CN114469131A - Self-adaptive real-time electrocardiosignal quality evaluation method - Google Patents
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Abstract
The application discloses a self-adaptive real-time electrocardiosignal quality evaluation method, a device, equipment and a storage medium thereof, wherein the method comprises the following steps: extracting the characteristics of the power spectrum signal quality by utilizing an AR model; inputting the extracted features into a K-means clustering algorithm, and realizing the self-adaptive calculation of a signal quality matching template through the K-means clustering algorithm; the quantification of the calculated signal quality is evaluated according to a similarity comparison method. According to the scheme provided by the application, the influence of certain differences of the electrocardiogram between different people and different periods of the same person on the signal quality evaluation precision is solved; meanwhile, the problem that the change of the heartbeat waveform caused by diseases is mistakenly identified as a noise interference signal is effectively solved.
Description
Technical Field
The invention relates to the technical field of wearable monitoring, in particular to a self-adaptive real-time electrocardiosignal quality evaluation method, device, equipment and a storage medium thereof.
Background
Wearable monitoring of physiological signals is an important way to realize early diagnosis of daily cardiovascular diseases outside hospitals. However, in practical application, the existing wearable monitoring system has the defect of low model precision, and cannot meet the requirements of medical-grade continuous physiological monitoring and disease risk prevention and control. One important reason is that the physiological signals monitored by wearable devices are susceptible to noise, especially motion artifacts, lead-off and electromyographic interference caused by daily activities. When the noise interference is serious, the real signal cannot be recovered by the denoising technology. Therefore, in order to improve the accuracy of signal analysis, it is necessary to remove signal fragments with poor quality through signal quality evaluation. In the prior art, the waveform parameters of signal quality are obtained by calculating the area difference under QRS complex waves among different leads of an ECG signal, and finally the quality evaluation of the multi-lead ECG signal is realized by a statistical method of a histogram and a cumulative histogram; or the existing signal quality evaluation algorithm of multi-lead ECG signal fusion, which realizes the fusion of multi-lead ECG by using the basic idea of local weighted linear prediction, and simultaneously, in order to effectively reserve the quality-related characteristics of ECG signals, the method also uses a fuzzy inference system for weighted value estimation of multi-lead characteristics; or decomposing the ECG signal to different frequency bands by using wavelet transform, then calculating time domain characteristics such as maximum absolute value amplitude, zero crossing point, kurtosis and waveform autocorrelation coefficient for each frequency band signal, and finally carrying out signal quality classification and identification on the combined characteristics through a classifier.
For an online wearable cardiovascular disease monitoring system, signal quality evaluation is generally carried out by an unsupervised method depending on a single type of characteristics due to real-time requirements, and the method has the advantages of simplicity in implementation and good real-time performance, but can easily identify signal waveform changes caused by heart diseases as signals with poor signal quality in a wrong mode. The heart disease waveform is rejected after being identified as a poor quality signal, so that the early warning effect on the occurrence of the cardiovascular event is lost, and the occurrence rate of the event is increased.
Disclosure of Invention
In view of the above-mentioned drawbacks and deficiencies of the prior art, it is desirable to provide an adaptive real-time cardiac signal quality assessment method, apparatus, device and storage medium thereof.
In a first aspect, an embodiment of the present application provides a method for evaluating quality of an adaptive real-time electrocardiographic signal, where the method includes: extracting the characteristics of the power spectrum signal quality by utilizing an AR model; inputting the extracted features into a K-means clustering algorithm, and realizing the self-adaptive calculation of a signal quality matching template through the K-means clustering algorithm; the calculated quantification of the signal quality is evaluated according to a similarity comparison method.
In one embodiment, before the extracting the feature of the power spectrum signal quality by using the AR model, the method further includes: detecting R peak of ECG by waveform detection algorithm, dividing heartbeat by using R peak as reference point, and dividing heartbeat by using R peak as reference pointnRepresents the n-th R peak and the m-th heart beat segment represents RnAnd Rn+2In (2), 4, 6, 8, and m n/2.
In one embodiment, the extracting the feature of the power spectrum signal quality by using the AR model includes: selecting an AR model with the order of 24-28; for ECGmDetermining power spectrum characteristic PSD of heart beat signal through AR model pairmWherein, the ECGmRepresenting the mth heart beat segment.
In one embodiment, the inputting the extracted features into a K-means clustering algorithm, and implementing adaptive computation of a signal quality matching template by the K-means clustering algorithm includes: power spectrum characteristic set PSD (phase-sensitive detector) by K-means clustering algorithm with K being 1mCalculating a clustering center; and selecting preset c heart beats for averaging to serve as a similarity matching template.
In one embodiment, the evaluating the quantification of the calculated signal quality according to a similarity comparison method includes: measuring the similarity between each heart beat and the template heart beat by a Pearson correlation coefficient method; and judging whether the quality of the heartbeat signal is accepted or not according to a set threshold value TR, and when the correlation coefficient of each heartbeat segment and the template is greater than the threshold value, considering the signal quality of the heartbeat to be acceptable, otherwise, judging the signal quality to be unacceptable.
In a second aspect, an embodiment of the present application further provides an adaptive real-time cardiac signal quality assessment apparatus, where the apparatus includes: the extraction unit is used for extracting the characteristics of the power spectrum signal quality by utilizing the AR model; the matching unit is used for inputting the extracted features into a K-means clustering algorithm and realizing the self-adaptive calculation of the signal quality matching template through the K-means clustering algorithm; and the evaluation unit is used for evaluating the quantification of the calculated signal quality according to a similarity comparison method.
In a third aspect, an embodiment of the present application further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the method according to any one of the descriptions in the embodiment of the present application.
In a fourth aspect, an embodiment of the present application further provides a computer device, which is a computer-readable storage medium, and a computer program is stored thereon, where the computer program is configured to: which when executed by a processor implements a method as described in any of the embodiments of the present application.
The invention has the beneficial effects that:
the self-adaptive real-time electrocardiosignal quality evaluation method provided by the invention solves the problem that the electrocardiogram has certain difference between different people and different periods of the same person, so that the signal quality evaluation precision is influenced; meanwhile, the problem that the change of the heartbeat waveform caused by diseases is mistakenly identified as a noise interference signal is effectively solved.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is a schematic flow chart illustrating a method for adaptive real-time quality evaluation of electrocardiographic signals according to an embodiment of the present application;
FIG. 2 illustrates an exemplary block diagram of an adaptive real-time cardiac signal quality assessment apparatus 200 according to an embodiment of the present application;
FIG. 3 illustrates a schematic structural diagram of a computer system suitable for use in implementing a terminal device of an embodiment of the present application;
FIG. 4 shows a schematic diagram of a segment of an ECG signal containing noise interference and heart beat segmentation results provided by an embodiment of the present application;
fig. 5 shows a segment of ECG signals containing ventricular premature beats and the results of heart beat segmentation provided by an embodiment of the present application.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and are not to be considered limiting of the invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
It will be understood that when an element is referred to as being "secured to" or "disposed on" another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "upper," "lower," "left," "right," and the like as used herein are for illustrative purposes only and do not denote a unique embodiment.
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating a method for adaptive real-time cardiac signal quality assessment according to an embodiment of the present disclosure.
As shown in fig. 1, the method includes:
By adopting the technical scheme, the influence of certain difference of the electrocardiogram between different people and different periods of the same person on the signal quality evaluation precision is solved; meanwhile, the problem that the change of the heartbeat waveform caused by diseases is mistakenly identified as a noise interference signal is effectively solved.
In some embodiments, before the extracting the feature of the power spectrum signal quality by using the AR model in the present application, the method further includes: detecting R peak of ECG by waveform detection algorithm, dividing heartbeat by using R peak as reference point, and dividing heartbeat by using R peak as reference pointnRepresents the nth R peak and represents R by the mth heart beat segmentnAnd Rn+2In (2), 4, 6, 8, and m n/2.
In some embodiments, the extracting of the feature of the power spectrum signal quality by using the AR model in the present application includes: selecting an AR model with the order of 24-28; for ECGmDetermining power spectrum characteristic PSD of heart beat signal through AR model pairmWherein, the ECGmRepresenting the mth heart beat segment.
In some embodiments, the inputting the extracted features into a K-means clustering algorithm, and the adaptive computation of the signal quality matching template through the K-means clustering algorithm includes: PSD (power spectral feature set) of power spectrum feature set through K-means clustering algorithm with K being 1mCalculating a clustering center; and selecting preset c heart beats for averaging to serve as a similarity matching template.
In some embodiments, the evaluation of the quantification of the calculated signal quality based on similarity comparison in the present application comprises: measuring the similarity between each heart beat and the template heart beat by a pearson correlation coefficient method; and judging whether the quality of the heartbeat signal is accepted or not according to a set threshold value TR, and when the correlation coefficient of each heartbeat segment and the template is greater than the threshold value, considering the signal quality of the heartbeat to be acceptable, otherwise, judging the signal quality to be unacceptable.
In summary, the technical solution of the present invention mainly requires the following three links: (1) extracting the quality characteristics of the power spectrum signals: the invention provides a method for estimating a power spectrum by using an AR model as a signal quality characteristic. In power spectrum estimation, the accuracy of power spectrum estimation is reduced due to the lower order of the AR model, and false peaks are generated due to the higher number of nodes. Therefore, selecting the optimal AR model order is particularly important for accurate estimation of the power spectrum. Experiments verify that the most accurate power spectrum estimation can be obtained when the order of the AR model is 24-28. (2) Calculating a signal quality matching template: for the unsupervised beat-to-beat signal quality detection method, firstly, a template is determined by using a 'clean' ECG heart beat cycle, then the similarity of the detected heart beat and the template is calculated by a similarity measurement method, and when the similarity exceeds a specified threshold value, the signal quality of the heart beat cycle is considered to be 'acceptable'. Because the electrocardiogram has certain differences between different persons and between different periods of the same person, in order to avoid the influence of the differences on the accuracy, a heartbeat template needs to be selected in a self-adaptive manner during signal measurement. The key to adapting the template selection is to avoid as much as possible the interference of the poor quality signal, which requires the removal of the abnormal heart beat signal. (3) Similarity measurement: common methods are Euclidean Distance (Euclidean Distance), Cosine Distance (Cosine Distance) and Pearson Correlation Coefficient (PCC). In the three methods, the Euclidean distance is calculated as the real distance between two sample feature vectors, and the number has no exact range; the cosine distance is the cosine of an included angle between two sample characteristic vectors, the numerical range is-1 to 1, the-1 indicates that the directions of the two vectors are opposite, and the 1 indicates that the directions are the same; PCC is a measure of the linear correlation between two sample vectors, ranging from 0 to 1, with larger values giving greater correlation. Clearly, the pearson correlation coefficient is more suitable for the present problem.
For a given segment of an ECG signal, unsupervised beat-to-beat signal quality detection-based essentially comprises the steps of:
1) detecting R peak of ECG by waveform detection algorithm, and performing heart beat segmentation with R peak as reference pointnDenotes the nth R peak, with the mth heart beatFragment represents RnAnd Rn+2In (2), 4, 6, 8, and m n/2. For convenience of presentation, at the same time, ECG is usedmRepresenting the mth heart beat segment.
2) For ECGmEstimating power spectrum characteristic PSD of heart beat signal by AR modelm。
3) Power spectrum characteristic set PSD by K-means algorithm with K being 1mAnd (5) solving a clustering center, and selecting the nearest c heart beats to average as a similarity matching template.
4) The similarity between each heart beat and the template heart beat is measured using the pearson correlation coefficient method, with higher similarity indicating a higher quality score and vice versa. When in application, whether the quality of the heartbeat signal is accepted is judged according to a set threshold value TR, and the signal quality of the heartbeat is considered to be accepted when the correlation coefficient is larger than the threshold value, otherwise, the signal quality of the heartbeat is not accepted.
The invention respectively uses the time domain waveform characteristics and the frequency domain power spectrum characteristics to carry out beat-to-beat quality judgment on the ECG signals with representative meanings. The experimental data are derived from a single lead ECG signal acquired by the Huaqi WATCH (model: WATCH 3) at a sampling rate of 1000 Hz. Fig. 4 and table 1 show the signal quality evaluation results of a segment containing noisy heartbeats; FIG. 5 and Table 2 show a signal quality assessment including ventricular premature beats; it can be seen that the power spectrum features estimated based on the AR model are more sensitive to noise and avoid discriminating ventricular premature beats as signals with poor signal quality.
TABLE 1 correlation coefficient of each heart beat segment with template in FIG. 4
TABLE 2 correlation coefficient of each heart beat segment with template in FIG. 5
Further, referring to fig. 2, fig. 2 shows an exemplary structural block diagram of an adaptive real-time cardiac signal quality assessment apparatus 200 according to an embodiment of the present application.
As shown in fig. 2, the apparatus includes:
an extracting unit 210, configured to extract features of power spectrum signal quality by using an AR model;
the matching unit 220 is used for inputting the extracted features into a K-means clustering algorithm and realizing the self-adaptive calculation of the signal quality matching template through the K-means clustering algorithm;
an evaluation unit 230 for evaluating the calculated quantification of the signal quality according to a similarity comparison method.
It should be understood that the units or modules recited in the apparatus 200 correspond to the various steps in the method described with reference to fig. 1. Thus, the operations and features described above for the method are equally applicable to the apparatus 200 and the units included therein, and are not described in detail here. The apparatus 200 may be implemented in a browser or other security applications of the electronic device in advance, or may be loaded into the browser or other security applications of the electronic device by downloading or the like. Corresponding elements in the apparatus 200 may cooperate with elements in the electronic device to implement aspects of embodiments of the present application.
Referring now to FIG. 3, a block diagram of a computer system 300 suitable for implementing a terminal device or server of the embodiments of the present application is shown.
As shown in fig. 3, the computer system 300 includes a Central Processing Unit (CPU)301 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)302 or a program loaded from a storage section 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data necessary for the operation of the system 300 are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
The following components are connected to the I/O interface 305: an input portion 306 including a keyboard, a mouse, and the like; an output section 307 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN card, a modem, or the like. The communication section 309 performs communication processing via a network such as the internet. A drive 310 is also connected to the I/O interface 305 as needed. A removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 310 as necessary, so that a computer program read out therefrom is mounted into the storage section 308 as necessary.
In particular, the process described above with reference to fig. 1 may be implemented as a computer software program, according to an embodiment of the present disclosure. For example, embodiments of the present disclosure include a method of adaptive real-time cardiac electrical signal quality assessment including a computer program tangibly embodied on a machine-readable medium, the computer program containing program code for performing the method of FIG. 1. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 309, and/or installed from the removable medium 311.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units or modules described in the embodiments of the present application may be implemented by software or hardware. The described units or modules may also be provided in a processor, and may be described as: a processor includes a first sub-region generating unit, a second sub-region generating unit, and a display region generating unit. Where the names of these units or modules do not in some cases constitute a definition of the unit or module itself, for example, the display area generating unit may also be described as a "unit for generating a display area of text from the first sub-area and the second sub-area".
As another aspect, the present application also provides a computer-readable storage medium, which may be the computer-readable storage medium included in the foregoing device in the foregoing embodiment; or it may be a separate computer readable storage medium not incorporated into the device. The computer readable storage medium stores one or more programs for use by one or more processors in performing the text generation method applied to the transparent window envelope described in the present application.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention as defined above. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (10)
1. A self-adaptive real-time electrocardiosignal quality assessment method is characterized by comprising the following steps:
extracting the characteristics of the power spectrum signal quality by utilizing an AR model;
inputting the extracted features into a K-means clustering algorithm, and realizing the self-adaptive calculation of a signal quality matching template through the K-means clustering algorithm;
the quantification of the calculated signal quality is evaluated according to a similarity comparison method.
2. The adaptive real-time cardiac signal quality assessment method according to claim 1, wherein before said extracting the features of the power spectrum signal quality using the AR model, the method further comprises:
detecting R peak of ECG by waveform detection algorithm, dividing heartbeat by using R peak as reference point, and dividing heartbeat by using R peak as reference pointnRepresents the nth R peak and represents R by the mth heart beat segmentnAnd Rn+2In (2), 4, 6, 8, and m n/2.
3. The adaptive real-time electrocardiosignal quality evaluation method according to claim 2, wherein the extracting the characteristics of the power spectrum signal quality by using the AR model comprises:
selecting an AR model with the order of 24-28;
for ECGmDetermining power spectrum characteristic PSD of heart beat signal through AR model pairmWherein, the ECGmRepresenting the mth heart beat segment.
4. The adaptive real-time electrocardiosignal quality assessment method according to claim 3, wherein the step of inputting the extracted features into a K-means clustering algorithm, and the adaptive calculation of the signal quality matching template is realized through the K-means clustering algorithm, and comprises the following steps:
power spectrum characteristic set PSD (phase-sensitive detector) by K-means clustering algorithm with K being 1mCalculating a clustering center;
and selecting preset c heart beats for averaging to serve as a similarity matching template.
5. The adaptive real-time cardiac signal quality assessment method according to claim 4, wherein said assessing the quantification of the calculated signal quality based on similarity comparison comprises:
measuring the similarity between each heart beat and the template heart beat by a Pearson correlation coefficient method;
and judging whether the quality of the heartbeat signal is accepted or not according to a set threshold value TR, and when the correlation coefficient of each heartbeat segment and the template is greater than the threshold value, considering the signal quality of the heartbeat to be acceptable, otherwise, judging the signal quality to be unacceptable.
6. An adaptive real-time electrocardiosignal quality assessment device is characterized by comprising:
the extraction unit is used for extracting the characteristics of the power spectrum signal quality by utilizing the AR model;
the matching unit is used for inputting the extracted features into a K-means clustering algorithm and realizing the self-adaptive calculation of the signal quality matching template through the K-means clustering algorithm;
and the evaluation unit is used for evaluating the quantification of the calculated signal quality according to a similarity comparison method.
7. The adaptive real-time cardiac signal quality assessment apparatus according to, wherein before said extracting the features of the power spectral signal quality using the AR model, the apparatus further comprises:
detecting R peak of ECG by waveform detection algorithm, dividing heartbeat by using R peak as reference point, and dividing heartbeat by using R peak as reference pointnRepresents the nth R peak and represents R by the mth heart beat segmentnAnd Rn+2In (2), 4, 6, 8, and m n/2.
8. The adaptive real-time cardiac signal quality assessment apparatus according to claim 7, wherein said extracting the characteristics of the power spectrum signal quality using the AR model comprises:
selecting an AR model with the order of 24-28;
for ECGmDetermining power spectrum characteristic PSD of heart beat signal through AR model pairmWherein, the ECGmRepresenting the mth heart beat segment.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1-5 when executing the program.
10. A computer-readable storage medium having stored thereon a computer program for:
the computer program, when executed by a processor, implementing the method as claimed in any one of claims 1-5.
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| CN115177267A (en) * | 2022-09-13 | 2022-10-14 | 合肥心之声健康科技有限公司 | Heart beat artifact identification method and system |
| CN115670397A (en) * | 2022-11-17 | 2023-02-03 | 北京中科心研科技有限公司 | PPG artifact identification method and device, storage medium and electronic equipment |
| CN116992219A (en) * | 2023-09-07 | 2023-11-03 | 博睿康科技（常州）股份有限公司 | Signal quality characterization unit and noise source positioning method based on noise detection index |
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