WO2022110524A1 - Procédé et dispositif de regroupement de données de battements cardiaques d'électrocardiogrammes, dispositif électronique et support - Google Patents

Procédé et dispositif de regroupement de données de battements cardiaques d'électrocardiogrammes, dispositif électronique et support Download PDF

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WO2022110524A1
WO2022110524A1 PCT/CN2021/072841 CN2021072841W WO2022110524A1 WO 2022110524 A1 WO2022110524 A1 WO 2022110524A1 CN 2021072841 W CN2021072841 W CN 2021072841W WO 2022110524 A1 WO2022110524 A1 WO 2022110524A1
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template
data
processed
heartbeat
heartbeat data
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PCT/CN2021/072841
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Chinese (zh)
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刘盛捷
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深圳邦健生物医疗设备股份有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7246—Details of waveform analysis using correlation, e.g. template matching or determination of similarity
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques

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  • the present invention relates to the technical field of electrocardiogram data processing, in particular to a method, device, electronic device and medium for clustering electrocardiogram heartbeat data.
  • the heartbeat clustering (or template matching) technology can divide the ECG signal into several templates according to the different morphological characteristics of the QRS wave, and each template stores the heartbeat index with the similar QRS wave shape.
  • the work is concentrated on several templates, which greatly saves the doctor's inspection time and improves the diagnosis efficiency.
  • the ECG heartbeat data clustering can use the feature parameter method. Specifically, after using signal transformation methods such as fast Fourier transform, wavelet transform, and Hilbert transform to convert the ECG signal from the time domain into a transform domain with features that are easier to distinguish, the classical clustering method is used to analyze the ECG signal. Automatic template matching. However, using the signal transformation method will significantly increase the amount of calculation, and many classical clustering methods need to set the number of templates to be matched in advance. When this method is used to perform template matching on cases with low signal-to-noise ratio and multiple sources of premature ventricular arrhythmia, different heartbeats will also be mixed in the same template, the ECG heartbeat data clustering is not accurate enough, and the processing efficiency is not high.
  • signal transformation methods such as fast Fourier transform, wavelet transform, and Hilbert transform
  • the present application provides an electrocardiogram heartbeat data clustering method, apparatus, electronic device and medium.
  • a method for clustering ECG heartbeat data including:
  • a new data template is created according to the heartbeat data to be processed, and the new data template corresponds to the new template identifier; determining the target data template that matches the heartbeat data to be processed;
  • the template matching result includes a template identifier of the target data template
  • a sorting operation is performed on the data template, and the sorting operation includes a template merging operation, a template sorting operation and/or a template deleting operation, Get the template finishing result;
  • the clustering result of the heartbeat data to be processed is obtained.
  • an ECG heartbeat data clustering device including a template matching module, a template sorting module and a clustering module, wherein:
  • the template matching module is used for:
  • a new data template is created according to the heartbeat data to be processed, and the new data template corresponds to the new template identifier; determining the target data template that matches the heartbeat data to be processed;
  • the template matching result includes a template identifier of the target data template
  • the template sorting module is configured to perform sorting operations on the data templates whenever the number of processed heartbeat data to be processed reaches a preset number threshold, and the sorting operations include template merging operations, template sorting operations operation and/or template deletion to obtain template sorting results;
  • the clustering module is configured to obtain the clustering result of the heartbeat data to be processed according to the template matching result and the template sorting result.
  • an electronic device comprising a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to perform the first aspect and any of the above. Steps for a possible implementation.
  • a computer storage medium stores one or more instructions, the one or more instructions are adapted to be loaded and executed by a processor as described in the first aspect and any one thereof Steps of possible implementations.
  • multiple heartbeats with similar shapes can be aggregated into one template through template matching, and the templates can be integrated in time, so that real-time clustering can be realized without complex signal transformation, and the data processing efficiency is higher.
  • FIG. 1 is a schematic flowchart of a method for clustering electrocardiogram heartbeat data according to an embodiment of the present application
  • FIG. 2 is a schematic flowchart of a signal quality evaluation process provided by an embodiment of the present application
  • FIG. 3 is a schematic flowchart of a template matching process provided by an embodiment of the present application.
  • FIG. 4 is a schematic diagram of a template matching result after heartbeat offset search provided by an embodiment of the present application.
  • FIG. 5 is a schematic diagram of a template matching result after template merging when there is a template offset provided by an embodiment of the present application
  • FIG. 6 is a schematic diagram of a template matching result after template deletion when the number of templates is too large, according to an embodiment of the present application
  • FIG. 7 is a schematic structural diagram of an apparatus for clustering electrocardiogram and heartbeat data according to an embodiment of the present application.
  • FIG. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
  • the ECG signals involved in the embodiments of the present application can be obtained by converting the electrical signals received by the ECG collection box through the electrode pads connected to various parts of the human body through signal conversion.
  • a typical ECG signal consists of P wave, QRS complex and T wave, which reflects the state of human heart activity.
  • doctors can learn many aspects of a patient's heart disease.
  • the Holter signal involved in the embodiments of the present application is a long-term ECG signal, generally 24 hours or more. Because the onset time and triggering conditions of many heart diseases are relatively secret, the general static ECG cannot find its existence within a dozen seconds, which requires the acquisition of long-term Holter signals to continuously record the patient's cardiac electrical signals. However, the recording time span of the Holter signal is large, and the Holter signal with a duration of 24 hours has about 100,000 heartbeats, which makes the diagnosis method of the doctor's visual inspection of the patient's condition inefficient. Signal processing techniques are developed accordingly.
  • the heartbeat clustering (or template matching) technology can divide the ECG signal into several templates according to the different morphological characteristics of the QRS wave, and each template stores the heartbeat index with the similar QRS wave shape.
  • the work is concentrated on several templates, which greatly saves the doctor's inspection time and improves the diagnosis efficiency.
  • FIG. 1 is a schematic flowchart of a method for clustering electrocardiogram heartbeat data according to an embodiment of the present application.
  • the method may include:
  • the execution subject of the embodiment of the present application may be an electrocardiogram and heartbeat data clustering device, which may be an electronic device.
  • Other portable devices such as mobile phones, laptops, or tablet computers with sensitive surfaces (eg, touch screen displays and/or touch pads).
  • the above-described devices are not portable communication devices, but rather desktop computers with touch-sensitive surfaces (eg, touch screen displays and/or touch pads).
  • the above-mentioned method for clustering electrocardiogram and heartbeat data may be supported and run by a server in response to an operation instruction of a user.
  • the above-mentioned heartbeat data to be processed may be heartbeat signal data clipped from the collected electrocardiogram data.
  • the method before the above step 101, the method further includes:
  • the above-mentioned acquisition of the heartbeat data to be processed includes:
  • the to-be-processed heartbeat data with a preset signal length is acquired from the heartbeat segment.
  • the electrocardiogram data in this embodiment of the present application may be an electrocardiogram signal collected by an electrocardiogram acquisition device within a period of time.
  • the position of the heartbeat can be determined according to the characteristics of the electrocardiogram, and then the signal can be divided into multiple equal-length segments reflecting the characteristics of the heartbeat. .
  • the above-mentioned electrocardiogram data is divided to obtain a plurality of heart beat segments, including:
  • the ECG data is divided according to the R wave position information obtained by detection, and a plurality of heartbeat segments of equal length centered on the R wave position are obtained.
  • a complete ECG signal is usually composed of P wave, QRS complex and T wave, in which the QRS complex is the main part of the ECG, and the R wave is dominant in the QRS complex, so the marked position of the heartbeat can be marked with R. Wave prevail. Equal length ECG segments centered on the R-wave position can be acquired for template matching.
  • the method further includes:
  • Band-pass filtering is performed on the above-mentioned two-lead electrocardiogram data to obtain the above-mentioned electrocardiogram data.
  • multi-lead electrocardiogram data collected by Holter may be used for processing.
  • Holter is an ECG signal recording system, which can provide a powerful information aid for doctors to comprehensively analyze the patient's heart condition by adopting portable ECG signal acquisition and continuous measurement of the patient's cardiac activity for up to 24 hours.
  • the Holter data can be read first, and the data includes the 24-hour multi-lead Holter data segment Signals ⁇ R N*M collected by the Holter device, where N represents the number of signal leads, and M represents the signal length; it can also include related parameters, such as:
  • the R wave position (RPos, in sampling point or time) obtained by the detection of the QRS wave
  • RR interval (representing the distance between the current heartbeat and the adjacent previous heartbeat, in sampling points or time units);
  • QRS wave width width, in sampling points or time
  • Primary analysis lead number and secondary analysis lead number are Primary analysis lead number and secondary analysis lead number.
  • the above-mentioned main analysis lead number and sub-analysis lead number are the two dominant lead data numbers determined by analysis in the above-mentioned multi-lead Holter data.
  • signal preprocessing can be performed: construct dual-lead Holter data according to the above-mentioned main analysis lead number and the above-mentioned secondary analysis lead number, and then filter out low-frequency baseline drift interference and high-frequency noise through the above-mentioned band-pass filtering.
  • the specific can be as follows: according to the R wave position RPos, the Holter data is divided into ECG data fragments X ⁇ R N*2*W with length W corresponding to the R wave position in the segment center, where N represents the divided The number of ECG data, 2 means double lead, W means single lead data length.
  • the above-mentioned heartbeat data to be processed may include a signal of a preset signal length cut out from a two-lead Holter data segment, which may be used as a recent signal RecentSignals ⁇ R 2*L , where L represents a preset signal length (recent signal length).
  • the above step 101 may include:
  • the minimum error among the plurality of relative errors is obtained, and if the minimum error is smaller than a preset error threshold, it is determined that the target data template corresponding to the minimum error matches the heartbeat data to be processed.
  • each data template may correspond to a representative waveform, which may be a waveform mean value of template members or determined by other means, which is not limited in this embodiment of the present application.
  • the heartbeat data to be processed can be classified by calculating the relative error to measure the similarity between the waveform of the heartbeat data to be processed and the representative waveform of each data template.
  • the heartbeat offset search range can also be selected and set according to requirements, so as to reduce the data processing amount of template search and matching, and improve the efficiency.
  • the error calculation formula can be as follows:
  • abs( ⁇ ) means taking the absolute value
  • ppv( ⁇ ) means taking the peak value , that is, the maximum value-minimum value.
  • the obtained relative errors are searched for the minimum error RE min , and the optimal offset Shift opt corresponding to the minimum error RE min and the matching template identifier MatchId (ie, the template identifier of the template matching the data waveform to be processed).
  • An offset search mechanism is introduced in the embodiment of the present application, which can avoid the problem of excessive templates caused by heartbeat offset and template offset, and reduce template information storage overhead.
  • the above-mentioned method before the above-mentioned comparison of the above-mentioned heartbeat data to be processed with a plurality of data templates in the template library, the above-mentioned method further includes:
  • the signal quality parameter of the above-mentioned heartbeat data to be processed is determined
  • the above-mentioned establishment of a new data template according to the above-mentioned heartbeat data to be processed includes:
  • the signal quality parameter of the above-mentioned heartbeat data to be processed is the first quality threshold
  • a new data template is established according to the above-mentioned heartbeat data to be processed.
  • the signal quality of the detected heartbeat data to be processed can be evaluated. Please refer to a schematic diagram of a signal quality evaluation process shown in FIG. 2 . As shown in FIG. 2 , the signal quality evaluation process includes three steps of recording recent signals, recording recent heartbeat information, and evaluating signal quality.
  • a signal-to-noise ratio (SIGNAL-NOISE RATIO, SNR) may be used to measure the signal quality.
  • the recent signal RecentSignals ⁇ R2*L can be cropped from the two-lead Holter data segment, where L represents the length of the recent signal; and the recent heart beat information RecentBeatInfo can also be updated, including adding the newly detected R wave position , update the position of the historical heartbeat in the recent signal, and remove the historical heartbeat that exceeds the length L of the recent signal, etc.
  • Vs represents the standard deviation of the QRS waveform amplitude of the recent heartbeat
  • Vn represents the standard deviation of the signal amplitude in the recent signal excluding the area of the QRS waveform of the recent heartbeat.
  • the above-mentioned signal quality parameters can be obtained by setting different rules according to requirements and calculating the signal-to-noise ratio, which is not limited in this embodiment of the present application.
  • the template matching is successful, update the matching template information with the newly detected heartbeat information, and output the matching template identifier; if RE min ⁇ RE Thre1 . If the template matching fails, the signal quality can be further judged.
  • the information of the target data template can be updated with the information of the heartbeat data to be processed, such as the newly detected R wave position, etc.
  • the heartbeat segment of the heartbeat data to be processed is classified into a matching template, becomes a template member of the template, and can output the template identifier of the target data template.
  • the above-mentioned newly-added data template is determined as: The above-mentioned target data template matching the above-mentioned heartbeat data to be processed.
  • the target data template that matches the heartbeat data can be used to create a new data template based on the heartbeat data to be processed.
  • the new data template can use the QRS waveform of the heartbeat data to be processed as the representative waveform of the new data template, and save The RR interval and width values of the heartbeat data to be processed and output the newly added template identifier.
  • the heartbeat data to be processed is used as the newly detected heartbeat, and the specific process may include:
  • Heartbeat match If the matching is successful, update the matching template information with the newly detected heartbeat information, and output the matching template number; if the matching fails. Further judge the signal quality;
  • a new template is created with the newly detected heartbeat information, and the number of the new template is output.
  • the template matching fails, and the invalid template number represented by a negative number can be output.
  • the quantity of the above-mentioned heartbeat data to be processed reaches a preset quantity threshold, perform a sorting operation on the above-mentioned data template, and the above-mentioned sorting operation includes a template merging operation, a template sorting operation, and/or a template deletion operation, and obtains: Template collation results.
  • the data template may be sorted out while the clustering is performed.
  • the above preset number threshold is 100, that is, whenever 100 pieces of data to be processed are newly detected, the template information in the existing template library needs to be sorted out.
  • the above-mentioned performing the sorting operation on the above-mentioned data template includes:
  • the function of template merging is mainly to merge similar templates in the template library to reduce the number of templates.
  • the specific operations can be as follows:
  • the reference template identifier ReferId is set as the first parameter in the unsorted template identifier list, and the processing template identifier TargetIdx to be matched is the remaining parameters in the unsorted template identifier list.
  • the relative error RE(s,t) of the reference template and each processing template can be calculated by using the same error calculation formula above, s ⁇ ShiftLtm2, t ⁇ TargetIdx; search for the processing template satisfying RE(s,t) ⁇ RE Thre2 Identifies the list TargetIdx1.
  • These processing templates are similar to the reference template, and the information of the reference template will be updated with the information of these processing templates and the corresponding offsets.
  • Steps (2)-(4) are repeated until the TargetIdx is empty, and the template merging ends.
  • the above-mentioned performing the sorting operation on the above-mentioned data template includes:
  • the multiple templates are sorted according to the descending order of the number of template members of each template in the multiple templates.
  • the above-mentioned sorting operation includes a template deletion operation
  • the above-mentioned sorting operation for the above-mentioned data template includes:
  • the number of templates can be managed and controlled in real time.
  • the above-mentioned first number threshold T1 can be set, and when the template number M exceeds the first number threshold T1, the first round of template deletion process can be started, and the template with a larger age value can be deleted by comparing and evaluating the age value of the template and the age parameter. .
  • the first round of template deletion process may include:
  • T is the number of templates
  • the corresponding age parameters can be calculated according to different age values, which can be calculated by the following formula:
  • the first duration parameter includes a 1 and b 1 , which are fixed duration and variable duration respectively.
  • the calculation method of the above-mentioned age parameter can be set as required, such as modifying the above-mentioned first duration parameter, which is not limited here;
  • DeleteNum number of templates to be deleted (DeleteNum)
  • performing the sorting operation on the above-mentioned data template further includes:
  • the template to be searched is obtained from the above-mentioned template, and the number of template members of the above-mentioned template to be searched is less than the preset number of members threshold;
  • the above-mentioned second quantity threshold T2 is greater than the above-mentioned th a quantity threshold T1;
  • Obtain the template whose age value is greater than the age reference value determine the templates with the largest age value in the top N as templates to be deleted from the templates whose age value is greater than the age reference value, and delete the template to be deleted from the template set.
  • a second round of template deletion process may be started.
  • the template with a smaller number of template members may be deleted first.
  • the second round of template deletion process may include:
  • the age reference value calculated in this stage is a fixed age parameter, which can be as follows:
  • the second duration parameter includes a 2 and b 2 , which are fixed duration and variable duration respectively.
  • the calculation method of the above-mentioned age reference value can be set as required, such as modifying the above-mentioned second duration parameter, which is not limited here.
  • CandidateNum is the number of templates contained in CandidateIdx2;
  • DeleteNum number of templates to be deleted (DeleteNum)
  • DeleteNum number of templates-T1
  • template matching can be used to output template matching results, and at the same time, it can support the sorting of template libraries, and template merging can be introduced to merge similar templates;
  • the age value adaptively deletes templates with large age and small capacity, so as to avoid the problem that the number of templates increases too quickly in the case of large interference, and has anti-interference ability;
  • the heartbeat data can be obtained in time The real-time clustering results of the template are more standardized.
  • the embodiments of the present application can be used to remotely monitor the real-time heartbeat clustering of a patient's heart activity state.
  • the patient wears a mobile ECG collection box equipped with a remote data transmission function, and the collection box collects the patient's ECG signal and uploads it to the server.
  • the data is received and parsed into 12-lead Holter real-time data.
  • new heartbeat data information is obtained, and then the 12-lead Holter data and heartbeat data information are transmitted to the ECG signal real-time clustering module for heartbeat clustering.
  • the real-time heartbeat clustering algorithm After the real-time heartbeat clustering algorithm reads the data, it constructs a dual-lead Holter signal from the 12-lead Holter signal according to the main analysis lead number and the secondary analysis lead number, and then completes the signal preprocessing and ECG segment division. The former is obtained. Filter the Holter signal, which obtains the QRS waveform data of the newly detected heartbeat.
  • the algorithm will combine the saved recent signals and the corresponding recent heartbeat information to calculate the signal quality parameters.
  • FIG. 5 shows the template member waveforms after the template merge.
  • the three heartbeat groups in the figure represent the reference template member waveforms without offset correction and the similar target templates numbered 5 and 12 (the above-mentioned similar processing templates). ) member waveform.
  • the methods used in ECG template matching can be roughly divided into two categories, one is the template matching method, and the other is the feature parameter method.
  • the template matching method is to directly match the newly acquired ECG signal with the existing template, and then determine the similarity between the two by calculating the correlation coefficient. If the correlation coefficient is greater than a given threshold, the ECG signal is considered to be similar to a specific template, otherwise a new template is established.
  • the template matching method requires a prior determination of a known template library for comparison, and may not be adaptable when encountering rare special cases.
  • the characteristic parameter method will perform signal transformation on the ECG signal in advance before template matching, usually using wavelet transformation. The transformed signal has a good degree of discrimination in certain frequency bands or regions, so it can be used for template matching.
  • the signal transformation method adopted by the characteristic parameter method has a large amount of calculation, so the algorithm takes a long time.
  • there are some methods but they are only applicable to offline clustering scenarios and cannot be applied to real-time clustering scenarios.
  • the ECG heartbeat data clustering method in the embodiment of the present application has the following advantages:
  • the template deletion mechanism is introduced.
  • the templates with large age and small capacity will be deleted adaptively according to the age of the template, so as to avoid the problem that the number of templates increases too quickly in the case of large interference. , with anti-interference ability.
  • an embodiment of the present application further discloses a device for clustering ECG heartbeat data.
  • the electrocardiogram and heartbeat data clustering device 700 includes a template matching module 710, a template sorting module 720 and a clustering module 730, wherein:
  • the above template matching module 710 is used for:
  • a new data template is established according to the above-mentioned heartbeat data to be processed, and the above-mentioned newly-added data template corresponds to the newly-added template identifier; the above-mentioned newly-added data template is determined to be the same as the above-mentioned The above-mentioned target data template matched by the heartbeat data to be processed;
  • the above-mentioned template matching result including the template identifier of the above-mentioned target data template
  • the above-mentioned template sorting module 720 is configured to perform sorting operations on the above-mentioned data templates whenever the number of the above-mentioned heartbeat data to be processed reaches a preset number threshold, and the above-mentioned sorting operations include a template merging operation, a template sorting operation and/ Or template delete operation to obtain template sorting results;
  • the clustering module 730 is configured to obtain the clustering result of the heartbeat data to be processed according to the template matching result and the template sorting result.
  • each step involved in the method shown in FIG. 1 may be performed by each module in the apparatus 700 for clustering electrocardiogram and heartbeat data shown in FIG. 7 , which will not be repeated here.
  • the electrocardiogram heartbeat data clustering device 700 in this embodiment of the present application can acquire heartbeat data to be processed, and compare the heartbeat data to be processed with multiple data templates; matching target data template, use the information of the heartbeat data to be processed to update the information of the target data template; if there is no target data template matching the heartbeat data to be processed, according to the heartbeat data to be processed establishing a new data template, where the new data template corresponds to a new template identifier; determining the new data template as the target data template matching the to-be-processed heartbeat data; outputting the to-be-processed heartbeat
  • the template matching result of the data, the template matching result includes the template identifier of the target data template; whenever the number of the processed heartbeat data to be processed reaches a preset number threshold, the data template is sorted operation, the sorting operation includes a template merging operation, a template sorting operation and/or a template deletion operation, and a template sorting result is obtained; according to the template matching result and the template
  • the embodiments of the present application further provide an electronic device.
  • the electronic device 800 at least includes a processor 801 , an input device 802 , an output device 803 and a computer storage medium 804 .
  • the processor 801, the input device 802, the output device 803, and the computer storage medium 804 in the electronic device 800 may be connected through a bus or other means.
  • the computer storage medium 804 may be stored in the memory of the electronic device 800 .
  • the computer storage medium 804 is used for storing computer programs, and the computer program includes program instructions.
  • the processor 801 is used for executing the program instructions stored in the computer storage medium 804 .
  • the processor 801 (or called CPU (Central Processing Unit, central processing unit)) is the computing core and the control core of the electronic device 800, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to thereby Implement the corresponding method flow or corresponding function; in an embodiment, the processor 801 in the embodiment of the present application may be used to perform a series of processing, including some or all of the methods in the embodiment shown in FIG. 1 , and so on.
  • CPU Central Processing Unit, central processing unit
  • Embodiments of the present application further provide a computer storage medium (Memory), where the above-mentioned computer storage medium is a memory device in the electronic device 800 for storing programs and data.
  • the computer storage medium here may include both the built-in storage medium in the electronic device 800 , and certainly also the extended storage medium supported by the electronic device 800 .
  • the computer storage medium provides storage space in which the operating system of the electronic device 800 is stored.
  • one or more instructions suitable for being loaded and executed by the processor 801 are also stored in the storage space, and these instructions may be one or more computer programs (including program codes).
  • the computer storage medium here can be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory; optionally, at least one storage medium located far away from the aforementioned processor can also be used. computer storage media.
  • one or more instructions stored in the computer storage medium can be loaded and executed by the processor 801 to implement the corresponding steps in the foregoing embodiment; in specific implementation, one or more instructions in the computer storage medium can be Any steps of the method shown in FIG. 1 are loaded and executed by the processor 801, and are not repeated here.
  • the disclosed system, apparatus and method may be implemented in other manners.
  • the division of the module is only for one logical function division, and there may be other division methods in actual implementation, for example, multiple modules or components may be combined or integrated into another system, or some features may be ignored, or not implement.
  • the shown or discussed mutual coupling, or direct coupling, or communication connection may be through some interfaces, indirect coupling or communication connection of devices or modules, and may be in electrical, mechanical or other forms.
  • Modules described as separate components may or may not be physically separated, and components shown as modules may or may not be physical modules, that is, may be located in one place, or may be distributed to multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
  • the above-mentioned embodiments it may be implemented in whole or in part by software, hardware, firmware or any combination thereof.
  • software it can be implemented in whole or in part in the form of a computer program product.
  • the computer program product includes one or more computer instructions.
  • the computer program instructions When the computer program instructions are loaded and executed on a computer, the procedures or functions according to the embodiments of the present application are generated in whole or in part.
  • the computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable device.
  • the computer instructions may be stored in or transmitted over a computer-readable storage medium.
  • the computer instructions can be sent from one website site, computer, server, or data center to another by wire (eg, coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (eg, infrared, wireless, microwave, etc.)
  • wire e.g. coaxial cable, fiber optic, digital subscriber line (DSL)
  • wireless e.g., infrared, wireless, microwave, etc.
  • the computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that includes an integration of one or more available media.
  • the available media may be read-only memory (ROM), or random access memory (RAM), or magnetic media, such as floppy disks, hard disks, magnetic tapes, magnetic disks, or optical media, such as, A digital versatile disc (DVD), or a semiconductor medium, for example, a solid state disk (SSD) and the like.
  • ROM read-only memory
  • RAM random access memory
  • magnetic media such as floppy disks, hard disks, magnetic tapes, magnetic disks, or optical media, such as, A digital versatile disc (DVD), or a semiconductor medium, for example, a solid state disk (SSD) and the like.

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Abstract

Sont divulgués un procédé et un dispositif de regroupement de données de battements cardiaques d'électrocardiogrammes, un dispositif électronique et un support. Le procédé consiste : à comparer les données de battements cardiaques à traiter avec de multiples modèles de données ; si un modèle de données cible correspondant est présent, à utiliser les informations desdites données de battements cardiaques pour mettre à jour les informations du modèle de données cible ; si aucun modèle n'est présent, à créer, sur la base desdites données de battements cardiaques, un nouveau modèle de données correspondant à un nouvel identifiant de modèle ; à déterminer le nouveau modèle de données comme étant le modèle de données cible correspondant auxdites données de battements cardiaques ; à sortir le résultat de correspondance de modèles desdites données de battements cardiaques, le résultat de correspondance de modèles comprenant l'identifiant de modèle du modèle de données cible ; dans la mesure où le volume desdites données de battements cardiaques atteint un seuil de volume prédéfini, à exécuter une opération d'organisation par rapport aux modèles de données, comprenant une opération de fusion de modèles, une opération de tri de modèles, et/ou une opération de suppression de modèles, afin d'acquérir un résultat d'organisation de modèles ; et à acquérir un résultat de regroupement desdites données de battements cardiaques sur la base du résultat de correspondance de modèles et du résultat d'organisation de modèles.
PCT/CN2021/072841 2020-11-30 2021-01-20 Procédé et dispositif de regroupement de données de battements cardiaques d'électrocardiogrammes, dispositif électronique et support WO2022110524A1 (fr)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230017546A1 (en) * 2021-07-19 2023-01-19 GE Precision Healthcare LLC Methods and systems for real-time cycle length determination in electrocardiogram signals
CN115778402A (zh) * 2022-12-02 2023-03-14 深圳华清心仪医疗电子有限公司 一种动态心电信号的伪差识别方法及系统

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112528783B (zh) * 2020-11-30 2024-04-16 深圳邦健生物医疗设备股份有限公司 心电图心搏数据聚类方法、装置、电子设备和介质
CN113191249B (zh) * 2021-04-28 2022-05-24 深圳邦健生物医疗设备股份有限公司 心电信号的模板匹配方法、装置、设备和介质
CN113180687B (zh) * 2021-04-29 2024-02-09 深圳邦健生物医疗设备股份有限公司 多导联动态心搏实时分类方法、装置、设备及存储介质
CN113643804B (zh) * 2021-07-21 2024-09-13 深圳市千帆电子有限公司 心功能检测数据分析方法、装置、电子设备和介质

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107358196A (zh) * 2017-07-12 2017-11-17 北京卫嘉高科信息技术有限公司 一种心搏类型的分类方法、装置及心电仪
CN107456227A (zh) * 2017-08-16 2017-12-12 北京蓬阳丰业医疗设备有限公司 全导联心电图聚类模板系统及方法
CN110693483A (zh) * 2019-09-02 2020-01-17 乐普智芯(天津)医疗器械有限公司 一种动态心电图自动分析的方法
CN111053551A (zh) * 2019-12-27 2020-04-24 深圳邦健生物医疗设备股份有限公司 Rr间期心电数据分布显示方法、装置、计算机设备和介质
CN111150387A (zh) * 2020-01-15 2020-05-15 深圳市邦健科技有限公司 心电图模板匹配方法、装置、计算机设备及存储介质
CN112528783A (zh) * 2020-11-30 2021-03-19 深圳邦健生物医疗设备股份有限公司 心电图心搏数据聚类方法、装置、电子设备和介质

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102835954B (zh) * 2012-09-07 2015-03-11 深圳邦健生物医疗设备股份有限公司 一种心拍波形模板生成方法及模块
CN104055522A (zh) * 2014-07-01 2014-09-24 清华大学深圳研究生院 一种心律失常情况下心电信号身份识别方法
CN109480825B (zh) * 2018-12-13 2021-08-06 武汉中旗生物医疗电子有限公司 心电数据的处理方法及装置
CN109620213B (zh) * 2019-02-25 2020-03-27 山东大学 一种基于多尺度差分特征的心电识别方法及装置
CN110141218B (zh) * 2019-06-17 2022-02-18 东软集团股份有限公司 一种心电信号分类方法、装置及程序产品、存储介质
CN110909757B (zh) * 2019-08-20 2023-07-14 北京北科慧识科技股份有限公司 生物识别系统模板的选取和更新方法
CN111616696B (zh) * 2020-05-20 2024-07-26 联想(北京)有限公司 一种心电信号检测方法、装置及存储介质

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107358196A (zh) * 2017-07-12 2017-11-17 北京卫嘉高科信息技术有限公司 一种心搏类型的分类方法、装置及心电仪
CN107456227A (zh) * 2017-08-16 2017-12-12 北京蓬阳丰业医疗设备有限公司 全导联心电图聚类模板系统及方法
CN110693483A (zh) * 2019-09-02 2020-01-17 乐普智芯(天津)医疗器械有限公司 一种动态心电图自动分析的方法
CN111053551A (zh) * 2019-12-27 2020-04-24 深圳邦健生物医疗设备股份有限公司 Rr间期心电数据分布显示方法、装置、计算机设备和介质
CN111150387A (zh) * 2020-01-15 2020-05-15 深圳市邦健科技有限公司 心电图模板匹配方法、装置、计算机设备及存储介质
CN112528783A (zh) * 2020-11-30 2021-03-19 深圳邦健生物医疗设备股份有限公司 心电图心搏数据聚类方法、装置、电子设备和介质

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230017546A1 (en) * 2021-07-19 2023-01-19 GE Precision Healthcare LLC Methods and systems for real-time cycle length determination in electrocardiogram signals
CN115778402A (zh) * 2022-12-02 2023-03-14 深圳华清心仪医疗电子有限公司 一种动态心电信号的伪差识别方法及系统

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