Heart transport and thoracotomy monitoring method based on high temporal-spatial resolution electrical mapping

Abstract

The invention relates to the technical field of data processing, in particular to a heart transfer and thoracotomy monitoring method based on high temporal-spatial resolution electric mapping. The method comprises the following steps: S1, generating a correlation between an electrical mapping image and a mapping electrode, and obtaining a plurality of electrical mapping signal target waveforms; s2, analyzing the ECG signal in real time, performing denoising processing, and obtaining a plurality of target waveforms; s3, dividing each target waveform into a plurality of sub-waveforms according to waveform types, inputting the sub-waveforms into the first model to obtain a first output result, if the sub-waveforms are abnormal, executing the step S4, and if the sub-waveforms are normal, inputting a plurality of second normal waveforms which are continuous in time before the first normal waveform as a waveform sequence into a second model, and obtaining a second output result; and S4, calculating and analyzing to obtain an abnormal position, generating a dynamic image, and marking the dynamic image in the electric mapping image. The problem that the heart monitoring precision is not enough is solved, and the heart monitoring precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and more specifically to a cardiac transport and thoracotomy monitoring method based on high temporal and spatial resolution electrical mapping. Background Art

[0002] With the development of medical electronics, high spatiotemporal resolution electrical mapping technology monitoring methods are widely used in clinical practice, especially during and after cardiac transplantation and cardiac surgery. Similar prior art includes a Chinese patent with publication number CN109688904A, which proposes a system and method for detecting arrhythmic electrocardiogram signals, including multiple threshold heart rates and multiple rate-based sensitivity levels for detecting arrhythmic ECG segments, wherein a heart rate with higher clinical relevance is assigned a rate-based sensitivity level with higher sensitivity. The ECG signal is monitored by a medical device, and the monitored ECG signal is processed using the multiple threshold heart rates and the multiple rate-based sensitivity levels to detect and capture arrhythmic ECG segments. In addition, similar prior art includes the US patent with publication number US20140330145A1, which discloses a method for automatically determining the local activation time in a multi-channel cardiac electrogram signal including multiple cardiac channels, the method comprising: storing the cardiac channel signal; using the ventricle, reference and mapping channels to calculate a first LAT value at multiple mapping channel positions; monitoring the quality of at least one ventricle, reference and mapping channel; if the quality of the monitored cardiac channel is lower than the standard, replacing the substandard channel with another channel from multiple channels with higher quality than the standard; and calculating a second LAT value based on the replacement cardiac channel. The above two patents both solve the problem of cardiac monitoring, but do not consider the problem of joint monitoring of epicardial electrical mapping and ECG signals, and the accuracy is not high enough to meet the requirements of high-precision monitoring. The present invention adopts high-resolution epicardial electrical mapping and joint monitoring with ECG signals, which can not only intuitively obtain the abnormal position but also further improve the monitoring accuracy. Summary of the invention

[0003] In order to better solve the above problems, the present invention provides a method for monitoring cardiac transport and thoracotomy based on high spatiotemporal resolution electrical mapping, the method comprising the following steps: Step S1: collecting electrical mapping signals of each mapping electrode in the monitored object at a first preset period through an electrical mapping unit, and obtaining physiological information of the monitored object through calculation and analysis based on a plurality of the electrical mapping signals, and generating an electrical mapping image and a correlation between the mapping electrodes based on the physiological information and the position information of each mapping electrode, wherein the mapping electrodes are arranged on the outer membrane of the monitored object; Step S2: collecting the ECG signal of the monitored object in real time through the ECG signal collection unit, obtaining an analysis result by real-time analysis of the ECG signal, and when the analysis result is abnormal, performing denoising on the ECG signal, and obtaining a plurality of target waveforms based on the ECG signal; Step S3: dividing each of the target waveforms into a plurality of sub-waveforms according to the waveform type, and inputting the sub-waveforms into the first model to obtain a first output result. When the first output result is abnormal, executing step S4, when the first output result is normal, obtaining a first normal waveform according to the first output result, and further inputting N second normal waveforms that are time-continuous before the first normal waveform as a waveform sequence into the second model to obtain a second output result; Step S4: When the first output result is abnormal, all abnormal electrodes are obtained according to the first output result, and the abnormal position is obtained based on the electrical mapping signals of all the abnormal electrodes; when the second output result is abnormal, the target electrode is obtained according to the second output result, and a dynamic image is generated according to the electrical mapping signals of the target electrode, and the abnormal position and the dynamic image are marked in the electrical mapping image.

[0004] As a preferred technical solution, the mapping electrodes in the electrical mapping unit have at least 32 channels, and the distance between electrode points in each of the mapping electrodes is less than or equal to 4 mm.

[0005] As a preferred technical solution, step S1 includes: Step S11: acquiring the electrical mapping signal of each mapping electrode at a first preset period through the electrical mapping unit, and acquiring the conduction information of the monitored object according to the setting position of each mapping electrode and the corresponding electrical mapping signal, wherein the conduction information includes the excitation point, conduction direction, conduction velocity, depolarization dispersion and repolarization dispersion, conduction phase and spectral characteristics; Step S12: acquiring the electrical mapping image based on the position information of each mapping electrode and the conduction information, and extracting the electrical mapping waveform from each electrical mapping signal according to the heartbeat, calculating the conduction feature points on each electrical mapping waveform, and acquiring the waveform relationship and the conduction relationship based on the time-space sequence of the electrical mapping waveform and the conduction feature points, wherein the correlation relationship includes the waveform relationship and the conduction relationship.

[0006] As a preferred technical solution, step S2 includes: Step S21: collecting the ECG signal of the monitored object in real time through the ECG signal collection unit, and extracting a plurality of first waveforms in the ECG signal, wherein the first waveform is an R wave; Step S22: Obtain the maximum value of each of the first waveforms and the maximum potential fluctuation value of the corresponding reference potential interval, calculate the first difference between the maximum value and the first threshold, and the second difference between the maximum potential fluctuation value and the second threshold. When the first difference and the second difference corresponding to each of the first waveforms are within the corresponding set range, the analysis result corresponding to the ECG signal is normal. Otherwise, the analysis result corresponding to the ECG signal is abnormal. The ECG signal is taken as a noise waveform, the noise waveform is denoised, and the denoised ECG signal and the ECG signal when the analysis result is normal are taken as the target waveform.

[0007] As a preferred technical solution, step S3 includes: Step S31: dividing each of the target waveforms into a plurality of sub-waveforms according to the waveform type, and inputting the plurality of sub-waveforms into the first model, and obtaining the first output result, wherein the first model is a machine learning model trained by first learning data, and the first learning data includes sub-waveforms and corresponding disease types corresponding to ECG signals of historical heart disease patients stored in a database, and also includes sub-waveforms corresponding to ECG signals of normal people; Step S32: when the first output result is abnormal, obtaining the disease types corresponding to the input multiple sub-waveforms through the first output result, and executing the step S4; Step S33: When the first output result is normal, obtain the input multiple sub-waveforms corresponding to the target waveform, and use the target waveform as the first normal waveform, and obtain N time-continuous second normal waveforms before the first normal waveform, input the first normal waveform and N second normal waveforms as a waveform sequence into the second model, and obtain a second output result, wherein the second model is a machine learning model trained with second learning data.

[0008] As a preferred technical solution, the second learning data includes historical abnormal electrical mapping data stored in a database and historical ECG signal sequences within a preset time period before the historical abnormal electrical mapping data were collected, and also includes disease types corresponding to the historical abnormal electrical mapping data.

[0009] As a preferred technical solution, step S4 includes the following steps: Step S41: when the first output result is abnormal, obtain the first acquisition time of the target waveform corresponding to the first output result, obtain the electrical mapping signal collected by each mapping electrode at the second acquisition time closest to the first acquisition time, extract the electrical mapping waveform of each electrical mapping signal according to the heartbeat, calculate the conduction feature points on each electrical mapping waveform, obtain the electrode conduction relationship based on the electrical mapping waveforms and conduction feature points of the electrode and other electrodes around the electrode in time and space sequence, compare the electrode conduction relationship with the correlation relationship corresponding to the mapping electrode, when the two are consistent, the mapping electrode is a normal electrode, when the two are inconsistent, the mapping electrode is an abnormal electrode, and obtain the abnormal electrode position closest to the excitement point position and other abnormal electrode positions, generate an abnormal position based on the abnormal electrode position and the other abnormal electrode positions and mark it in the electrical mapping image; Step S42: when the second output result is abnormal, the target position corresponding to the electrical mapping image is obtained according to the historical abnormal electrical mapping data in the second output result, the corresponding target electrode is obtained based on the target position, the acquisition period of the target electrode by the electrical mapping unit is reduced to a second preset period, and the electrical mapping signal acquired by the target electrode is saved, and a dynamic image is generated within the corresponding range of the electrical mapping image with the saved electrical mapping signal of the target electrode, and the image is marked in the electrical mapping image; Step S43: The user takes corresponding measures based on the abnormal position and the electrical mapping signal at the abnormal position, and identifies the abnormality in real time according to the dynamic image.

[0010] As a preferred technical solution, step S4 further includes: When the second output result is normal, the acquisition period of the electrical labeling unit is increased to a third preset period, wherein the third preset period is smaller than the first preset period, and the first preset period is smaller than the second preset period.

[0011] As a preferred technical solution, the mapping electrode is flexible and biocompatible.

[0012] The present invention also provides a cardiac transport and thoracotomy monitoring system based on high temporal and spatial resolution electrical mapping, the system is used to implement the above method, the system comprises: An electrical mapping unit, used for collecting electrical mapping signals of each mapping electrode in the monitored object at a first preset period, and obtaining physiological information of the monitored object through calculation and analysis based on a plurality of the electrical mapping signals, and generating an electrical meter measurement image and a correlation between the mapping electrodes based on the physiological information and the position information of each mapping electrode, wherein the mapping electrodes are arranged on the outer membrane of the monitored object; An ECG signal acquisition unit is used to acquire ECG signals of a monitored object in real time, obtain analysis results by analyzing the ECG signals in real time, perform denoising on the ECG signals when the analysis results are abnormal, and obtain multiple target waveforms based on the ECG signals; a discrimination prediction unit, configured to divide each of the target waveforms into a plurality of sub-waveforms according to the waveform type, and input the sub-waveforms into a first model to obtain a first output result, and when the first output result is abnormal, execute step S4, and when the first output result is normal, obtain a first normal waveform according to the first output result, and further input N second normal waveforms that are continuous in time before the first normal waveform as a waveform sequence into a second model to obtain a second output result; A display generation unit is used to obtain all abnormal electrodes according to the first output result when the first output result is abnormal, and obtain the abnormal position based on the electrical mapping signals of all the abnormal electrodes; when the second output result is abnormal, obtain the target electrode according to the second output result, and generate a dynamic image according to the electrical mapping signals of the target electrode, and mark the abnormal position and the dynamic image in the mapping image.

[0013] Compared with the prior art, the beneficial effects of the present invention are at least as follows: The present invention obtains the above-mentioned electrical mapping signal of each of the above-mentioned mapping electrodes in a first preset period through the above-mentioned electrical mapping unit, obtains the above-mentioned electrical mapping image according to the position information of each of the above-mentioned mapping electrodes and the above-mentioned conduction information, and obtains the correlation between the above-mentioned mapping electrodes, wherein the mapping electrodes are arranged on the epicardium of the heart, and the above-mentioned electrical mapping unit is a high-resolution multi-channel mapping, and can simultaneously monitor various positions of the heart, laying a foundation for accurately and quickly obtaining the abnormal state and abnormal position of the above-mentioned monitored object, and the above-mentioned ECG signal acquisition unit is used to acquire the above-mentioned ECG signal of the above-mentioned monitored object in real time, and acquire the target waveform, and input the multiple above-mentioned sub-waveforms corresponding to the above-mentioned target waveform into the above-mentioned first model, and acquire the above-mentioned first output result, and judge whether the above-mentioned target waveform is normal. When the above-mentioned first output result is abnormal, each of the above-mentioned electrical mapping signals acquired at the second acquisition time is compared with the four surrounding adjacent electrical mapping signals. Compare and obtain comparison results. Based on the comparison results, obtain the electrode conduction relationship and compare it with the corresponding electrode correlation relationship. Obtain the abnormal electrode position closest to the excitement point and other abnormal electrode positions, and then obtain the abnormal position. When the first output result is normal, input the first normal waveform and the previous N time-continuous second normal waveforms into the second model to obtain the second output result. When the second output result is abnormal, obtain the target position and target electrode through the second output result, and obtain a more precise and accurate electrical mapping signal of the target electrode by reducing the sampling period of the electrical mapping unit. The electrical mapping signal of the target electrode is also saved, and a dynamic image is generated and marked in the electrical mapping image. Through the mutual cooperation of the above technical solutions, more abundant monitoring information is obtained, which is convenient for users to quickly and accurately identify abnormal positions and take corresponding measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The present invention is a flow chart of a cardiac transport and thoracotomy monitoring method based on high spatiotemporal resolution electrical mapping; Figure 2 This is a structural diagram of the cardiac transport and thoracotomy monitoring system based on high temporal and spatial resolution electrical mapping of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0016] The present invention provides a method for monitoring cardiac transport and thoracotomy based on high spatiotemporal resolution electrical mapping, such as Figure 1 As shown, the method comprises the following steps: Step S1: collecting electrical mapping signals of each mapping electrode in the monitored object at a first preset period through an electrical mapping unit, and obtaining physiological information of the monitored object through calculation and analysis based on a plurality of the electrical mapping signals, and generating an electrical mapping image and a correlation between the mapping electrodes based on the physiological information and the position information of each mapping electrode, wherein the mapping electrodes are arranged on the outer membrane of the monitored object; Specifically, during the heart transplant transportation process or open-chest surgery, it is necessary to obtain the heart's health status in real time with high precision. ECG signals can continuously record the patient's electrocardiogram and provide rich diagnostic information, but they are not specific enough and sometimes cannot accurately determine the type and location of arrhythmias. High-time resolution recordings can calculate a variety of time-frequency parameters such as high-frequency amplitude, high-frequency relative power, QRS duration, depolarization duration, asymmetry and morphological variation, so as to accurately map the heart's electrical activity, effectively capture tiny abnormalities, and locate abnormal electrical activity. More detailed electrophysiological parameters can be used to better evaluate the heart's health status before and after the heart transplant transportation process or open-chest surgery, so that medical staff can adjust the treatment plan in a timely manner to better protect the heart. This also reduces the patient's postoperative risks and improves the patient's postoperative survival rate and recovery quality. It is especially suitable for complex arrhythmias that cannot be clearly diagnosed by electrocardiogram, and there are problems with large data collection and calculation volume. Therefore, a method combining the above-mentioned high spatiotemporal resolution electrical mapping technology and ECG signals is adopted to improve the accuracy of diagnosis and the treatment effect. Specifically, the above-mentioned electrical mapping signal of each of the above-mentioned mapping electrodes, that is, the potential information and conduction time of the above-mentioned mapping electrode, is obtained by the above-mentioned electrical mapping unit in a first preset period, and the above-mentioned conduction information of the above-mentioned monitored object is obtained through the above-mentioned electrical mapping signal of the mapping electrode, and the above-mentioned electrical mapping image is obtained according to the position information of each of the above-mentioned mapping electrodes and the above-mentioned conduction information, and the electrical mapping signal of each of the above-mentioned mapping electrodes is compared with the electrical mapping signals of the adjacent mapping electrodes around it to obtain the correlation between the above-mentioned mapping electrodes. At the same time, since the above-mentioned mapping electrodes are located on the epicardium of the heart, the abnormal position can be intuitively located through the abnormal electrode. Through the above-mentioned technical scheme, not only the above-mentioned electrical mapping image can be obtained, but also the correlation between each of the above-mentioned mapping electrodes and the surrounding adjacent mapping electrodes can be obtained, laying a foundation for accurately and quickly obtaining the abnormal state and abnormal position of the above-mentioned monitored object.

[0017] Step S2: collecting the ECG signal of the monitored object in real time through the ECG signal collection unit, obtaining an analysis result by real-time analysis of the ECG signal, and when the analysis result is abnormal, performing denoising on the ECG signal, and obtaining a plurality of target waveforms based on the ECG signal; Specifically, the ECG signal of the monitored object is collected in real time by the ECG signal acquisition unit, and the first waveform in the ECG signal, i.e., the R wave, is extracted, and the maximum value of the first waveform and the corresponding maximum fluctuation value of the reference potential interval are obtained. The maximum fluctuation potential of the reference voltage interval and the maximum value of the R wave are used to determine whether the ECG signal is mixed with noise, and the noise waveform is denoised, and the ECG signal without noise and the ECG signal after denoising are used as the target waveform. Through the technical solution, the accurate target waveform can be obtained, laying a foundation for further accurately identifying abnormalities through the target waveform.

[0018] Step S3: dividing each of the target waveforms into a plurality of sub-waveforms according to the waveform type, and inputting the sub-waveforms into the first model to obtain a first output result. When the first output result is abnormal, executing step S4, when the first output result is normal, obtaining a first normal waveform according to the first output result, and further inputting N second normal waveforms that are time-continuous before the first normal waveform as a waveform sequence into the second model to obtain a second output result; Specifically, the target waveform is divided into a plurality of sub-waveforms according to the waveform type, and the plurality of sub-waveforms corresponding to the target waveform are input into the first model, and the first output result is obtained. The first model is a machine learning model trained with sub-waveforms and corresponding disease types corresponding to ECG signals of historical heart disease patients and sub-waveforms corresponding to ECG signals of normal people as training data. Whether the target waveform is normal can be judged by the first output result. Since the ECG signal can only judge whether the disease is present but cannot determine the specific location information of the disease, the high spatiotemporal resolution electrical mapping technology can solve this problem well. Therefore, when the first output result is abnormal, the step S4 is executed, and the specific abnormal location of the disease and more disease information are determined by the electrical mapping technology. When the first output result is normal, some heart diseases that are not obviously reflected in the ECG signal cannot be identified. Therefore, the target waveform corresponding to the first output result is used as the first normal waveform, and the first normal waveform and the previous N time-continuous second normal waveforms are input into the second model to obtain the second output result. Through the above technical solution, the monitoring state of the above monitoring object can be judged more accurately.

[0019] Step S4: When the first output result is abnormal, all abnormal electrodes are obtained according to the first output result, and the abnormal position is obtained based on the electrical mapping signals of all the abnormal electrodes; when the second output result is abnormal, the target electrode is obtained according to the second output result, and a dynamic image is generated according to the electrical mapping signals of the target electrode, and the abnormal position and the dynamic image are marked in the electrical mapping image.

[0020] Specifically, when the above-mentioned first output result is abnormal, each of the above-mentioned electrical mapping signals acquired at the second acquisition time closest to the first acquisition time of the target waveform corresponding to the above-mentioned first output result is taken as an analysis object, and each of the above-mentioned electrical mapping signals is compared with the four surrounding adjacent electrical mapping signals to obtain a comparison result, and the above-mentioned electrode conduction relationship is obtained based on the above-mentioned comparison result, and the above-mentioned electrode conduction relationship is compared with the corresponding above-mentioned electrode correlation relationship, and the abnormal electrode position closest to the above-mentioned excitement point is obtained, and other abnormal electrode positions and abnormal positions are also obtained, so that medical staff can take corresponding measures according to the abnormal position and the above-mentioned electrical mapping signals at the abnormal position. If it is abnormal, the target position and target electrode are obtained through the mapping position target position in the historical abnormal electrical mapping data in the second output result, and the sampling period of the electrical mapping unit is reduced to obtain a more precise and accurate electrical mapping signal of the target electrode. The electrical mapping signal of the target electrode is also saved, and a dynamic image is generated and marked in the electrical mapping image, so as to facilitate users to identify the abnormal position and take corresponding measures. Through the above technical solution, when the monitored object is transplanted, transferred or operated on, the real-time status of the monitored object can be obtained in real time, and when the monitored object is abnormal or is about to be abnormal, the abnormal position can be accurately and quickly determined, and timely measures can be taken.

[0021] Furthermore, the mapping electrodes in the electrical mapping unit have at least 32 channels, and the distance between electrode points in each of the mapping electrodes is less than or equal to 4 mm.

[0022] Specifically, electrical mapping data can use multiple mapping electrodes at the same time to record data from multiple parts of the heart, such as the atria and ventricles. High spatiotemporal resolution electrical mapping data should have a higher sampling rate so that high-frequency potential information can be recorded. By recording high spatiotemporal resolution electrical mapping records, subtle changes in the electrical activity of the heart can be captured. Electrocardiogram data is a standard heart monitoring tool widely used in clinical practice. It has multiple leads, each of which records the electrical activity of the heart from a different angle, providing a comprehensive view of heart function.

[0023] Furthermore, the step S1 comprises: Step S11: acquiring the electrical mapping signal of each mapping electrode at a first preset period through the electrical mapping unit, and acquiring the conduction information of the monitored object according to the setting position of each mapping electrode and the corresponding electrical mapping signal, wherein the conduction information includes the excitation point, conduction direction, conduction velocity, depolarization dispersion and repolarization dispersion, conduction phase and spectral characteristics; Step S12: acquiring the electrical mapping image based on the position information of each mapping electrode and the conduction information, and extracting the electrical mapping waveform from each electrical mapping signal according to the heartbeat, calculating the conduction feature points on each electrical mapping waveform, and acquiring the waveform relationship and the conduction relationship based on the time-space sequence of the electrical mapping waveform and the conduction feature points, wherein the correlation relationship includes the waveform relationship and the conduction relationship.

[0024] Specifically, the electrical mapping signal of each of the mapping electrodes, that is, the electrical signal and conduction time of the mapping electrodes, is obtained through the electrical mapping unit with a first preset period. Since the electrical mapping unit is a high temporal and spatial resolution electrical mapping unit, and the conduction information of the monitored object is obtained through the electrical mapping signal of the mapping electrode, wherein the excitement point in the conduction information is the starting point of the electric wave conduction of the monitored object, the electrical mapping image is also obtained according to the position information of each of the mapping electrodes and the conduction information, and the electrical mapping waveform is extracted from each electrical mapping signal according to the heartbeat, and the conduction feature points on each electrical mapping waveform are calculated. Based on the electrical mapping waveform and the conduction feature points at each mapping electrode position, the waveform relationship and conduction relationship at adjacent electrode positions are obtained in time and space sequence, and the waveform relationship includes waveform similarity and difference of the waveforms. Through the technical solution, not only the electrical mapping image but also the correlation between each of the mapping electrodes and the surrounding adjacent mapping electrodes can be obtained, laying a foundation for accurately and quickly obtaining the abnormal state and abnormal position of the monitored object.

[0025] Furthermore, the step S2 comprises: Step S21: collecting the ECG signal of the monitored object in real time through the ECG signal collection unit, and extracting a plurality of first waveforms in the ECG signal, wherein the first waveform is an R wave; Step S22: Obtain the maximum value of each of the first waveforms and the maximum potential fluctuation value of the corresponding reference potential interval, calculate the first difference between the maximum value and the first threshold, and the second difference between the maximum potential fluctuation value and the second threshold. When the first difference and the second difference corresponding to each of the first waveforms are within the corresponding set range, the analysis result corresponding to the ECG signal is normal. Otherwise, the analysis result corresponding to the ECG signal is abnormal. The ECG signal is taken as a noise waveform, the noise waveform is denoised, and the denoised ECG signal and the ECG signal when the analysis result is normal are taken as the target waveform.

[0026] Specifically, the ECG signal of the monitored object is collected in real time by the ECG signal collection unit, and the first waveform in the ECG signal, i.e., the R wave, is extracted, and the maximum value of the first waveform and the maximum fluctuation value of the corresponding reference potential interval are obtained. Since the reference potential interval of the R wave is an equipotential interval in the ECG signal waveform, and the potential changes slightly within a cardiac cycle, the ECG signal is judged whether there is noise mixed in the maximum fluctuation potential of the reference voltage interval and the maximum value of the R wave. When at least one of the first difference and the second difference is not within the corresponding set range, that is, the first difference is greater than When the first setting range or the above-mentioned second difference is greater than the second setting range, the above-mentioned ECG signal is a noise waveform, and the above-mentioned noise waveform is denoised; when the above-mentioned first difference and the above-mentioned second difference are both within the corresponding setting range, that is, the above-mentioned first difference is less than or equal to the above-mentioned first setting range and the second difference is less than or equal to the above-mentioned second setting range, the above-mentioned ECG signal is free of noise mixing, and the above-mentioned ECG signal without noise and the above-mentioned ECG signal after denoising are used as the above-mentioned target waveform. Through the above-mentioned technical scheme, the above-mentioned target waveform can be accurately obtained, laying a foundation for further accurately identifying abnormalities through the above-mentioned target waveform.

[0027] Furthermore, the step S3 comprises: Step S31: dividing each of the target waveforms into a plurality of sub-waveforms according to the waveform type, and inputting the plurality of sub-waveforms into the first model, and obtaining the first output result, wherein the first model is a machine learning model trained by first learning data, and the first learning data includes sub-waveforms and corresponding disease types corresponding to ECG signals of historical heart disease patients stored in a database, and also includes sub-waveforms corresponding to ECG signals of normal people; Step S32: when the first output result is abnormal, obtaining the disease types corresponding to the input multiple sub-waveforms through the first output result, and executing the step S4; Specifically, by dividing the target waveform into multiple sub-waveforms according to the waveform type, the waveform types include P wave, T wave and QRS wave, and inputting the multiple sub-waveforms corresponding to the target waveform into the first model, and obtaining the first output result, the first model is a machine learning model trained with sub-waveforms and corresponding disease types corresponding to ECG signals of historical heart disease patients, and sub-waveforms corresponding to ECG signals of normal people as training data. By inputting the sub-waveforms of the target waveform into the first model, it can be judged whether the target waveform is normal according to the first output result, that is, whether the target waveform is a diseased ECG signal. When the target waveform is abnormal, the corresponding disease type is also output. Since the ECG signal can only determine whether the disease is present, but cannot determine the specific location information of the disease, the high spatiotemporal resolution electrical mapping technology can solve this problem well. Therefore, by executing the step S4, the specific disease location and more disease information are determined by the electrical mapping technology, so that medical staff can take timely measures to ensure that the abnormal state of the monitored object will not further deteriorate.

[0028] Step S33: When the first output result is normal, obtain the input multiple sub-waveforms corresponding to the target waveform, and use the target waveform as the first normal waveform, and obtain N time-continuous second normal waveforms before the first normal waveform, input the first normal waveform and N second normal waveforms as a waveform sequence into the second model, and obtain a second output result, wherein the second model is a machine learning model trained with second learning data.

[0029] Specifically, although the first output result shows that the target waveform is normal, the ECG signal at a single moment can only reflect limited information about the monitored object and cannot accurately identify some diseases that are not obviously reflected in the ECG signal. Therefore, when the first output result is normal, the corresponding target waveform is taken as the first normal waveform, and the N time-continuous second normal waveforms before the first normal waveform are obtained, and the first normal waveform and the N time-continuous second normal waveforms are taken as the waveform sequence, and the waveform sequence is input into the second model to obtain the second output result, and then the health status of the monitored object is more accurately judged through the change information of each target waveform in the waveform sequence over time, wherein the first The second model is a machine learning model trained with second learning data. The above-mentioned second learning data is historical abnormal electrical mapping data and a historical ECG signal sequence within a preset time period before the acquisition time of the above-mentioned historical abnormal electrical mapping data. It also includes the disease type corresponding to the historical abnormal electrical mapping data. When the above-mentioned second output result is normal, it means that the above-mentioned monitored object is normal. When the above-mentioned second output result is abnormal, it means that the above-mentioned monitored object may have abnormalities in a future time period. Among them, when the above-mentioned second output result is abnormal, the above-mentioned second output result also outputs the corresponding historical abnormal mapping data and disease type. Through the above-mentioned technical solution, when the above-mentioned first output result is normal, the above-mentioned waveform sequence can be used to more accurately predict the monitoring status of the above-mentioned monitored object in a future time period.

[0030] Furthermore, the second learning data includes historical abnormal electrical mapping data stored in a database and historical ECG signal sequences within a preset time period before the acquisition time of the historical abnormal electrical mapping data, and also includes disease types corresponding to the historical abnormal electrical mapping data.

[0031] Specifically, the second model trained by using the second learning data can accurately predict the health status of the monitored object in the future time period. Medical staff can understand the status of the monitored object in the future time period in advance, and ensure that the monitored object can accurately and timely obtain the status parameters of the monitored object during transplantation, transportation or open-chest surgery.

[0032] Furthermore, the step S4 comprises: Step S41: when the first output result is abnormal, obtain the first acquisition time of the target waveform corresponding to the first output result, obtain the electrical mapping signal collected by each mapping electrode at the second acquisition time closest to the first acquisition time, extract the electrical mapping waveform of each electrical mapping signal according to the heartbeat, calculate the conduction feature points on each electrical mapping waveform, obtain the electrode conduction relationship based on the electrical mapping waveforms and conduction feature points of the electrode and other electrodes around the electrode in time and space sequence, compare the electrode conduction relationship with the correlation relationship corresponding to the mapping electrode, when the two are consistent, the mapping electrode is a normal electrode, when the two are inconsistent, the mapping electrode is an abnormal electrode, and obtain the abnormal electrode position closest to the excitement point position and other abnormal electrode positions, generate an abnormal position based on the abnormal electrode position and the other abnormal electrode positions and mark it in the electrical mapping image; Specifically, when the above-mentioned first output result is abnormal, it means that the monitored object may have an abnormal state, that is, a disease state, and it is necessary to promptly obtain more abundant abnormal information through the electrical mapping unit, so as to facilitate accurate and timely treatment by medical staff. Since the closer the first acquisition time to the target waveform corresponding to the above-mentioned first output result, the more accurate the above-mentioned electrical mapping signal acquired, each of the above-mentioned electrical mapping signals acquired at the above-mentioned second acquisition time is taken as an analysis object, wherein the above-mentioned second acquisition time may be before the first acquisition time or after the first acquisition time, and each of the above-mentioned electrical mapping signals is compared with other adjacent electrical mapping signals to obtain a comparison result, and the above-mentioned electrode conduction relationship is obtained based on the above-mentioned comparison result, and the above-mentioned electrode conduction relationship includes the conduction sequence and conduction velocity relationship between the above-mentioned electrode and the four adjacent electrodes around it, and the above-mentioned The electrode conduction relationship is compared with the corresponding electrode correlation relationship. When the two are consistent, the electrode is normal. When the two are inconsistent, the electrode is an abnormal electrode. Since the electrical conduction of the monitored object starts from the excitation point, the conduction at the position close to the excitation point is inaccurate, and the subsequent ones are abnormal. Therefore, the abnormal electrode position closest to the excitation point is obtained first, and other abnormal electrode positions are also obtained at the same time, wherein the other abnormal electrode positions are related to the abnormal electrode position and are marked as the first abnormal position in the electrical mapping image, so that medical staff can take corresponding measures according to the abnormal position and the electrical mapping signal at the abnormal position. Through the above technical solution, the abnormal position and the electrical mapping signal at the abnormal position can be quickly and accurately obtained, so that medical staff can take corresponding measures in time to prevent the condition of the monitored object from further deteriorating.

[0033] Step S42: when the second output result is abnormal, the target position corresponding to the electrical mapping image is obtained according to the historical abnormal electrical mapping data in the second output result, the corresponding target electrode is obtained based on the target position, the acquisition period of the target electrode by the electrical mapping unit is reduced to a second preset period, and the electrical mapping signal acquired by the target electrode is saved, and a dynamic image is generated within the corresponding range of the electrical mapping image with the saved electrical mapping signal of the target electrode, and the image is marked in the electrical mapping image; Step S43: The user takes corresponding measures based on the abnormal position and the electrical mapping signal at the abnormal position, and identifies the abnormality in real time according to the dynamic image.

[0034] Specifically, when the second output result is abnormal, it indicates that the monitored object may be abnormal next. Since the historical abnormal electrical mapping data includes not only the abnormal data but also the mapping position corresponding to the abnormal data, the target position corresponding to the electrical mapping image is obtained through the mapping position in the historical abnormal electrical mapping data in the second output result, and the target electrode at the target position is obtained based on the target position. By reducing the sampling period of the electrical mapping unit, a more precise and accurate electrical mapping signal of the target electrode is obtained, and the electrical mapping signal of the target electrode is saved. All the saved electrical mapping signals of the target electrode are used as an electrical mapping signal sequence, and the dynamic image is generated in the electrical mapping image based on the electrical mapping signal sequence, so as to facilitate the user to identify the abnormal position and take corresponding measures. Through the technical solution, when the monitored object is transplanted, transferred or undergoing thoracotomy, the real-time status of the monitored object can be obtained in real time, and when the monitored object is abnormal or is about to be abnormal, the abnormal position can be accurately and quickly determined, and timely measures can be taken.

[0035] Furthermore, the step S4 further includes: When the second output result is normal, the acquisition period of the electrical labeling unit is increased to a third preset period, wherein the third preset period is smaller than the first preset period, and the first preset period is smaller than the second preset period.

[0036] Specifically, although the high spatiotemporal resolution electrical mapping technology can more accurately monitor the status of the above-mentioned monitored object and obtain richer monitoring information of the monitored object, the high spatiotemporal resolution electrical mapping technology has a relatively large amount of data, occupies more storage resources, has a relatively large data transmission pressure, and requires a large power consumption of analog-to-digital conversion during acquisition. The equipment may also generate heat and the temperature may rise. Therefore, in the initial state, the above-mentioned electrical mapping unit performs electrical mapping signal acquisition and creates an electrical mapping image in the first preset period. When the above-mentioned second output result is normal, it means that the probability of the above-mentioned monitored object being abnormal is small. In order to save the above-mentioned storage resource occupation and reduce the temperature rise of the equipment, therefore, when the probability of the monitored object being abnormal is small, the above-mentioned acquisition period is increased to the above-mentioned third preset period.

[0037] Furthermore, the mapping electrode is flexible and biocompatible.

[0038] The present invention also provides a cardiac transport and thoracotomy monitoring system based on high temporal and spatial resolution electrical mapping, the system is used to implement the above method, such as Figure 2 As shown, the system comprises: An electrical mapping unit, used for collecting electrical mapping signals of each mapping electrode in the monitored object at a first preset period, and obtaining physiological information of the monitored object through calculation and analysis based on a plurality of the electrical mapping signals, and generating an electrical meter measurement image and a correlation between the mapping electrodes based on the physiological information and the position information of each mapping electrode, wherein the mapping electrodes are arranged on the outer membrane of the monitored object; An ECG signal acquisition unit is used to acquire ECG signals of a monitored object in real time, obtain analysis results by analyzing the ECG signals in real time, perform denoising on the ECG signals when the analysis results are abnormal, and obtain multiple target waveforms based on the ECG signals; a discrimination prediction unit, configured to divide each of the target waveforms into a plurality of sub-waveforms according to the waveform type, and input the sub-waveforms into a first model to obtain a first output result, and when the first output result is abnormal, execute step S4, and when the first output result is normal, obtain a first normal waveform according to the first output result, and further input N second normal waveforms that are continuous in time before the first normal waveform as a waveform sequence into a second model to obtain a second output result; A display generation unit is used to obtain all abnormal electrodes according to the first output result when the first output result is abnormal, and obtain the abnormal position based on the electrical mapping signals of all the abnormal electrodes; when the second output result is abnormal, obtain the target electrode according to the second output result, and generate a dynamic image according to the electrical mapping signals of the target electrode, and mark the abnormal position and the dynamic image in the mapping image.

[0039] In summary, the present invention obtains the above-mentioned electrical mapping signal of each of the above-mentioned mapping electrodes with a first preset period through the above-mentioned electrical mapping unit, obtains the above-mentioned electrical mapping image according to the position information of each of the above-mentioned mapping electrodes and the above-mentioned conduction information, and obtains the correlation between the above-mentioned mapping electrodes, wherein the mapping electrodes are arranged on the epicardium of the heart, and the above-mentioned electrical mapping unit is a high-resolution multi-channel mapping, and can simultaneously monitor various positions of the heart, laying a foundation for accurately and quickly obtaining the abnormal state and abnormal position of the above-mentioned monitored object, and the above-mentioned ECG signal acquisition unit is used to acquire the above-mentioned ECG signal of the above-mentioned monitored object in real time, and acquire the target waveform, and input the multiple above-mentioned sub-waveforms corresponding to the above-mentioned target waveform into the above-mentioned first model, and acquire the above-mentioned first output result, and judge whether the above-mentioned target waveform is normal. When the above-mentioned first output result is abnormal, each of the above-mentioned electrical mapping signals acquired at the second acquisition time is compared with the four surrounding adjacent electrical mapping signals Make a comparison and obtain a comparison result. Based on the comparison result, obtain the electrode conduction relationship and compare it with the corresponding electrode correlation relationship. Obtain the abnormal electrode position closest to the excitement point and other abnormal electrode positions to obtain the abnormal position. When the first output result is normal, input the first normal waveform and the previous N time-continuous second normal waveforms into the second model to obtain the second output result. When the second output result is abnormal, obtain the target position and target electrode through the second output result. By reducing the sampling period of the electrical mapping unit, a more precise and accurate electrical mapping signal of the target electrode is obtained. The electrical mapping signal of the target electrode is also saved, and a dynamic image is generated and marked in the electrical mapping image. Through the mutual cooperation of the above technical solutions, more abundant monitoring information is obtained to facilitate users to quickly and accurately identify abnormal positions and take corresponding measures.

[0040] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0041] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0042] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for monitoring cardiac transit and thoracotomy based on high spatiotemporal resolution electrical mapping, characterized in that: The method comprises the following steps: Step S1: collecting electrical mapping signals of each mapping electrode in the monitored object at a first preset period through an electrical mapping unit, and obtaining physiological information of the monitored object through calculation and analysis based on a plurality of the electrical mapping signals, and generating an electrical mapping image and a correlation between the mapping electrodes based on the physiological information and the position information of each mapping electrode, wherein the mapping electrodes are arranged on the outer membrane of the monitored object; Step S2: collecting the ECG signal of the monitored object in real time through the ECG signal collection unit, obtaining an analysis result by real-time analysis of the ECG signal, and when the analysis result is abnormal, performing denoising on the ECG signal, and obtaining a plurality of target waveforms based on the ECG signal; Step S3: dividing each of the target waveforms into a plurality of sub-waveforms according to the waveform type, and inputting the sub-waveforms into the first model to obtain a first output result. When the first output result is abnormal, executing step S4, when the first output result is normal, obtaining a first normal waveform according to the first output result, and further inputting N second normal waveforms that are time-continuous before the first normal waveform as a waveform sequence into the second model to obtain a second output result; Step S4: When the first output result is abnormal, all abnormal electrodes are obtained according to the first output result, and the abnormal position is obtained based on the electrical mapping signals of all the abnormal electrodes; when the second output result is abnormal, the target electrode is obtained according to the second output result, and a dynamic image is generated according to the electrical mapping signals of the target electrode, and the abnormal position and the dynamic image are marked in the electrical mapping image.

2. The method according to claim 1, characterized in that The mapping electrodes in the electrical mapping unit have at least 32 channels, and the distance between electrode points in each of the mapping electrodes is less than or equal to 4 mm.

3. The method according to claim 1, characterized in that The step S1 comprises: Step S11: acquiring the electrical mapping signal of each mapping electrode at a first preset period through the electrical mapping unit, and acquiring the conduction information of the monitored object according to the setting position of each mapping electrode and the corresponding electrical mapping signal, wherein the conduction information includes the excitation point, conduction direction, conduction velocity, depolarization dispersion and repolarization dispersion, conduction phase and spectral characteristics; Step S12: acquiring the electrical mapping image based on the position information of each mapping electrode and the conduction information, and extracting the electrical mapping waveform from each electrical mapping signal according to the heartbeat, calculating the conduction feature points on each electrical mapping waveform, and acquiring the waveform relationship and the conduction relationship based on the time-space sequence of the electrical mapping waveform and the conduction feature points, wherein the correlation relationship includes the waveform relationship and the conduction relationship.

4. The method according to claim 1, characterized in that: The step S2 comprises: Step S21: collecting the ECG signal of the monitored object in real time through the ECG signal collection unit, and extracting a plurality of first waveforms in the ECG signal, wherein the first waveforms are R waves and other ECG waveform features; Step S22: Obtain the maximum value of each of the first waveforms and the maximum potential fluctuation value of the corresponding reference potential interval, calculate the first difference between the maximum value and the first threshold, and the second difference between the maximum potential fluctuation value and the second threshold. When the first difference and the second difference corresponding to each of the first waveforms are within the corresponding set range, the analysis result corresponding to the ECG signal is normal. Otherwise, the analysis result corresponding to the ECG signal is abnormal. The ECG signal is taken as a noise waveform, the noise waveform is denoised, and the denoised ECG signal and the ECG signal when the analysis result is normal are taken as the target waveform.

5. The method according to claim 1, characterized in that The step S3 comprises: Step S31: dividing each of the target waveforms into a plurality of sub-waveforms according to the waveform type, and inputting the plurality of sub-waveforms into the first model, and obtaining the first output result, wherein the first model is a machine learning model trained by first learning data, and the first learning data includes sub-waveforms and corresponding disease types corresponding to ECG signals of historical heart disease patients stored in a database, and also includes sub-waveforms corresponding to ECG signals of normal people; Step S32: when the first output result is abnormal, obtaining the disease types corresponding to the input multiple sub-waveforms through the first output result, and executing the step S4; Step S33: When the first output result is normal, obtain the input multiple sub-waveforms corresponding to the target waveform, and use the target waveform as the first normal waveform, and obtain N time-continuous second normal waveforms before the first normal waveform, input the first normal waveform and N second normal waveforms as a waveform sequence into the second model, and obtain a second output result, wherein the second model is a machine learning model trained with second learning data.

6. The method according to claim 5, characterized in that The second learning data includes historical abnormal electrical mapping data stored in a database and historical ECG signal sequences within a preset time period before the acquisition time of the historical abnormal electrical mapping data, and also includes disease types corresponding to the historical abnormal electrical mapping data.

7. The method according to claim 1, characterized in that The step S4 comprises the following steps: Step S41: when the first output result is abnormal, obtain the first acquisition time of the target waveform corresponding to the first output result, obtain the electrical mapping signal collected by each mapping electrode at the second acquisition time closest to the first acquisition time, extract the electrical mapping waveform of each electrical mapping signal according to the heartbeat, calculate the conduction feature points on each electrical mapping waveform, obtain the electrode conduction relationship based on the electrical mapping waveforms and conduction feature points of the electrode and other electrodes around the electrode in time and space sequence, compare the electrode conduction relationship with the correlation relationship corresponding to the mapping electrode, when the two are consistent, the mapping electrode is a normal electrode, when the two are inconsistent, the mapping electrode is an abnormal electrode, and obtain the abnormal electrode position closest to the excitement point position and other abnormal electrode positions, generate an abnormal position based on the abnormal electrode position and the other abnormal electrode positions and mark it in the electrical mapping image; Step S42: when the second output result is abnormal, the target position corresponding to the electrical mapping image is obtained according to the historical abnormal electrical mapping data in the second output result, the corresponding target electrode is obtained based on the target position, the acquisition period of the target electrode by the electrical mapping unit is reduced to a second preset period, and the electrical mapping signal acquired by the target electrode is saved, and a dynamic image is generated within the corresponding range of the electrical mapping image with the saved electrical mapping signal of the target electrode, and the image is marked in the electrical mapping image; Step S43: The user takes corresponding measures based on the abnormal position and the electrical mapping signal at the abnormal position, and identifies the abnormality in real time according to the dynamic image.

8. The method according to claim 7, characterized in that The step S4 further comprises: When the second output result is normal, the acquisition period of the electrical labeling unit is increased to a third preset period, wherein the third preset period is smaller than the first preset period, and the first preset period is smaller than the second preset period.

9. The method according to claim 1, characterized in that: The mapping electrode is flexible and biocompatible.

10. A cardiac transport and thoracotomy monitoring system based on high spatiotemporal resolution electrical mapping, the system being used to implement the method according to any one of claims 1 to 8, characterized in that: The system comprises: An electrical mapping unit, used for collecting electrical mapping signals of each mapping electrode in the monitored object at a first preset period, and obtaining physiological information of the monitored object through calculation and analysis based on a plurality of the electrical mapping signals, and generating an electrical meter measurement image and a correlation between the mapping electrodes based on the physiological information and the position information of each mapping electrode, wherein the mapping electrodes are arranged on the outer membrane of the monitored object; An ECG signal acquisition unit is used to acquire ECG signals of a monitored object in real time, obtain analysis results by analyzing the ECG signals in real time, perform denoising on the ECG signals when the analysis results are abnormal, and obtain multiple target waveforms based on the ECG signals; a discrimination prediction unit, configured to divide each of the target waveforms into a plurality of sub-waveforms according to the waveform type, and input the sub-waveforms into a first model to obtain a first output result, and when the first output result is abnormal, execute step S4, and when the first output result is normal, obtain a first normal waveform according to the first output result, and further input N second normal waveforms that are continuous in time before the first normal waveform as a waveform sequence into a second model to obtain a second output result; A display generation unit is used to obtain all abnormal electrodes according to the first output result when the first output result is abnormal, and obtain the abnormal position based on the electrical mapping signals of all the abnormal electrodes; when the second output result is abnormal, obtain the target electrode according to the second output result, and generate a dynamic image according to the electrical mapping signals of the target electrode, and mark the abnormal position and the dynamic image in the mapping image.

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