease that severely threatens human health, the prevalence is rising year by year and the diseased population is getting younger and younger, and cardiovascular disease treatment is very complex, requiring long-term monitoring and prevention. An electrocardiogram is one of the common clinical detection modes for diagnosing cardiovascular diseases, and a user needs to lie on a bed in a static state during measurement so as to avoid interference of various noises on electrocardiosignals. However, due to randomness and uncontrollable cardiovascular diseases, the short-time long electrocardiogram of a hospital can only monitor the change condition of heart activity in a short time, and whether the electrocardiosignals are abnormal or not cannot be accurately judged, so that the electrocardiosignals need to be dynamically and stably monitored for a long time. Along with the development of wearable equipment, the monitoring of the dynamic electrocardiogram is mainly completed in the wearable equipment, and the electrocardio electrode can adopt a more comfortable fabric electrode to replace a disposable electrocardio electrode patch, but because the fabric electrode has larger impedance and cannot be firmly fixed on human skin, the electrode-skin impedance change is easily caused by friction with the skin in the movement process, and muscle movement exists during dynamic monitoring, so that the wearable dynamic electrocardiogram is distorted to lose the original information during the monitoring. Based on the above-mentioned problems, a new recognition algorithm for extracting electrocardiosignal information in a motion state is needed.
Disclosure of Invention
The invention aims to solve the defects of the prior art and provides an electrocardiosignal characteristic extraction system based on morphological heart beat template clustering so as to accurately extract the characteristics of electrocardiosignals of a human body in a motion state.
In order to achieve the above object, the present invention provides the following solutions: electrocardiosignal characteristic extraction system based on morphology heart beat template clustering comprises: the device comprises a data acquisition module, an extraction module, a data processing module and a classification analysis module;
The data acquisition module is used for acquiring electrocardiographic data in a motion state;
The extraction module is connected with the data acquisition module and is used for extracting a QRS complex based on the electrocardiograph data to obtain an extraction signal, enhancing the extraction signal and extracting an R peak of the enhanced extraction signal;
The data processing module is connected with the extraction module and is used for processing the extracted signals based on the R peak to obtain QRS wave group heart beats and heart beat characteristic parameters;
The classification analysis module is connected with the data processing module and is used for classifying the QRS complex heart beats based on the heart beat characteristic parameters to obtain a template library; and carrying out matching analysis on the target heart beat waveform and templates in the template library to obtain an updated template library, and processing electrocardiosignals based on the updated template library.
Preferably, the heart beat characteristic parameters include: QRS wave width, QRS wave area, QRS wave height, QRS wave obesity index, and QRS wave peak coarse-pitch index.
Preferably, the QRS wave obesity index calculating method includes:
FQRS＝AQRS/|HQRS|
Where F QRS represents the QRS wave obesity index, A QRS represents the QRS wave area, and H QRS represents the QRS wave height.
Preferably, the QRS wave peak coarse-ton index calculating method includes:
Where AG QRS represents the QRS wave peak coarse-pitch index, f (n) represents the sampling value of the nth sampling point of the electrocardiosignal, T R represents the position of the R wave peak value point, MS20 represents the number of the corresponding sampling points after 20 milliseconds, and H QRS represents the QRS wave height.
Preferably, the method for obtaining the template library comprises the following steps: classifying the QRS complex heart beats according to the heart beat characteristic parameters to obtain QRS heart beat templates under three motion states; and carrying out hierarchical clustering on the electrocardiograph data, wherein the QRS heart beat templates respectively correspond to the QRS heart beat templates under three motion states, and the QRS heart beat templates form the template library.
Preferably, the method for obtaining the QRS heart beat template under three motion states comprises the following steps:
Defining each QRS complex heart beat as a cluster, and calculating the difference value of all the QRS complex heart beats according to the heart beat characteristic parameters; combining the two clusters with the smallest difference value into a new cluster; calculating dissimilarity between the new cluster and other clusters, updating a similarity matrix based on the dissimilarity, and carrying out the next iteration until the preset iteration times are finished, so as to obtain the QRS heart beat template under three motion states.
Preferably, the method for performing the matching analysis comprises:
Constructing an upper limit and a lower limit of a contour waveform by taking a reference waveform in a QRS heart beat template as a center to form a contour window for waveform detection;
aligning a target heart beat waveform with a waveform in the QRS heart beat template in an R peak position, and calculating a difference value of the target heart beat waveform and the waveform in the QRS heart beat template at a time point in the contour window; dividing the difference value at the time point by the sum of the difference values of the P wave, R wave and T wave peaks of the waveforms in the target heart beat waveform and the QRS heart beat template; and obtaining a QRS complex heart beat difference value, wherein the QRS complex heart beat difference value is smaller than 0.7, and the matching is successful.
Preferably, the method for obtaining the updated template library comprises the following steps:
When the waveform in the QRS heart beat template has a waveform matched with the target heart beat waveform, adding the target heart beat waveform into the QRS heart beat template, and updating the QRS heart beat template to obtain the updated template library;
Checking whether the waveforms in the QRS heart beat templates are 8 or not when the waveforms in the QRS heart beat templates are not matched with the waveforms in the target heart beat templates, and when the waveforms in the QRS heart beat templates are not 8, taking the target heart beat waveforms as prototypes, establishing a new template in the QRS heart beat templates, and setting the number of the target heart beat waveform templates as the number of the new template; when the number of the QRS heart beat templates reaches 8, deleting one template in the QRS heart beat templates, taking the target heart beat waveform as a prototype, establishing a new template in the QRS heart beat templates, and setting the number of the target heart beat waveform template as the number of the new template.
Compared with the prior art, the invention has the beneficial effects that:
the electrocardiosignal characteristic extraction system based on morphological heart beat template clustering has the advantages of high time efficiency for extracting characteristic values and no need of consuming a large amount of time and calculation resources. (1) The precision is high, fine structures and related features in the electrocardiosignal can be accurately captured, and the precision of identification and analysis of the electrocardiosignal is improved; (2) robust and interpreted: compared with a neural network algorithm, the morphological heart beat template clustering algorithm has stronger robustness and interpretation, and can better describe the characteristics, trend and abnormal condition of electrocardiosignals; (3) high interpretability: the diagnosis result can be interpreted through the specific form and parameter characteristics of the heartbeat template, and the diagnosis result is more visual and easy to understand and realize.
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In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the embodiments are briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for matching analysis by a classification analysis module according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a template library portion in a stationary state according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a portion of a template library sample in a routine state of the present invention;
FIG. 4 is a schematic diagram of a portion of a template library in a running state according to an embodiment of the present invention;
FIG. 5 is a graph of an electrocardiographic signal spectrum in a resting state according to an embodiment of the present invention;
FIG. 6 is a graph of an electrocardiographic signal after noise signal removal in a stationary state according to an embodiment of the present invention;
FIG. 7 is a graph of an electrocardiographic signal spectrum in a routine of the present invention;
FIG. 8 is a graph of an electrocardiographic signal after noise signal removal in a routine state of the present invention;
FIG. 9 is a graph of the electrocardiographic signal spectrum in the running state according to the embodiment of the present invention;
fig. 10 is a graph of an electrocardiographic signal after removing noise signals in a running state according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1
The embodiment provides an electrocardiosignal characteristic extraction system based on morphological heart beat template clustering, which comprises the following steps: the device comprises a data acquisition module, an extraction module, a data processing module and a classification analysis module;
the data acquisition module is used for acquiring electrocardiographic data in a motion state.
In this embodiment, the electrocardiographic data includes: electrocardiogram data f 1(x)、f2(x)、f3(x) collected in three motion states of stationary, running (10 km/h) and walking (4 km/h).
The extraction module is connected with the data acquisition module and is used for extracting the QRS complex based on the electrocardiograph data to obtain an extraction signal, enhancing the extraction signal and extracting an R peak of the enhanced extraction signal.
In this embodiment, the Patrick s.hamilton algorithm is used to extract QRS complexes of the electrocardiographic data under the three motion states, and the extracted signals after extraction are f 1QRS(x)、f2QRS(x)、f3QRS(x) respectively. And then enhancing by adopting a window sliding integral function, and extracting R peaks of the enhanced extracted signals by adopting a double-slope variable threshold method.
The data processing module is connected with the extraction module and is used for processing the extracted signals based on the R peak to obtain QRS complex heart beats and heart beat characteristic parameters.
The processing method comprises the following steps:
The extracted signal firstly passes through a 16Hz low-pass filter and then passes through an 8Hz high-pass filter to filter out respiratory interference, power frequency interference and myoelectric noise; the baseline shift is then removed by a median filter and the electrocardiographic signal baseline is pulled to zero potential. The purpose of using strong filtering (8-16 Hz) in the preprocessing is to maximize the QRS wave energy, while pulling the baseline to zero potential is to facilitate calculation of the characteristic parameters such as the subsequent burst area.
Specifically, firstly, determining a starting point T 0 and an ending point T s of each QRS complex heart beat waveform according to the extracted R peak position, and calculating heart beat characteristic parameters; the heart beat characteristic parameters of the embodiment comprise: QRS wave width (W QRS), QRS wave area (a QRS), QRS wave height (H QRS), QRS wave obesity index (F QRS) and QRS wave peak coarse-to-ton index (AG QRS).
The QRS wave width (W QRS) calculating method is as follows:
WQRS＝Ts-T0
Where W QRS represents the QRS wave width, T s represents the end point of the QRS complex heart beat waveform, and T 0 represents the start point of the QRS complex heart beat waveform.
The QRS complex area (a QRS) calculation method is: the start point T 0 of the QRS complex heart beat waveform starts, absolute value integration is carried out on the preprocessed electrocardio data sampling value, and the end point T s of the QRS complex heart beat waveform ends. The calculation formula is as follows:
Where A QRS represents the QRS wave area, f (n) represents the sampled value of the nth sampling point of the electrocardiosignal, and T Q represents the starting point of the QRS wave.
The QRS wave height (H QRS) calculating method comprises the following steps: the sampled value of the preprocessed electrocardiographic data at the R peak value point position (T R), wherein a positive value represents the R peak forward direction, and a negative value represents the R peak reverse direction. The calculation formula is as follows:
HQRS＝f(TR)
Where H QRS denotes the QRS wave height and T R denotes the R peak value point position.
The method for calculating the QRS wave obesity index (F QRS) comprises the following steps: the ratio of QRS complex heart beat waveform area to the absolute value of height. The calculation formula is as follows:
FQRS＝AQRS/|HQRS|
Where F QRS represents the QRS wave obesity index, A QRS represents the QRS wave area, and H QRS represents the QRS wave height.
The calculation method of the QRS wave peak coarse-ton index (AG QRS) is as follows:
Where AG QRS represents the QRS wave peak coarse-pitch index, f (n) represents the sampling value of the nth sampling point of the electrocardiosignal, T R represents the position of the R wave peak value point, MS20 represents the number of the corresponding sampling points after 20 milliseconds, and H QRS represents the QRS wave height.
The classification analysis module is connected with the data processing module and is used for classifying the QRS complex heart beats based on the heart beat characteristic parameters to obtain a template library; and carrying out matching analysis on the target heart beat waveform and templates in the template library to obtain an updated template library, and processing electrocardiosignals based on the updated template library.
Specifically, the method for obtaining the template library comprises the following steps: classifying the QRS complex cardiac beats according to the five cardiac beat characteristic parameters, and classifying all cardiac beats by adopting a hierarchical clustering algorithm to obtain QRS cardiac beat templates under three motion states; specifically, the QRS complex heart beat is defined as a cluster, and the difference values of all the QRS complex heart beats are calculated according to the heart beat characteristic parameters;
The method for calculating the QRS complex heart beat difference value S DIFF comprises the following steps:
wherein T (n) is a sampling value of an nth sampling point of the template heart beat waveform data, X (n) is a sampling value of an nth sampling point of the target heart beat waveform data, R peak is an R peak position, K D is a constant, T max(Tmin) is a maximum (small) value of the template heart beat waveform data within a range of [ R peak-KD,Rpeak+KD ], and X max(Xmin) is a maximum (small) value of the target heart beat waveform data within a range of [ R peak-KD,Rpeak+KD ].
Combining the two clusters with the smallest difference value into a new cluster; calculating dissimilarity between the new cluster and other clusters, updating a similarity matrix based on the dissimilarity, and carrying out the next iteration until the preset iteration times are finished. And obtaining QRS heart beat templates under three motion states. And carrying out hierarchical clustering on the electrocardiograph data, and obtaining P 11(x),P12(x),…,P21(x),P22(x),…,P31(x),P32(x) and … after the electrocardiograph data f 1(x)、f2(x)、f3(x) are subjected to hierarchical clustering, wherein the QRS heart beat templates respectively correspond to the QRS heart beat templates S 1、S2、S3 under the three motion states, and the QRS heart beat templates form a template library. As shown in fig. 2,3 and 4.
The method for matching and analyzing the electrocardio data and the templates in the template library comprises the following steps:
In the embodiment, an outline limiting and accumulated difference method is adopted to match the electrocardiographic data with templates in a template library; the contour limit refers to the waveform in the QRS heart beat template as a reference waveform of the contour limit, and the reference waveform is taken as the center to construct an upper limit Δf 1 and a lower limit Δf 2 of the contour waveform, so as to form a contour window of waveform detection, namely the contour window is x=f r(x)+Δf1-Δf2, wherein r takes the values of 1,2 and 3. The cumulative difference method is to pair the target heart beat waveform and the waveform in the QRS heart beat template at the R peak position, and then calculate the difference value of the target waveform and the waveform in the QRS heart beat template at each time point within the range of the contour window (generally the width of the QRS wave is 60-100 ms); dividing the difference value at the time point by the sum of the difference values of the P wave, the R wave and the T wave peaks of the target heart beat waveform and the waveform in the QRS heart beat template to obtain a QRS wave group heart beat difference value S DIFF.
In this embodiment, the judging conditions for successful matching of the target heart beat waveform and the waveform in the QRS heart beat template include:
(1) The R peak is in the same direction;
(2) The body types are the same;
(3) I H QRS(X)-HQRS(T)|<|HQRS(X)+HQRS (T) |/8, where H QRS (X) is the QRS wave height of the target heart beat waveform and H QRS (T) is the QRS wave height of the waveform in the QRS heart beat template;
(4) W QRS(X)-WQRS(T)|≤20ms,WQRS (X) is the QRS wave width of the target heart beat waveform, W QRS (T) is the QRS wave width of the waveform in the QRS heart beat template;
(5)SDIFF<0.7。
specifically, as shown in fig. 1, the method for obtaining the updated template library includes:
When the waveform in the QRS heart beat template is matched with the target heart beat waveform, adding the target heart beat waveform into the QRS heart beat template by P mi(x) (m is the type of the QRS heart beat template and i is the number of signals in the QRS heart beat template), and updating the QRS heart beat template to obtain an updated template library.
Checking whether the waveforms in the QRS heart beat templates reach 8 when the waveforms in the QRS heart beat templates are not matched with the waveforms of the target heart beat, when the waveforms in the QRS heart beat templates are not matched with the waveforms of the target heart beat templates, taking the waveforms of the target heart beat templates as prototypes, establishing a new template in the QRS heart beat templates, and setting the number of the template of the waveform of the target heart beat as the number S n (n is more than or equal to 4 and less than or equal to 8) of the new template; when the number of the QRS heart beat templates reaches 8, deleting one of the templates, taking the target heart beat waveform as a prototype, establishing a new template in the QRS heart beat template, and setting the number of the target heart beat waveform template as the number of the new template. And clustering the electrocardiosignals by using the updated template library. As can be seen in FIGS. 5-10, the invention can effectively remove the motion noise of the electrocardiosignal.
The above embodiments are merely illustrative of the preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, but various modifications and improvements made by those skilled in the art to which the present invention pertains are made without departing from the spirit of the present invention, and all modifications and improvements fall within the scope of the present invention as defined in the appended claims.
Claims (5)
1. Electrocardiosignal characteristic extraction system based on morphology heart beat template clustering, which is characterized by comprising: the device comprises a data acquisition module, an extraction module, a data processing module and a classification analysis module;
The data acquisition module is used for acquiring electrocardiographic data in a motion state;
The extraction module is connected with the data acquisition module and is used for extracting a QRS complex based on the electrocardiograph data to obtain an extraction signal, enhancing the extraction signal and extracting an R peak of the enhanced extraction signal;
The data processing module is connected with the extraction module and is used for processing the extracted signals based on the R peak to obtain QRS wave group heart beats and heart beat characteristic parameters;
The classification analysis module is connected with the data processing module and is used for classifying the QRS complex heart beats based on the heart beat characteristic parameters to obtain a template library; matching and analyzing the target heart beat waveform with templates in the template library to obtain an updated template library, and processing electrocardiosignals based on the updated template library;
The heart beat characteristic parameters comprise: QRS wave width, QRS wave area, QRS wave height, QRS wave obesity index, and QRS wave peak coarse-pitch index;
the QRS wave obesity index calculating method comprises the following steps:
Wherein F QRS represents the QRS wave obesity index, A QRS represents the QRS wave area, and H QRS represents the QRS wave height;
The QRS wave peak coarse-pitch index calculation method comprises the following steps:
Where AG QRS represents the QRS wave peak coarse-pitch index, f (n) represents the sampling value of the nth sampling point of the electrocardiosignal, T R represents the position of the R wave peak value point, MS20 represents the number of the corresponding sampling points after 20 milliseconds, and H QRS represents the QRS wave height.
2. The system for extracting electrocardiographic features based on morphological heart beat template clustering according to claim 1, wherein the method for obtaining the template library comprises: classifying the QRS complex heart beats according to the heart beat characteristic parameters to obtain QRS heart beat templates under three motion states; and carrying out hierarchical clustering on the electrocardiograph data, wherein the QRS heart beat templates respectively correspond to the QRS heart beat templates under three motion states, and the QRS heart beat templates form the template library.
3. The system for extracting the characteristic of the electrocardiosignal based on the morphological heart beat template clustering as claimed in claim 2, wherein the method for obtaining the QRS heart beat templates under three motion states comprises the following steps:
Defining each QRS complex heart beat as a cluster, and calculating the difference value of all the QRS complex heart beats according to the heart beat characteristic parameters; combining the two clusters with the smallest difference value into a new cluster; calculating dissimilarity between the new cluster and other clusters, updating a similarity matrix based on the dissimilarity, and carrying out the next iteration until the preset iteration times are finished, so as to obtain the QRS heart beat template under three motion states.
4. The system for extracting electrocardiographic features based on morphological heart beat template clustering according to claim 2, wherein the method for performing matching analysis comprises:
Constructing an upper limit and a lower limit of a contour waveform by taking a reference waveform in a QRS heart beat template as a center to form a contour window for waveform detection;
aligning a target heart beat waveform with a waveform in the QRS heart beat template in an R peak position, and calculating a difference value of the target heart beat waveform and the waveform in the QRS heart beat template at a time point in the contour window; dividing the difference value at the time point by the sum of the difference values of the P wave, R wave and T wave peaks of the waveforms in the target heart beat waveform and the QRS heart beat template; and obtaining a QRS complex heart beat difference value, wherein the QRS complex heart beat difference value is smaller than 0.7, and the matching is successful.
5. The system for extracting electrocardiographic features based on morphological heart beat template clustering according to claim 4, wherein the method for obtaining the updated template library comprises:
When the waveform in the QRS heart beat template has a waveform matched with the target heart beat waveform, adding the target heart beat waveform into the QRS heart beat template, and updating the QRS heart beat template to obtain the updated template library;
Checking whether the waveforms in the QRS heart beat templates are 8 or not when the waveforms in the QRS heart beat templates are not matched with the waveforms in the target heart beat templates, and when the waveforms in the QRS heart beat templates are not 8, taking the target heart beat waveforms as prototypes, establishing a new template in the QRS heart beat templates, and setting the number of the target heart beat waveform templates as the number of the new template; when the number of the QRS heart beat templates reaches 8, deleting one template in the QRS heart beat templates, taking the target heart beat waveform as a prototype, establishing a new template in the QRS heart beat templates, and setting the number of the target heart beat waveform template as the number of the new template.
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