A dynamic pulse monitoring and analysis system for cardiovascular diseases

Abstract

The present invention relates to the field of medical data monitoring technology, and in particular to a dynamic pulse monitoring and analysis system for cardiovascular diseases. The system utilizes a pulse big data analysis module to analyze the pulse information of all disease samples in a current large database. Based on the statistics of the pulse information, the pathological correlation degree of each pulse with the cardiovascular disease can be determined. By separately analyzing the pulse ratio information in two time periods and combining it with the pathological correlation degree, the cardiovascular monitoring attention of each patient can be quantified. Combining the elasticity decrease of the large arteries and the emergency performance of the small arteries can more effectively reflect the overall monitoring attention of each patient. Based on the overall monitoring attention, medical staff can focus on monitoring key patients. The present invention improves the efficiency of pulse monitoring and avoids delays in the condition of key patients.

Description

Technical Field

[0001] The present invention relates to the technical field of medical data monitoring, and in particular to a dynamic pulse monitoring and analysis system for cardiovascular diseases. Background Art

[0002] Cardiovascular disease is typically a chronic condition. Hospitals offer long-term health monitoring for elderly patients and other patients in need. Pulse monitoring is a crucial component of long-term monitoring, allowing for targeted monitoring of different conditions. Monitoring scenarios often involve multiple patients. To facilitate medical staff's understanding of pulse conditions, pulse data can be centrally displayed on a visualization panel. However, centrally monitoring pulses of a large number of patients and displaying the results can prevent medical staff from paying close attention to patients with underlying health conditions, impacting normal medical care. Summary of the Invention

[0003] In order to solve the technical problem that during long-term pulse monitoring, the patient sample is large and medical staff cannot pay attention to the target patient for key monitoring in time, the purpose of the present invention is to provide a dynamic pulse monitoring and analysis system for cardiovascular disease, and the technical solution adopted is as follows:

[0004] The present invention proposes a dynamic pulse monitoring and analysis system for cardiovascular disease, the system comprising:

[0005] The pulse condition big data analysis module is used to collect statistics on the pulse condition information of each disease sample in the big database, and obtain the pathological correlation degree of the pulse condition based on the distribution of each pulse condition in all disease samples and the correlation between pulse conditions of different disease samples;

[0006] The real-time pulse monitoring module is used to monitor the pulse data of the target patient during the day and night in real time and upload it to the pulse big data analysis module; based on the monitored pulse data, the daytime and nighttime proportions of each pulse condition of the patient are obtained, and combined with the pathological correlation degree of each pulse condition, the characteristic pulse development degree of the target patient during the day and night periods is obtained; based on the characteristic pulse development degree in the two periods, the cardiovascular monitoring attention level of the target patient is obtained;

[0007] The vascular elasticity monitoring module is used to obtain the elasticity reduction of the target patient's large arteries by monitoring PWV data;

[0008] The blood pressure monitoring module is used to obtain the target patient's arteriolar emergency performance by detecting AASI;

[0009] The monitoring and early warning module is used to obtain the overall monitoring attention of the target patient based on the target patient's cardiovascular monitoring attention, the degree of elasticity decline of the large arteries and the emergency performance of the small arteries, and to focus on monitoring the patient based on the overall monitoring attention.

[0010] Furthermore, the pulse big data analysis module includes a pulse distribution statistics unit, a pulse information matching unit and a pulse pathology correlation analysis unit;

[0011] The pulse condition distribution statistics unit is used to count the proportion of each pulse condition in all disease samples in the large database to obtain the etiology characteristic expression degree of each pulse condition; and count the proportion of each pulse condition in each disease sample to screen out characteristic pulse conditions in each disease sample;

[0012] The pulse information matching unit is used to match the characteristic pulse information between different disease samples, and obtain the pulse linkage analysis expression degree between the disease samples based on the similarity of the characteristic pulse type and the similarity of the characteristic pulse distribution; and calculate all the pulse linkage analysis expression degrees between the samples to which each characteristic pulse belongs to obtain the cardiovascular disease characteristic correlation degree of each pulse;

[0013] The pulse-pathology correlation analysis unit is used to obtain the pathology correlation degree of the pulse according to the etiology characteristic expression degree of each pulse and the cardiovascular disease characteristic correlation degree.

[0014] Furthermore, the method for obtaining the pulse condition linkage analysis performance includes:

[0015] For two matching disease samples, the intersection-and-union ratio of the characteristic pulse types between the two disease samples is calculated as the similarity of the characteristic pulse types; for the characteristic pulse types shared by the two disease samples, the distribution proportion of the shared characteristic pulse types in each disease sample is used as the distribution feature, and the initial distribution similarity of each shared characteristic pulse type between the two diseases is obtained based on the difference between the distribution features, and the average value of the initial distribution similarity of all shared characteristic pulse types is used as the similarity of the characteristic pulse type distribution; the product of the intersection-and-union ratio and the average value of the initial distribution similarity is used as the expression level of the pulse linkage analysis.

[0016] Furthermore, the method for obtaining the cardiovascular disease characteristic correlation degree includes:

[0017] All types of disease samples in the large database are matched with each other. If a certain pulse condition is a characteristic pulse condition in the matching process, the pulse condition linkage analysis expression degree obtained in the matching process is statistically calculated, and the cumulative values of all the pulse condition linkage analysis expression degrees obtained are normalized to obtain the cardiovascular disease characteristic correlation degree of the pulse condition.

[0018] Furthermore, the step of obtaining the characteristic pulse development degree of the target patient during the day and night time periods includes:

[0019] For the daytime period, the pathological correlation degree of each pulse condition is multiplied by the corresponding daytime proportion to obtain the development characteristics of each pulse condition during the daytime. The average value of the development characteristics of all pulse conditions of the target patient is taken as the characteristic pulse condition development degree during the daytime.

[0020] For the night time period, the characteristic pulse development degree at night is obtained based on the same method.

[0021] Furthermore, the method for obtaining cardiovascular monitoring attention includes:

[0022] The characteristic pulse development degree at night is used as the numerator, the characteristic pulse development degree during the day is used as the denominator, and the ratio is normalized to obtain the characteristic pulse trend coefficient;

[0023] The characteristic pulse development degree during the day is adjusted based on the characteristic pulse trend coefficient to obtain cardiovascular monitoring attention.

[0024] Furthermore, the method for obtaining the degree of elasticity reduction of the aorta includes:

[0025] Obtain a PWV data sequence of the target patient during the monitoring time period; subtract the previous element from the next element in the PWV data sequence, and use the ratio of the element difference to the previous element as the PWV increment ratio; use the proportion of positive PWV increment ratios as the positive increment influence; and use the product of the average value of the PWV increment ratios and the positive increment influence as the degree of decrease in aortic elasticity.

[0026] Furthermore, the method for obtaining the arteriolar emergency performance includes:

[0027] Obtaining the target patient's AASI value, comparing the AASI value with a preset threshold value; if the AASI value is greater than the preset threshold value, normalizing the ratio of the AASI value to the preset threshold value to obtain the target patient's arteriolar function detachment degree; if the AASI value is not greater than the preset threshold value, setting the target patient's arteriolar function detachment degree to 0; using the proportion of time during a preset monitoring period when the target patient's AASI value is greater than the preset threshold value as the arteriolar function risk degree; and increasing the arteriolar function risk degree based on the arteriolar function detachment degree to obtain the arteriolar emergency performance degree.

[0028] Furthermore, the method for obtaining the overall monitoring attention includes:

[0029] The target patient's cardiovascular monitoring attention is used as the weight of the large artery elasticity decrease, and the negative correlation mapping result of the cardiovascular monitoring attention is used as the weight of the small artery emergency performance. The large artery elasticity decrease and the small artery emergency performance are weighted and summed to obtain the overall monitoring attention.

[0030] Furthermore, the focusing on monitoring the patient according to the overall monitoring attention includes:

[0031] In the visualization panel, the patient monitoring data is sorted and displayed according to the overall monitoring attention.

[0032] The present invention has the following beneficial effects:

[0033] The present invention first utilizes the pulse big data analysis module to analyze the pulse information of all disease samples in the current large database. Based on the statistics of the pulse information, the pathological correlation degree of each pulse for cardiovascular disease can be determined. The importance of each pulse in the monitoring process can be quantified by the pathological correlation degree. The present invention further takes into account the different mental states of patients during the day and at night, and the pulse data caused by them need to be analyzed separately. Therefore, by analyzing the pulse ratio information in the two time periods separately, the cardiovascular monitoring attention of each patient can be quantified in combination with the pathological correlation degree. That is, the cardiovascular monitoring attention represents the attention degree of the pulse presented by the patient. Thus, the overall monitoring attention degree of each patient can be more effectively reflected in combination with the elasticity decrease of the large arteries and the emergency performance of the small arteries. Based on the overall monitoring attention degree, medical staff can focus on monitoring key patients, thereby improving the efficiency of pulse monitoring and avoiding delays in the condition of key patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 A block diagram of a dynamic pulse monitoring and analysis system for cardiovascular disease provided by one embodiment of the present invention;

[0036] Figure 2 A pulse condition curve diagram provided by one embodiment of the present invention;

[0037] Figure 3 A pulse big data analysis module structure diagram provided by one embodiment of the present invention;

[0038] Figure 4A pulse statistics diagram of a disease sample provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to further elaborate the technical means and the effect that the present invention is taken for reaching predetermined object of the invention, below in conjunction with accompanying drawing and preferred embodiment, to the dynamic pulse monitoring and analysis system of a kind of cardiovascular disease that proposes according to the present invention, its specific embodiment, structure, feature and effect thereof, are described in detail as follows.In the following description, different " embodiment " or " another embodiment " refer to not necessarily the same embodiment.In addition, the specific features, structure or characteristics in one or more embodiments can be by any suitable form combination.

[0040] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0041] The specific scheme of the dynamic pulse monitoring and analysis system for cardiovascular disease provided by the present invention is described in detail below in conjunction with the accompanying drawings.

[0042] See also Figure 1 , which shows a block diagram of a dynamic pulse monitoring and analysis system for cardiovascular diseases provided by an embodiment of the present invention. The system includes a pulse big data analysis module 101, a pulse real-time monitoring module 102, a vascular elasticity monitoring module 103, a blood pressure monitoring module 104, and a monitoring and early warning module 105.

[0043] The pulse big data analysis module 101 is used to count the pulse information of each disease sample in the big database. Wherein the big database contains the pulse information of multiple confirmed patient samples. Including information such as pulse type and pulse frequency, and by monitoring the patient's pulse in real time, the pulse data monitored in real time can be stored in the big database to realize the update of the big database. The embodiment of the present invention can monitor the patient's pulse data based on the intelligent pulse diagnosis bracelet. The bracelet is worn on the radial artery of the patient's wrist. The flexible pressure sensor array of lactone and infrared positioning technology automatically calibrate the three parts of Cun, Guan and Chi, and can dynamically obtain the patient's pulse data by applying floating, middle and sinking three-stage pressure through the gradient of the micro-stepping motor. After data collection and uploading of the bracelet, data such as pulse frequency data, pulse graph parameters, pulse waveform characteristics, pulse wave propagation parameters, etc. can be obtained. The specific examples of some pulse information are as follows:

[0044] (1) Pulse frequency data: such as stringy pulse, thin pulse, slippery pulse, deep pulse, etc. The frequency and proportion of these pulse patterns are often used to analyze the pulse characteristics of patients with cardiovascular diseases;

[0045] (2) Pulse parameters: Pulse parameters are an important part of TCM pulse diagnosis, including amplitude (such as h1, h3 / h1), time value (such as w1, t1 / t4), area (such as Ad, As), etc. These parameters can reflect the functional status of the cardiovascular system;

[0046] (3) Pulse waveform characteristics: including the amplitude, speed, period, shape, etc. of the pulse wave. Figure 2 As shown, it shows a pulse condition curve diagram provided by one embodiment of the present invention, Figure 2 The curve in the figure corresponds to the curve of the string pulse, which is more common in patients with coronary heart disease and usually reflects the characteristics of arteriosclerosis, increased peripheral resistance and decreased vascular elasticity.

[0047] (4) Pulse wave propagation parameters: such as pulse wave velocity (PWV), arterial stiffness index (AASI), etc. These parameters can reflect changes in vascular elasticity and hemodynamics.

[0048] It should be noted that, in the embodiment of the present invention, in order to quantify the importance of monitoring the patient, the pulse information specifically used is mainly the type of pulse and the distribution information of each pulse in the sample.

[0049] Different pulse conditions reflect different physiological and pathological changes. Similarly, different diseases also correspond to different pulse condition distributions. Therefore, in order to effectively quantify the monitoring attention of patients, it is first necessary to determine the degree of association between each pulse condition and each disease. Common cardiovascular diseases such as coronary heart disease, myocarditis, heart failure, etc., because the pulse condition data of confirmed patients are stored in the big database, the pulse condition information of each disease sample in the big database can be counted. According to the distribution of each pulse condition in all disease samples and the correlation between pulse conditions of different disease samples, the pathological correlation degree of the pulse condition can be obtained. That is, for a pulse condition, if it appears more in all disease samples, it means that the pulse condition is a representative pulse condition of cardiovascular disease and its pathological correlation degree is high; and after performing correlation analysis between different disease samples, it is determined that the pulse condition appears more distributed among different diseases, which means that its pathological correlation degree is high. Therefore, the pathological correlation degree of each pulse condition can be determined by the pulse condition big data analysis module 101 to characterize the important attention degree of the pulse condition in the field of cardiovascular disease.

[0050] Preferably, see Figure 3 , which shows a structural block diagram of a pulse big data analysis module provided by an embodiment of the present invention. In one embodiment of the present invention, the pulse big data analysis module mainly includes a pulse distribution statistics unit 201, a pulse information matching unit 202 and a pulse pathology correlation analysis unit 203.

[0051] Wherein the pulse distribution statistics unit 201 is used to count the proportion of each pulse condition in all disease samples in the large database, and obtain the etiology characteristic expression of each pulse condition. That is, the ratio of the number of occurrences of each pulse condition in all samples to the total number of pulse conditions in all samples is used as the etiology characteristic expression. The greater the etiology characteristic expression, the more representative the pulse condition is in cardiovascular disease. Further considering that the patient will obtain a variety of pulse conditions during the pulse monitoring process, some of which have fewer occurrences and do not belong to the significant pulse conditions under the disease, the occurrence proportion of each pulse condition in each disease sample is counted, and the characteristic pulse condition is screened out in each disease sample. That is, the characteristic pulse condition is the pulse condition that appears more frequently under a disease. In the embodiment of the present invention, for each disease, the first four pulse conditions with the highest occurrence proportion are selected as the characteristic pulse conditions. In other embodiments, a threshold value for the occurrence proportion can also be set, and the pulse condition with a threshold value greater than the occurrence proportion threshold can be used as the characteristic pulse condition. Please refer to Figure 4 , which shows a pulse statistics diagram of a disease sample provided by an embodiment of the present invention, Figure 4 This is a pulse distribution result chart obtained from a large number of samples of coronary disease patients. As shown in the figure, the first four pulse patterns have a significantly larger distribution than other pulse patterns. Therefore, the first four pulse patterns can be used as characteristic pulse patterns of coronary disease samples, which facilitates subsequent matching correlation analysis between different symptoms.

[0052] Pulse information matching unit 202 is used for matching the characteristic pulse information between different disease samples, because there is interaction between the cardiovascular diseases of different diseases, the phenomenon of mutual influence, a disease may be the risk factor or the complication of another disease, for example hypertension is the important risk factor of coronary heart disease, long-term hypertension can increase the weight of cardiac burden, cause heart wall thickening and arteriosclerosis, and can increase the burden of coronary artery, cause vascular inner wall to be damaged, form atherosclerotic plaque, thereby increase the risk of occurrence of coronary heart disease, so hypertension and coronary heart disease can have certain similar performance on pulse.Based on this feature, can be matched association analysis between different diseases in big database, can obtain the pulse linkage analysis expression degree between disease samples based on the similarity of the characteristic pulse kind between two diseases of matching and the similarity of characteristic pulse distribution.That is, the characteristic pulse kind between two diseases is more similar, and distribution proportion is also comparatively similar, illustrates that two diseases have obvious correlation characteristics, then the pulse linkage analysis expression degree is larger.

[0053] For a pulse condition, if the pulse condition linkage analysis expression between the disease it belongs to and other diseases is large, and it belongs to more diseases, it means that the pulse condition has a strong correlation with cardiovascular disease. Therefore, the pulse condition information matching unit 202 further counts all the pulse condition linkage analysis expression between the samples belonging to each characteristic pulse condition to obtain the cardiovascular disease characteristic correlation of each pulse condition.

[0054] The pulse-pathology correlation analysis unit 203 can obtain the pathological correlation degree of the pulse condition based on the etiology characteristic expression degree and the cardiovascular disease characteristic correlation degree of each pulse condition. In the embodiment of the present invention, because the product of the quantized etiology characteristic expression degree and the cardiovascular disease characteristic correlation degree can be directly used as the pathological correlation degree of each pulse condition, that is, the two characteristics are positively correlated by the product, the greater the etiology characteristic expression degree and the greater the cardiovascular disease characteristic correlation degree, the greater the pathological correlation degree of the pulse condition.

[0055] Furthermore, in one embodiment of the present invention, the method for obtaining the pulse condition linkage analysis performance includes:

[0056] For two matching disease samples, the intersection-and-union ratio of the characteristic pulse types between the two disease samples is calculated as the similarity of the characteristic pulse types. The larger the intersection-and-union ratio, the more similar the characteristic pulse types between the two disease samples are, that is, the greater the similarity of the types.

[0057] For the shared characteristic pulse pattern between two disease samples, the distribution ratio of shared characteristic pulse pattern in every disease sample is used as distribution characteristic, and the initial distribution similarity of each shared characteristic pulse pattern between two diseases is obtained according to the difference between the distribution characteristic. Because there is an initial distribution similarity for each shared characteristic pulse pattern, there are a plurality of shared characteristic pulse patterns between two diseases, so the mean value of the initial distribution similarity of all shared characteristic pulse patterns is used as the similarity of characteristic pulse pattern distribution. In an embodiment of the present invention, the initial distribution similarity is the ratio between the two distribution characteristics, wherein the denominator is a larger value, and the closer this ratio is to 1, the closer the distribution characteristic of the shared characteristic pulse pattern is, and then the initial distribution similarity is larger.

[0058] The product of the intersection-over-union ratio and the average value of the initial distribution similarity is taken as the performance of pulse-condition linkage analysis.

[0059] Furthermore, in an embodiment of the present invention, a method for obtaining the cardiovascular disease feature correlation degree includes:

[0060] All types of disease samples in the large database are matched with each other. If a certain pulse condition is a characteristic pulse condition in the matching process, the pulse condition linkage analysis expression degree obtained in the matching process is statistically calculated, and the cumulative values of all the pulse condition linkage analysis expression degrees obtained are normalized to obtain the cardiovascular disease characteristic correlation degree of the pulse condition.

[0061] It should be noted that the normalization methods in the embodiments of the present invention all adopt the range normalization method, that is, the maximum and minimum values are counted in their respective dimensions, and the maximum and minimum values are used to normalize each data. The specific method is a technical means well known to those skilled in the art and will not be elaborated here.

[0062] Pulse real-time monitoring module 102 is used for real-time monitoring of the pulse of target patient.The embodiment of the present invention takes into account in the dynamic monitoring process, and daytime monitoring is easily subject to the influence of multiple activities such as motion, pressure, diet, and these influences can change pulse information, and then cover the early signal of cardiovascular health.And during night monitoring, the patient is in resting state usually, and other influencing factors are relatively small, therefore help to reflect the true state of patient's cardiovascular function, so the pulse real-time monitoring module is divided into daytime and night with the real-time monitoring period.It should be noted that, when the information collected daytime and night was carried out joint analysis in the embodiment of the present invention, what selected was the data between daytime and the night of the previous day.

[0063] The real-time pulse monitoring module 102 can obtain the daytime and nighttime proportions of each pulse condition of the patient based on the monitored pulse data. For a patient, the more proportions of pulse conditions monitored in a time period, and the greater the pathological correlation degree of the pulse condition, the more obvious the symptoms of the target patient's pulse condition development in the current time period. Therefore, the pathological correlation degree of each pulse condition can be combined to obtain the characteristic pulse condition development degree of the target patient in both the daytime and nighttime time periods. The characteristic pulse condition development degrees of the two time periods are then jointly analyzed to determine the target patient's cardiovascular monitoring attention on that day.

[0064] Preferably, in an embodiment of the present invention, obtaining the characteristic pulse development degree of the target patient under two time periods of day and night includes:

[0065] For the daytime period, the pathological correlation degree of each pulse condition is multiplied by the corresponding daytime proportion to obtain the development characteristics of each pulse condition during the day, and the average value of the development characteristics of all pulse conditions of the target patient is used as the characteristic pulse condition development degree during the day.

[0066] For the night time period, the characteristic pulse development degree at night is obtained based on the same method.

[0067] Preferably, in an embodiment of the present invention, the method for acquiring cardiovascular monitoring attention includes:

[0068] Since the night can better reflect the relevant manifestations of the patient's cardiovascular disease than the day, when the patient's pulse is in a more obviously abnormal state at night, it means that it may contain more accurate, real and undisturbed instantaneous pulse characteristics corresponding to incidental cardiovascular events (such as atrial fibrillation, myocardial ischemia, etc.).

[0069] Therefore, the characteristic pulse development degree at night is used as the numerator, the characteristic pulse development degree during the day is used as the denominator, and the ratio is normalized to obtain the characteristic pulse trend coefficient. That is, the larger the characteristic pulse trend coefficient is, the more urgent the characteristic pulse development degree at night is, and the more important the patient's pulse information is, the more important it is to monitor. Therefore, the characteristic pulse trend coefficient can be used as a weight to adjust the characteristic pulse development degree during the day, so that cardiovascular monitoring attention can be obtained during the day and medical staff can be effectively reminded to pay close attention. Based on the characteristic pulse trend coefficient, the characteristic pulse development degree during the day is increased to obtain the cardiovascular monitoring attention. It should be noted that, because the characteristic pulse trend coefficient has been normalized, the characteristic pulse trend coefficient can be added to the positive integer 1 as the increase coefficient, and the increase process can be realized by directly multiplying the increase coefficient with the characteristic pulse development degree during the day.

[0070] Pulse wave velocity (PWV) quantifies vascular stiffness from the perspective of large artery elasticity and is suitable for assessing systemic arteriosclerosis. The arterial stiffness index (AASI) quantifies vascular stiffness from the perspective of small artery function. It reflects arteriolar and microcirculatory function and is more sensitive to early vascular dysregulation. Therefore, these two data can be used to assess the hemodynamic status of a patient's vascular system, thereby obtaining a more accurate overall monitoring focus. Therefore, the vascular elasticity monitoring module 103 monitors PWV data to determine the degree of elasticity loss in the target patient's large arteries; the blood pressure monitoring module 104 measures the AASI to determine the target patient's arteriolar stress response. The monitoring and early warning module 105 determines the target patient's overall monitoring focus based on the target patient's cardiovascular monitoring focus, the degree of elasticity loss in the large arteries, and the arteriolar stress response. The patient is then monitored based on this overall monitoring focus.

[0071] Preferably, the embodiments of the present invention take into account the negative correlation between WV and arterial wall elasticity. Lower elasticity indicates higher vascular stiffness and faster PWV. Accelerated PWV leads to a sustained increase in left ventricular afterload, which can lead to left ventricular hypertrophy in the long term and increase the risk of heart failure. Therefore, the embodiments of the present invention set a longer monitoring period to obtain a PWV data sequence for the target patient within the monitoring period. In this embodiment of the present invention, the monitoring period is set to one week, and each element in the PWV data sequence represents one day. That is, for the real-time monitoring process of a patient, the data from the week before the patient's current day is selected to form the PWV data sequence.

[0072] Subtract the previous element from the next element in the PWV data sequence, and take the ratio of the difference between the elements and the previous element as the PWV increment ratio. It should be noted that because it is an element difference, the PWV increment ratio has positive and negative cases. Negative values represent negative increments, and positive values represent positive increments. If there are more positive increments in the PWV increment ratio, and the larger the value, the greater the risk of the patient being ill, and the greater the decrease in the elasticity of the large arteries. It is necessary to focus on monitoring. Therefore, the proportion of positive PWV increment ratios is taken as the positive increment influence, and the product of the average value of the PWV increment ratio and the positive increment influence is taken as the decrease in the elasticity of the large arteries.

[0073] Preferably, in an embodiment of the present invention, the method for obtaining the arteriolar emergency performance includes:

[0074] Similar to the PWV data described above, a monitoring period is also preset. The AASI value for the target patient is obtained daily during the monitoring period and compared with a preset threshold. A larger AASI value indicates a more severe emergency situation for the corresponding cardiovascular event. In this embodiment of the present invention, the threshold is set to 0.7.

[0075] If it is greater than the preset threshold, the ratio of the AASI value to the preset threshold is normalized to obtain the arteriolar detachment degree of the target patient.

[0076] If it is not greater than the preset threshold, the arteriolar detachment degree of the target patient is set to 0.

[0077] The proportion of time during which the target patient's AASI value is greater than a preset threshold during a preset monitoring period is used as the arteriolar function risk score. The greater the arteriolar function risk score and the arteriolar function detachment degree, the more urgent the patient's arteriolar condition during the current monitoring period. The arteriolar function risk score is increased based on the arteriolar function detachment degree to obtain the arteriolar emergency performance score. In this embodiment of the present invention, the method for increasing the arteriolar function risk score is the same as the method for increasing the characteristic pulse development degree. Because the arteriolar function detachment degree is normalized data, it is added to the positive integer 1 and then multiplied by the arteriolar function risk score to obtain the arteriolar emergency performance score.

[0078] Preferably, in the embodiment of the present invention, considering that the pulse wave velocity (PWV) and arterial stiffness index (AASI) are equivalent to the patient's hemodynamic performance from the perspective of systemic arterial and arteriolar microcirculation, and both are analyzed based on the perspectives of recent sustained performance and current emergency situation performance, respectively, each should be analyzed separately. Therefore, the method for obtaining the overall monitoring attention includes:

[0079] Based on the target patient's cardiovascular monitoring attention level, a higher level of cardiovascular monitoring attention indicates a greater need for attention to the patient's large artery function, reflecting both their overall and recent sustained hemodynamic performance. Conversely, a lower level of cardiovascular monitoring attention indicates a greater need for attention to small artery function, reflecting early vascular abnormalities from the perspective of small artery microcirculation. Therefore, the target patient's cardiovascular monitoring attention level is used as the weight for the degree of decreased large artery elasticity, and the negative correlation mapping result of cardiovascular monitoring attention is used as the weight for the degree of small artery emergency response. The weighted sum of the degree of decreased large artery elasticity and the degree of small artery emergency response is then taken to obtain the overall monitoring attention level.

[0080] Preferably, in an embodiment of the present invention, the patient's monitoring data is sorted and displayed in the visualization panel according to the overall monitoring attention. The sorted display enables the monitoring center to pay more attention to patients with greater cardiovascular disease risks, thereby improving monitoring efficiency.

[0081] In summary, in the embodiment of the present invention, the pulse big data analysis module is first utilized to analyze the pulse information of all disease samples in the current large database. Based on the statistics of the pulse information, the pathological correlation degree of each pulse for cardiovascular disease can be determined. The pulse ratio information in two time periods is analyzed separately, and the cardiovascular monitoring attention of each patient can be quantified in combination with the pathological correlation degree. The overall monitoring attention of each patient is more effectively reflected by combining the elasticity decrease of the large arteries and the emergency performance of the small arteries. Based on the overall monitoring attention, medical staff can focus on monitoring key patients. The present invention improves the efficiency of pulse monitoring and avoids delays in the condition of key patients.

[0082] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0083] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A dynamic pulse monitoring and analysis system for cardiovascular disease, characterized in that: The system comprises: The pulse condition big data analysis module is used to collect statistics on the pulse condition information of each disease sample in the big database, and obtain the pathological correlation degree of the pulse condition based on the distribution of each pulse condition in all disease samples and the correlation between pulse conditions of different disease samples; The real-time pulse monitoring module is used to monitor the pulse data of the target patient during the day and night in real time and upload it to the pulse big data analysis module; based on the monitored pulse data, the daytime and nighttime proportions of each pulse condition of the patient are obtained, and combined with the pathological correlation degree of each pulse condition, the characteristic pulse development degree of the target patient during the day and night periods is obtained; based on the characteristic pulse development degree in the two periods, the cardiovascular monitoring attention level of the target patient is obtained; The vascular elasticity monitoring module is used to obtain the elasticity reduction of the target patient's large arteries by monitoring PWV data; The blood pressure monitoring module is used to obtain the target patient's arteriolar emergency performance by detecting AASI; The monitoring and early warning module is used to obtain the overall monitoring attention of the target patient based on the target patient's cardiovascular monitoring attention, the degree of elasticity decline of the large arteries and the emergency performance of the small arteries, and to focus on monitoring the patient based on the overall monitoring attention.

2. A dynamic pulse monitoring and analysis system for cardiovascular disease according to claim 1, characterized in that: The pulse big data analysis module includes a pulse distribution statistics unit, a pulse information matching unit and a pulse pathology correlation analysis unit; The pulse condition distribution statistics unit is used to count the proportion of each pulse condition in all disease samples in the large database to obtain the etiology characteristic expression degree of each pulse condition; And count the occurrence ratio of each pulse condition in each disease sample, and screen out the characteristic pulse condition in each disease sample; The pulse information matching unit is used to match the characteristic pulse information between different disease samples, and obtain the pulse linkage analysis expression degree between the disease samples based on the similarity of the characteristic pulse type and the similarity of the characteristic pulse distribution; and calculate all the pulse linkage analysis expression degrees between the samples to which each characteristic pulse belongs to obtain the cardiovascular disease characteristic correlation degree of each pulse; The pulse-pathology correlation analysis unit is used to obtain the pathology correlation degree of the pulse according to the etiology characteristic expression degree of each pulse and the cardiovascular disease characteristic correlation degree.

3. A dynamic pulse monitoring and analysis system for cardiovascular disease according to claim 2, characterized in that: The method for obtaining the pulse condition linkage analysis expression degree includes: For two matching disease samples, the intersection-and-union ratio of the characteristic pulse types between the two disease samples is calculated as the similarity of the characteristic pulse types; for the characteristic pulse types shared by the two disease samples, the distribution proportion of the shared characteristic pulse types in each disease sample is used as the distribution feature, and the initial distribution similarity of each shared characteristic pulse type between the two diseases is obtained based on the difference between the distribution features, and the average value of the initial distribution similarity of all shared characteristic pulse types is used as the similarity of the characteristic pulse type distribution; the product of the intersection-and-union ratio and the average value of the initial distribution similarity is used as the expression level of the pulse linkage analysis.

4. A dynamic pulse monitoring and analysis system for cardiovascular disease according to claim 2, characterized in that: The method for obtaining the cardiovascular disease characteristic correlation degree includes: All types of disease samples in the large database are matched with each other. If a certain pulse condition is a characteristic pulse condition in the matching process, the pulse condition linkage analysis expression degree obtained in the matching process is statistically calculated, and the cumulative values of all the pulse condition linkage analysis expression degrees obtained are normalized to obtain the cardiovascular disease characteristic correlation degree of the pulse condition.

5. A dynamic pulse monitoring and analysis system for cardiovascular disease according to claim 1, characterized in that: The method of obtaining the characteristic pulse development degree of the target patient during the day and night time periods includes: For the daytime period, the pathological correlation degree of each pulse condition is multiplied by the corresponding daytime proportion to obtain the development characteristics of each pulse condition during the daytime. The average value of the development characteristics of all pulse conditions of the target patient is taken as the characteristic pulse condition development degree during the daytime. For the night time period, the characteristic pulse development degree at night is obtained based on the same method.

6. A dynamic pulse monitoring and analysis system for cardiovascular disease according to claim 1, characterized in that: The method for obtaining the cardiovascular monitoring attention includes: The characteristic pulse development degree at night is used as the numerator, the characteristic pulse development degree during the day is used as the denominator, and the ratio is normalized to obtain the characteristic pulse trend coefficient; The characteristic pulse development degree during the day is adjusted based on the characteristic pulse trend coefficient to obtain cardiovascular monitoring attention.

7. A dynamic pulse monitoring and analysis system for cardiovascular disease according to claim 1, characterized in that: The method for obtaining the degree of elasticity reduction of the aorta includes: Obtain a PWV data sequence of the target patient during the monitoring time period; subtract the previous element from the next element in the PWV data sequence, and use the ratio of the element difference to the previous element as the PWV increment ratio; use the proportion of positive PWV increment ratios as the positive increment influence; and use the product of the average value of the PWV increment ratios and the positive increment influence as the degree of decrease in aortic elasticity.

8. A dynamic pulse monitoring and analysis system for cardiovascular disease according to claim 1, characterized in that: The method for obtaining the arteriolar emergency performance comprises: Obtaining the target patient's AASI value, comparing the AASI value with a preset threshold value; if the AASI value is greater than the preset threshold value, normalizing the ratio of the AASI value to the preset threshold value to obtain the target patient's arteriolar function detachment degree; if the AASI value is not greater than the preset threshold value, setting the target patient's arteriolar function detachment degree to 0; using the proportion of time during a preset monitoring period when the target patient's AASI value is greater than the preset threshold value as the arteriolar function risk degree; and increasing the arteriolar function risk degree based on the arteriolar function detachment degree to obtain the arteriolar emergency performance degree.

9. A dynamic pulse monitoring and analysis system for cardiovascular disease according to claim 1, characterized in that: The method for obtaining the overall monitoring attention includes: The target patient's cardiovascular monitoring attention is used as the weight of the large artery elasticity decrease, and the negative correlation mapping result of the cardiovascular monitoring attention is used as the weight of the small artery emergency performance. The large artery elasticity decrease and the small artery emergency performance are weighted and summed to obtain the overall monitoring attention.

10. A dynamic pulse monitoring and analysis system for cardiovascular disease according to claim 1, characterized in that: The focused monitoring of the patient according to the overall monitoring focus includes: In the visualization panel, the patient monitoring data is sorted and displayed according to the overall monitoring attention.

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