Dynamic electrocardiogram analysis method and device and terminal equipment

Abstract

The invention belongs to the technical field of data analysis, and provides a dynamic electrocardiogram analysis method and device and terminal device.The method comprises the steps that firstly, electrocardiogram data of a to-be-detected object is obtained; generating change trend data based on the historical electrocardio data and the current electrocardio data; and analyzing the change trend data to generate event description information. According to the method, the multi-day electrocardiogram data of the to-be-detected object can be independently analyzed day by day, and the electrocardiogram analysis result of each day can be obtained; the analysis result of the whole electrocardiogram data is further analyzed, the statistical result of the development and change process of the related illness state of the to-be-detected object can be obtained, the change process of the heart state of the to-be-detected object can be visually displayed, and the electrocardiogram data analysis efficiency and accuracy are improved.

Description

Technical Field

[0001] The present application belongs to the field of data analysis technology, and in particular, relates to a dynamic electrocardiogram analysis method, device and terminal equipment. Background Art

[0002] Cardiovascular disease is the leading disease threatening human health. Electrocardiogram (ECG) is a comprehensive reflection of the potential changes during the heart's electrical activity on the body surface. Each part of the ECG represents a specific physiological significance. With the development of dynamic ECG recording, long-term and multi-time period ECG detection is increasingly used to evaluate the development, treatment, rehabilitation, and drug treatment effects of heart disease.

[0003] However, the current mainstream dynamic ECG analysis software only provides independent ECG analysis for multiple time periods, so it is necessary to manually compare the analysis results repeatedly to obtain its development and change patterns over time, which reduces the efficiency of ECG data analysis. Summary of the invention

[0004] The embodiment of the present application provides a dynamic electrocardiogram analysis method, which aims to solve the technical problem that the efficiency of electrocardiogram data analysis is low when the subject to be tested wears a conventional dynamic electrocardiogram analysis device.

[0005] In a first aspect, an embodiment of the present application provides a method for analyzing a dynamic electrocardiogram, the method comprising:

[0006] Acquire historical ECG data and current ECG data of the subject to be tested, wherein the current ECG data is the ECG data within a preset time period, and the historical ECG data is the ECG data of the subject to be tested corresponding to each historical time period before the preset time period;

[0007] Generate change trend data based on each historical ECG data and the current ECG data;

[0008] The change trend data is analyzed to generate event description information.

[0009] In one embodiment, the type of the electrocardiogram data includes first electrocardiogram data, the first electrocardiogram data includes an electrocardiogram band corresponding to an ST segment and / or a T wave, and the method further includes:

[0010] Acquire the first electrocardiogram data of the subject to be measured within the preset time period;

[0011] Combining the first electrocardiogram data within the preset time period and the first electrocardiogram data within each historical time period to generate a trend graph of changes in the first electrocardiogram data;

[0012] Perform feature analysis on the first electrocardiogram data change trend graph to obtain a first myocardial event, and generate first description information corresponding to the first myocardial event.

[0013] In one embodiment, the step of obtaining the first electrocardiogram data of the subject to be measured within the preset time period includes:

[0014] Calculating the first electrocardiogram data of each heartbeat of the subject to be tested within a preset time period;

[0015] The step of combining the first electrocardiogram data in the preset time period with the first electrocardiogram data in each historical unit time to generate a trend graph of changes in the first electrocardiogram data includes:

[0016] Adding and averaging the first electrocardiogram data of each heartbeat per minute within the preset time period to obtain a mean value of the first electrocardiogram data per minute; generating a current first electrocardiogram data change trend graph based on each first electrocardiogram data mean value;

[0017] Combining the current first ECG data trend graph with the historical first ECG data trend graphs in each historical unit time period to obtain the first ECG data change trend graph;

[0018] The step of performing feature analysis on the first electrocardiogram data change trend graph to obtain a first myocardial event and generating first description information corresponding to the first myocardial event includes:

[0019] Based on the first ECG data change trend graph, first abnormal data is obtained, and the first abnormal data at least includes a maximum value, an average value, and a duration related to the lead combination data; a first myocardial event is obtained according to the first abnormal data, and first description information corresponding to the first myocardial event is generated.

[0020] In one embodiment, the acquiring first abnormal data based on the first electrocardiogram data change trend graph includes:

[0021] Acquire a plurality of lead data based on the first electrocardiogram data change trend diagram, and select lead combination data that meets a preset abnormal condition from the plurality of lead data;

[0022] The maximum value, average value and duration of the lead combination data are calculated.

[0023] In one embodiment, the method further comprises:

[0024] Responding to the user's switching operation on the type of ECG data to determine the type of current ECG data;

[0025] Display the target change trend data corresponding to the type of the current ECG data.

[0026] In one embodiment, after displaying the target change trend data corresponding to the type of the current electrocardiogram data, the method further includes:

[0027] In response to the user's preset triggering operation on the target change trend data, the triggered target event description information is displayed.

[0028] In one embodiment, when the type of the current ECG data is the first ECG data, the step of displaying the first ECG data change trend graph includes:

[0029] The amplitude change of the first electrocardiogram data along the first time axis is displayed on the current interface.

[0030] In one embodiment, when the type of the current ECG data is the first ECG data, the step of displaying the first ECG data change trend graph includes:

[0031] Acquire the lead combination data generated when the first electrocardiogram data changes in all time periods;

[0032] Myocardial infarction position information and a myocardial infarction blood vessel image corresponding to the lead combination data are determined according to the lead combination data.

[0033] In one embodiment, the method further comprises:

[0034] In response to a user selecting a designated continuous time period from among all the time periods;

[0035] Acquiring a heart model of the subject to be tested;

[0036] Based on the myocardial infarction position information and myocardial infarction vascular images corresponding to the lead combination data within the specified continuous time period,

[0037] The change process of the myocardial infarction position image and / or the myocardial infarction blood vessel image within the specified continuous time period is dynamically displayed in the heart model.

[0038] In a second aspect, an embodiment of the present application provides a dynamic electrocardiogram analysis device, comprising:

[0039] An acquisition module, used to acquire historical ECG data and current ECG data of the subject to be tested, wherein the current ECG data is the ECG data within a preset time period, and the historical ECG data is the ECG data corresponding to each historical time period before the preset time period of the subject to be tested;

[0040] A generating module, used for generating change trend data based on each historical ECG data and the current ECG data;

[0041] The analysis module is used to analyze the change trend data and generate event description information.

[0042] In a third aspect, the present invention further proposes a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect above when executing the computer program.

[0043] The beneficial effects of the present application are that: the ECG data of the subject for multiple days can be analyzed separately by day to obtain the ECG analysis results for each day; then the analysis results of the overall ECG data are further analyzed to obtain statistical results of the development and change process of the relevant disease condition of the subject to be tested, and the change process of the heart state of the subject to be tested can be visualized, thereby improving user experience and enhancing the efficiency and accuracy of ECG data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 This is a schematic flow chart of a first embodiment of a dynamic electrocardiogram analysis method provided by the present application;

[0046] Figure 2 This is a schematic flow chart of a second embodiment of a dynamic electrocardiogram analysis method provided by the present application;

[0047] Figure 3 It is a schematic flow chart of a third embodiment of a dynamic electrocardiogram analysis method provided by the present application;

[0048] Figure 4 It is a schematic flow chart of a fourth embodiment of a dynamic electrocardiogram analysis method provided by the present application;

[0049] Figure 5 is a simplified structural diagram of a terminal device provided in an embodiment of the present application;

[0050] Figure 6 It is a structural schematic diagram of the dynamic electrocardiogram analysis device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0052] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0053] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0054] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0055] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0056] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0057] Dynamic electrocardiogram can assist doctors to improve analysis efficiency and diagnosis level. The applicant of the present invention has found that in the current mainstream dynamic electrocardiogram analysis software, the collected electrocardiogram data is only analyzed separately by day, or the data of multiple days are analyzed together. This will result in that in the multi-day detection, the development of the disease, the rehabilitation effect after surgical treatment, the effect of the drug, etc., which change over time are not automatically analyzed. Doctors need to open multiple cases and compare and analyze them manually repeatedly, which reduces the doctor's diagnostic efficiency.

[0058] In order to solve the above problems, the present invention proposes a dynamic electrocardiogram analysis method, which is applied to an electrocardiogram terminal device, such as a dynamic electrocardiogram detector. Figure 1 As shown, the dynamic electrocardiogram analysis method of this embodiment mainly includes the following steps:

[0059] Step S10, obtaining historical ECG data and current ECG data of the subject to be tested, wherein the current ECG data is the ECG data within a preset time period, and the historical ECG data is the ECG data corresponding to each historical time period before the preset time period of the subject to be tested;

[0060] It should be noted that the execution subject of this embodiment takes a dynamic electrocardiogram detector as an example; the time period takes a day as an example, and the "preset time period" represents today; the electrocardiogram data may at least include electrocardiogram bands corresponding to the ST segment and T wave of the object to be measured, QRS wave, and electrocardiogram data corresponding to the RR interval. The object to be measured may be a patient wearing a dynamic electrocardiogram detector.

[0061] Step S20, generating change trend data based on each historical ECG data and the current ECG data;

[0062] It is understandable that the unit of the time period is one day, and the historical time period is a day before today. Then the historical ECG data can be the ECG data recorded every day by the object to be tested from a day before today to yesterday. The above time range can be set according to the user's needs. For example, the above time range can be set by the user to the period from the start date of the use of the dynamic electrocardiogram detector of this embodiment by the user to be tested before today to yesterday. In this way, the current ECG data (today) plus the historical ECG data (from the start date to yesterday) are the entire process of the ECG activity data continuously recorded by the object to be tested in daily life, and the changing trend is a changing trend presented by the ECG data of the whole process.

[0063] Step S30: Analyze the change trend data to generate event description information.

[0064] Specifically, the change trend data analysis results may at least include ST / T wave amplitudes of the whole heartbeat, electrocardiogram vectors, scatter plots, the frequency of occurrence of atrial fibrillation and the total duration and other analysis results; the event description information may be "diagnosis suggestions", "patient severity" and other text information used to assist doctors in diagnosis;

[0065] The user can perform human-computer interaction based on the graphical operation interface of the dynamic electrocardiogram detector. The dynamic electrocardiogram detector can respond to the user's preset trigger operation on the target change trend data, and then display the triggered target event description information, wherein the user can be a doctor or a subject to be tested (such as a patient). For example, if the user wants to view the specific information and treatment suggestions of a certain myocardial infarction site, the ST / T waves of different leads in the target change trend graph can be triggered by touching the screen. The preset trigger operation can be the user's touch screen operation on the display screen of the dynamic electrocardiogram detector, or the user's operation of triggering a panel button on the dynamic electrocardiogram detector.

[0066] In the specific implementation, this embodiment performs statistical analysis on the ECG data information calculated from multiple days of data at a higher dimension based on the daily analysis of multiple days of data. The analysis results can characterize the progression of the condition of the subject to be tested, or the recovery after surgical treatment, or the effect of a certain drug on the heart, etc. It can realize long-term detection of the patient's ECG and is suitable for the chronic disease management of heart-related diseases.

[0067] In addition, the dynamic electrocardiogram analysis method of the present application also includes:

[0068] The dynamic electrocardiogram detector responds to the user's switching operation on the type of electrocardiogram data to determine the type of current electrocardiogram data; and displays the target change trend data corresponding to the type of current electrocardiogram data.

[0069] It is understandable that the user performs human-computer interaction based on the graphical operation interface of the dynamic electrocardiogram detector to select the type of ECG data. The type of ECG data includes at least the ECG band type, QRS wave type, and the ECG data type corresponding to the RR interval. Different types of ECG data correspond to different change trend data. For example, by displaying and analyzing the statistics of ECG waveform characteristics (ST / T wave amplitude showing the ECG band type), interval characteristics, event information, and the location of the disease, and the statistical results, doctors can be assisted in judging the development of the disease, the development trend, and whether the condition has worsened. By statistically analyzing ECG information for more than a few days, outputting its change trend, and qualitatively displaying it, more information can be provided to the doctor's judgment, which can improve the efficiency and accuracy of the doctor's diagnosis.

[0070] Embodiment 2

[0071] refer to Figure 2 , Figure 2 The embodiment of the flow chart of the method for analyzing a dynamic electrocardiogram in which the type of electrocardiogram data is the first electrocardiogram data; the first electrocardiogram data includes an electrocardiogram band corresponding to an ST segment and / or a T wave;

[0072] The step S10 further comprises:

[0073] Step S10a: obtaining the first ECG data (ST value and / or T wave amplitude) of the subject to be tested within the preset time period; specifically, the Holter detector of this embodiment calculates the first ECG data of each heartbeat of the subject to be tested within the preset time period (i.e., the ST value of each heartbeat and / or the T wave amplitude of each heartbeat);

[0074] The step S20 further comprises:

[0075] Step S20a: combining the first ECG data (ST value and / or T wave amplitude) within the preset time period and within each historical time period to generate a first ECG data change trend graph, wherein the first ECG data change trend graph represents an ST band change trend graph and / or a T wave amplitude change trend graph;

[0076] Specifically, the Holter detector of this embodiment adds and averages the first electrocardiogram data (ST value and / or T wave amplitude) of each heart beat per minute within the preset time period to obtain the mean value of the first electrocardiogram data per minute (i.e., the mean value of the ST value per minute and / or the mean value of the T wave amplitude per minute);

[0077] Then, the current first ECG data trend graph and the historical first ECG data trend graphs in each historical unit time period are combined to obtain the first ECG data change trend graph:

[0078] For the ST value, a current ST band trend graph is generated based on the average values ​​of each ST value, and finally the current ST band trend graph is combined with the historical ST band trend graphs in each historical unit time period to obtain an ST band change trend graph;

[0079] For the T wave amplitude, a current T wave amplitude change trend graph is generated based on the mean values ​​of each T wave amplitude, and the current T wave amplitude trend graph is combined with the historical T wave amplitude trend graphs in each historical period to obtain a T wave amplitude change trend graph;

[0080] That is, in the analysis of the ECG data of a single day, the present embodiment calculates the ST value and T wave amplitude information of each heart beat, and then calculates the mean of the ST and T wave amplitudes per minute to form an ST / T wave trend graph;

[0081] In a specific implementation, the first ECG data change trend graph may be generated in the following manner: a time axis is established in a statistical interface, and the amplitude changes of ST / T during the multi-day ECG data recording process are marked on the time axis, so as to facilitate the user (doctor) to understand the development process of the condition of the subject to be tested;

[0082] When the user uses the dynamic electrocardiogram monitor of this embodiment, the current interface of the dynamic electrocardiogram monitor will display the amplitude change of the first ECG data along the time axis. The amplitude change can be the amplitude change in all time periods, the amplitude change in continuous time periods within a certain time range, the amplitude change in some discrete time periods within a certain time range, or the amplitude change within a certain time period range selected by the user.

[0083] In one example, the above-mentioned change trend graph may also include start time and end time options corresponding to different periods of myocardial infarction, so as to facilitate the user to select myocardial infarction data of different periods;

[0084] In one example, the above-mentioned change trend graph may also include data related to the current development stage of the patient;

[0085] In one example, a time axis coordinate system can also be established in the statistical interface of the above-mentioned trend graph, in which the horizontal axis is the time axis of consecutive days from near to far (the vertical axis corresponds to the number of days), and the vertical axis in the time axis coordinate system is the total duration of ST / T wave abnormalities every day, which is used to show the time changes of ST / T wave abnormalities during multi-day data recording.

[0086] The step S30 further comprises:

[0087] Step S30a: performing feature analysis on the variation trend graph of the first ECG data (the ST band variation trend graph and / or the T wave amplitude) to obtain a first myocardial event, and generating first description information corresponding to the first myocardial event.

[0088] It should be noted that the first myocardial event may include a "myocardial infarction" event, and the first description information may include information such as "myocardial infarction occurrence site" or "myocardial infarction culprit vessel";

[0089] In this embodiment, first abnormal data is obtained based on the first ECG data change trend graph, and the first abnormal data at least includes a maximum value, an average value, and a duration related to the lead combination data; a first myocardial event is obtained based on the first abnormal data, and first description information corresponding to the first myocardial event is generated;

[0090] In a specific implementation, the dynamic electrocardiogram detector of this embodiment obtains multiple lead data based on the first electrocardiogram data change trend diagram, selects lead combination data that meets the preset abnormal conditions from the multiple lead data; then calculates the maximum value, average value and duration related to the lead combination data; the lead data ST lead and T wave lead:

[0091] Specifically, for the ST value, the Holter monitor of this embodiment will obtain abnormal ST data based on the ST band change trend diagram, and the abnormal ST data at least includes the maximum value, average value and duration related to the abnormal ST lead;

[0092] As for the T wave amplitude, the dynamic electrocardiogram detector of this embodiment will obtain abnormal T wave data based on the T wave amplitude change trend diagram, and the abnormal T wave data at least includes the maximum value, average value and duration related to the abnormal T wave lead;

[0093] Finally, a first myocardial event is obtained according to the abnormal ST data and / or the abnormal T wave data, and first description information corresponding to the first myocardial event is generated.

[0094] In a specific implementation, this embodiment takes the "myocardial infarction" event as the first myocardial event as an example, and the above step S30a can be completed by the following sub-steps:

[0095] Sub-step a1, obtaining the ST elevation / depression threshold set by the user, and based on the ST elevation / depression threshold, finding the lead data with the largest proportion of ST elevation / depression in the ST band change trend diagram, taking the lead data with the largest proportion as the main data, counting the lead data combinations in which ST elevation / depression occurs simultaneously, and outputting the lead data combination with the largest proportion;

[0096] Sub-step a2, calculating the maximum value, average value and duration of abnormal ST change of each lead data of the lead data combination with the largest proportion in the single-day ECG data;

[0097] Sub-step a3, performing statistics on the lead data related to the T wave amplitude in the same processing manner as the above sub-steps a to b;

[0098] Sub-step a4, counting the changes in the ST-T morphology and amplitude of the multi-day data, and outputting the first myocardial event ("myocardial infarction" event) according to the changes in the ST-T morphology and amplitude of the multi-day data and in combination with the first preset condition; specifically, the dynamic electrocardiogram detector of this embodiment will pre-store a code program of the first preset condition that represents medical clinical experience and standards, and the condition type of the first preset condition includes an abnormally tall T wave corresponding to the electrocardiogram in the hyperacute period, ST segment elevation in the acute period, T wave inversion from upright, ST recovery to baseline in the subacute period, T wave inversion becomes shallow. In the old period, the ST segment and T wave return to normal, etc., the pre-set condition presentation code, the dynamic electrocardiogram detector will determine whether the changes in the ST-T morphology and amplitude of the multi-day data obtained meet a certain condition type of the first preset condition, and finally be able to output the entire myocardial infarction development of the subject to be tested during the process of wearing the dynamic electrocardiogram detector (i.e., the first description information).

[0099] It should be noted that this embodiment counts the lead data combinations where ST / T wave changes occur, and different lead data combinations correspond to different myocardial infarction location information and myocardial infarction vascular images;

[0100] When the doctor uses the dynamic electrocardiogram detector of this embodiment, the current interface of the dynamic electrocardiogram detector will display the lead data combination generated when the first electrocardiogram data (ST / T wave) changes in all time periods by default (this embodiment takes one "time period" as "one day" as an example); and the current interface of the dynamic electrocardiogram detector will also display the (3D animated) heart model of the object to be tested, and finally deepen the image of the myocardial infarction position corresponding to the lead data combination in the heart model:

[0101] In this embodiment, the myocardial infarction area and the culprit vessel corresponding to the ST / T abnormal lead data combination of each day in the multi-day data can be deepened and visualized in the 3D heart model. The interface of the 3D heart model will dynamically display the change process of the deepened area over time: expansion, contraction or transfer.

[0102] In addition, users can also select data for a specified continuous period from multiple days of data:

[0103] Specifically, the dynamic electrocardiogram detector responds to a specified continuous time period selected by a user (who may be a doctor or a patient) from all the time periods; based on the myocardial infarction location information and myocardial infarction vascular images corresponding to the lead combination data within the specified continuous time period; dynamically displays (e.g., plays) the change process of the myocardial infarction location image and / or the myocardial infarction vascular image within the specified continuous time period in the heart model: in a specific implementation, when the user uses the dynamic electrocardiogram detector of this embodiment, the user can choose to display the deepened area of ​​any day in the multi-day data, or can choose to play the change process of the deepened area of ​​any consecutive days in the multi-day data.

[0104] The beneficial effects of the second embodiment are: the ST / T wave data of multiple days can be analyzed separately by day, and the ST / T wave ECG analysis results of each day can be obtained; then the analysis results of the overall ST / T wave ECG data are further automatically analyzed, and the statistical results of the development and change process of the first myocardial event of the object to be tested can be obtained, and the change process of the development of myocardial infarction of the object to be tested can be visualized, which helps to improve the doctor's diagnostic efficiency and accuracy.

[0105] Embodiment 3

[0106] refer to Figure 3 , Figure 3 A schematic flow chart of an embodiment of a method for analyzing a dynamic electrocardiogram when the type of electrocardiogram data is a QRS wave;

[0107] In this embodiment, the step S10 further includes:

[0108] Step S101b, obtaining the QRS wave vector ring of the object to be measured within the preset time period;

[0109] It is understandable that the time period in this embodiment is one day, the preset time period is today, and the historical time period is a day before today;

[0110] Step S102b, calculating the ECG vector parameters of each wave vector loop, performing weighted average processing on each ECG vector parameter, and obtaining the current parameter average value of the QRS wave vector loop;

[0111] In this embodiment, the ECG vector parameters may at least include: the amplitude of each wave vector ring, the angle of the average ECG axis, the area proportion of the R ring and the S ring, and the amplitude and angle of the maximum vector of the R ring and the S ring and their amplitude ratio;

[0112] In a specific implementation, the above step S101b and step S102b may be completed through the following sub-steps:

[0113] Sub-step b1, during the analysis of the ECG data of a single day, m minutes may be randomly selected in each hour, and normal heart beats contained therein may be selected.

[0114] Sub-step b2, calculate the QRS wave vector loop formed by these normal heart beats, calculate the duration of the QRS loop, and the angle of the average cardiac axis.

[0115] Sub-step b3, calculating the running direction of the entire QRS loop in the frontal plane and the transverse plane, calculating the area proportion of the R loop and the S loop respectively, and calculating the amplitude and angle of the maximum vector of the R loop and the S loop and their amplitude ratio.

[0116] Sub-step b4, calculating the average value of the above electrocardiographic vector parameters of all normal heart beats selected from the single-day data.

[0117] The step S20 further comprises:

[0118] Step S20b, obtaining the historical parameter averages of the QRS wave vector ring in each historical period, and generating a vector electrocardiogram change trend diagram of the QRS wave vector ring in all unit periods based on the historical parameter averages and the current parameter averages;

[0119] It is understandable that the said all unit time periods are multiple days including today, and the said electrocardiogram change trend diagram represents the all electrocardiogram change effect diagrams of the subject to be measured within the time range from a previous day to today.

[0120] In one example, the ECG vector change trend graph is displayed in a statistical interface for displaying multi-day data, and has the function of displaying the change process of different types of ECG vector parameters within a multi-day time range (dynamically displaying the vector movement process in multi-day detection in the transverse and frontal planes). The change process of these displayed ECG vector parameters includes at least: the change process of the average ECG axis size and angle, the change process of the maximum vector size and angle of the R loop, and the change process of the maximum vector size and angle of the S loop.

[0121] In one example, the ECG vector change trend graph also has the function of displaying the change of the running direction of the QRS loop of multiple days of data in the form of a table:

[0122] In one example, the ECG vector change trend graph also has the function of displaying ECG vector related data in the form of a line graph, including the duration of the QRS loop, the amplitude ratio of the maximum vectors of the S loop and the R loop, the area ratio of the S loop, and the time when the maximum vector occurs.

[0123] The step S30 further comprises:

[0124] Step S301b, performing feature analysis on the vector cardiogram change trend diagram to obtain a second myocardial event;

[0125] Step S302b: acquiring a current abnormality type corresponding to the second myocardial event, and generating corresponding second description information based on the current abnormality type.

[0126] In this embodiment, the second myocardial event may be a myocardial hypertrophy event, and the second description information may be prompt information; the second description information generated based on the current abnormality type may include at least several situations:

[0127] If the maximum amplitude of the R vector exceeds the set threshold value, a prompt message is issued;

[0128] or

[0129] When the running direction of the QRS loop changes (e.g., the running direction of the horizontal QRS loop changes from counterclockwise to figure 8 or clockwise), a prompt message is issued;

[0130] or

[0131] When fitting a line graph output from multiple days of data using a fitting algorithm, if the fitting curve continues to show an upward trend, a prompt will be issued;

[0132] or

[0133] When the average cardiac axis exceeds the left and right angle thresholds and shows left and right deviations, and the deviation angle and amplitude continue to increase, a prompt is issued.

[0134] The vectorcardiogram change trend diagram of this embodiment displays the change process of the vectorcardiogram parameters in various forms on a multi-day data statistics interface, so as to assist doctors in determining whether the patient has myocardial hypertrophy and whether the condition of myocardial hypertrophy has worsened.

[0135] Embodiment 4

[0136] refer to Figure 4 , Figure 4 A schematic flow chart of an embodiment of a dynamic electrocardiogram analysis method when the type of electrocardiogram data is RR interval;

[0137] In this embodiment, the step S10 further includes:

[0138] Step S101c: obtaining a current scatter plot of RR intervals of adjacent heart beats of the subject to be tested within the preset time period;

[0139] In a specific implementation, the time period of this embodiment is one day, the preset time period is today, and the historical time period is a day before today;

[0140] During the analysis of single-day data, the RR interval information of adjacent heart beats was used for iterative mapping;

[0141] Specifically, in the statistical interface for displaying multi-day data in this embodiment, the current RR interval of each heart beat is used as the X-axis coordinate and the RR interval of the next heart beat is used as the Y-axis coordinate. The plane diagram of a large number of point sets formed by iteratively plotting the RR intervals of all heart beats is a scatter plot.

[0142] Step S102c: clustering the data points on the current scatter plot to obtain a current clustering result;

[0143] Accordingly, the step S20 further includes:

[0144] Step S201c: obtaining a historical scatter plot related to RR intervals of adjacent heart beats of the subject to be tested within the historical period; clustering the data points on the historical scatter plot to obtain a historical clustering result;

[0145] In the specific implementation process of clustering the data points on the current scatter plot and clustering the data points on the historical scatter plot in this embodiment, the data points of each scatter plot can be divided into different current cluster groups according to the scatter point distribution characteristics, and the number of current groups is counted, wherein each data point is divided into a specific cluster group;

[0146] In specific implementation, this embodiment can group the points on the scatter plot by using the K-Means clustering algorithm, first initialize the number of groups and group centers, classify the point into the group closest to it by calculating the distance between the data point and each group center, and then recalculate the group center by the average distance between all points in the group and the group center. Repeat the above steps, and finally divide the scatter plot into different groups and group centers according to its scatter distribution characteristics, and each point is divided into a specific group; and record the number of group centers corresponding to the scatter plot.

[0147] Step S202c: generating scatter plot change trend data based on the current clustering result and the historical clustering result;

[0148] It is understandable that the scatter plot change trend data can be presented in the following form: in a statistical interface for displaying multi-day data, the change trend of the number of groups over time is displayed.

[0149] Accordingly, the step S30 further includes:

[0150] Step S30c: monitoring the scatter plot change trend data, predicting or determining a corresponding arrhythmia event according to the monitoring result, and generating description information corresponding to the arrhythmia event.

[0151] It should be noted that the arrhythmia events of this embodiment may at least include premature beats, atrial flutter / atrial fibrillation events, ventricular flutter / ventricular fibrillation events, etc.;

[0152] The monitoring results may include the following situations:

[0153] Case 1: When the number of groups continues to increase, a description message will be issued to prompt that there may be multiple premature beats and the number of premature beats is increasing, reminding the subject to be tested (patient) or the user (doctor) to intervene in time or adjust the treatment plan.

[0154] Case 2: When the number of groups exceeds a certain threshold and the width exceeds a certain threshold (set by the user), a descriptive message will be issued to remind the subject to be tested (patient) or the user (doctor) to prevent the occurrence of atrial fibrillation events, because multiple premature beats emit impulses irregularly, and continued development may cause atrial flutter / atrial fibrillation events, ventricular flutter / ventricular fibrillation events.

[0155] In addition, in an example, the scatter plot change trend data of this embodiment also includes the proportion of heart beats in the group included above the 45-degree line of the single-day data, which is used to indicate the proportion of premature heart beats. In the multi-day data statistics interface, the change trend of the proportion of premature heart beats over time is displayed;

[0156] During the statistical process of multi-day data, if a fan-shaped scatter plot appears, a prompt will be given, and the results of atrial flutter / atrial fibrillation events (including the frequency of occurrence, the longest duration, and the changes in the total duration over time) will be simultaneously counted and displayed.

[0157] In addition, in one example, this embodiment also activates the atrial fibrillation heart rate management function, that is, for atrial fibrillation patients, their heart rate needs to be controlled within the range of 60 to 115 bpm. The detection method of the fan-shaped scatter plot is to detect the length of the group in the 45-degree line direction and the longest width in the perpendicular 45-degree line direction. When the width and the aspect ratio exceed the threshold, the group is considered to be a fan-shaped scatter plot.

[0158] This embodiment can count the changes in the scatter plot graph and automatically analyze the interval characteristics of the RR interval, thereby assisting the user in determining whether the condition is getting worse, thereby improving the efficiency of data analysis.

[0159] Embodiment 5

[0160] An embodiment of the present invention provides a terminal device, such as Figure 5 As shown, Figure 5This is a schematic diagram of the structure of a terminal device provided in an embodiment of the present application. The terminal device is used for a dynamic electrocardiogram analysis method and can be a dynamic electrocardiogram detector. The terminal device of this embodiment includes: a processor 01, a memory 02, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the dynamic electrocardiogram analysis method of the present application are implemented.

[0161] Those skilled in the art will understand that Figure 5 It is merely an example of the terminal device and does not constitute a limitation thereto. The terminal device may include more or fewer components than shown in the figure, or a combination of certain components, or different components.

[0162] The processor may be a central processing unit (CPU), and the processor 01 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0163] In some embodiments, the memory may be the internal storage unit, such as a hard disk or memory of a terminal device. In other embodiments, the memory may also be an external storage device of a terminal device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the terminal device.

[0164] Embodiment 6

[0165] In one embodiment, if Figure 6 As shown, the present invention also provides a dynamic electrocardiogram analysis device, comprising:

[0166] An acquisition module 10 is used to acquire current ECG data of the subject to be tested, where the current ECG data is ECG data within a preset time period;

[0167] A generating module 20, configured to generate change trend data based on each historical ECG data and the current ECG data, wherein the historical ECG data is the ECG data of the subject to be measured corresponding to each historical unit time before the preset time period;

[0168] The analysis module 30 is used to analyze the change trend data and generate event description information.

[0169] It should be noted that the above-mentioned device can be understood as a chip in the terminal device; the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0170] When the device is used in the terminal equipment of the dynamic electrocardiograph recorder, the statistical analysis of the analysis results of multiple days is of great significance to the development, treatment, long-term evaluation of rehabilitation of heart diseases, and research and evaluation of the effect of drug treatment.

[0171] In addition, an embodiment of the present application further provides a storage medium, which is a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0172] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0173] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0174] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0175] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0176] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0177] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A dynamic electrocardiogram analysis method, It is characterized in that The method comprises: Acquire historical ECG data and current ECG data of the subject to be tested, wherein the current ECG data is the ECG data within a preset time period, and the historical ECG data is the ECG data of the subject to be tested corresponding to each historical time period before the preset time period; Generate change trend data based on each historical ECG data and the current ECG data; The change trend data is analyzed to generate event description information.

2. The method according to claim 1, It is characterized in that The type of the electrocardiogram data includes first electrocardiogram data, the first electrocardiogram data includes an electrocardiogram band corresponding to an ST segment and / or a T wave, and the method further includes: Acquire the first electrocardiogram data of the subject to be measured within the preset time period; Combining the first electrocardiogram data within the preset time period and the first electrocardiogram data within each historical time period to generate a trend graph of changes in the first electrocardiogram data; Perform feature analysis on the first electrocardiogram data change trend graph to obtain a first myocardial event, and generate first description information corresponding to the first myocardial event.

3. The method according to claim 2, It is characterized in that The step of acquiring the first electrocardiogram data of the subject to be tested within the preset time period includes: Calculating the first electrocardiogram data of each heartbeat of the subject to be tested within a preset time period; The step of combining the first electrocardiogram data in the preset time period with the first electrocardiogram data in each historical unit time to generate a trend graph of changes in the first electrocardiogram data includes: Adding and averaging the first electrocardiogram data of each heartbeat per minute within the preset time period to obtain a mean value of the first electrocardiogram data per minute; generating a current first electrocardiogram data change trend graph based on each first electrocardiogram data mean value; Combining the current first ECG data trend graph with the historical first ECG data trend graphs in each historical unit time period to obtain the first ECG data change trend graph; The step of performing feature analysis on the first electrocardiogram data change trend graph to obtain a first myocardial event and generating first description information corresponding to the first myocardial event includes: Based on the first ECG data change trend graph, first abnormal data is obtained, and the first abnormal data at least includes a maximum value, an average value, and a duration related to the lead combination data; a first myocardial event is obtained according to the first abnormal data, and first description information corresponding to the first myocardial event is generated.

4. The method according to claim 3, It is characterized in that The acquiring first abnormal data based on the first electrocardiogram data change trend graph includes: Acquire a plurality of lead data based on the first electrocardiogram data change trend diagram, and select lead combination data that meets a preset abnormal condition from the plurality of lead data; The maximum value, average value and duration of the lead combination data are calculated.

5. The method according to any one of claims 1 to 4, It is characterized in that The method further comprises: Responding to the user's switching operation on the type of ECG data to determine the type of current ECG data; Display the target change trend data corresponding to the type of the current ECG data.

6. The method according to claim 5, It is characterized in that After displaying the target change trend data corresponding to the type of the current electrocardiogram data, the method further includes: In response to the user's preset triggering operation on the target change trend data, the triggered target event description information is displayed.

7. The method according to claim 5, It is characterized in that When the type of the current electrocardiogram data is the first electrocardiogram data, the step of displaying the first electrocardiogram data change trend graph includes: The amplitude change of the first electrocardiogram data along the first time axis is displayed on the current interface.

8. The method according to claim 5, It is characterized in that When the type of the current electrocardiogram data is the first electrocardiogram data, the step of displaying the first electrocardiogram data change trend graph includes: Acquire the lead combination data generated when the first electrocardiogram data changes in all time periods; Myocardial infarction position information and a myocardial infarction blood vessel image corresponding to the lead combination data are determined according to the lead combination data.

9. The method according to claim 8, It is characterized in that The method further comprises: In response to a user selecting a designated continuous time period from among all the time periods; Acquiring a heart model of the subject to be tested; Based on the myocardial infarction position information and myocardial infarction vascular images corresponding to the lead combination data within the specified continuous time period, The change process of the myocardial infarction position image and / or the myocardial infarction blood vessel image within the specified continuous time period is dynamically displayed in the heart model.

10. A dynamic electrocardiogram analysis device, It is characterized in that include: An acquisition module, used to acquire historical ECG data and current ECG data of the subject to be tested, wherein the current ECG data is the ECG data within a preset time period, and the historical ECG data is the ECG data corresponding to each historical time period before the preset time period of the subject to be tested; A generating module, used for generating change trend data based on each historical ECG data and the current ECG data; The analysis module is used to analyze the change trend data and generate event description information.

11. A terminal device, It is characterized in that The terminal device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method according to any one of claims 1 to 9 when executing the computer program.

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