Efficient data transmission system of traditional Chinese medicine cardiopulmonary rehabilitation based on remote monitoring

Abstract

The present invention relates to the technical field of medical data transmission, and in particular to an efficient transmission system for traditional Chinese medicine cardiopulmonary rehabilitation data based on remote monitoring. The system comprises: a data acquisition module, which is used to obtain a first sequence and a second sequence corresponding to heart rate data and blood oxygen saturation data, respectively; a suspected abnormal point acquisition module, which is used to obtain a fluctuation area, and calculate the suspected abnormal characteristic values ​​of the data points in the fluctuation area, and then obtain the suspected abnormal point; an associated characteristic value acquisition module, which is used to calculate the change characteristic value of the suspected abnormal point according to the difference between the suspected abnormal point and the adjacent data points before and after; and obtain the associated change characteristic value of the suspected abnormal point according to the second sequence, and then obtain the associated characteristic value; an abnormal point acquisition module, which is used to calculate the abnormal degree of the suspected abnormal point, and then obtain the abnormal point; an abnormal processing module, which processes and saves the abnormal points in the first sequence and the second sequence. The present invention can obtain accurate cardiopulmonary rehabilitation data.

Description

Technical Field

[0001] The present invention relates to the technical field of medical data transmission, and in particular to a remote monitoring-based efficient transmission system for traditional Chinese medicine cardiopulmonary rehabilitation data. Background Art

[0002] For most patients who require long-term monitoring and personalized intervention, especially those who cannot go to the hospital for follow-up visits frequently, live in remote areas or have chronic cardiopulmonary diseases, the use of remote monitoring systems for patients and the collection of cardiopulmonary rehabilitation data for remote transmission and analysis are of great significance in improving treatment outcomes, reducing waste of medical resources and improving patients' quality of life.

[0003] Existing technologies usually use wearable devices to collect patients' cardiopulmonary rehabilitation data and establish a connection between patients and hospital terminals based on wireless communication technologies (such as Wi-Fi, 5G, etc.). However, during the communication transmission process, there are various interference noises, which can easily interfere with the data and cause partial data distortion, reducing the accuracy of the patient's cardiopulmonary rehabilitation data transmission. Summary of the invention

[0004] In order to solve the technical problem of abnormal points in the above-mentioned cardiopulmonary rehabilitation data, the purpose of the present invention is to provide a high-efficiency transmission system for traditional Chinese medicine cardiopulmonary rehabilitation data based on remote monitoring. The technical solution adopted is as follows:

[0005] An embodiment of the present invention provides a remote monitoring-based efficient transmission system for TCM cardiopulmonary rehabilitation data, the system comprising: a data acquisition module for acquiring heart rate data and blood oxygen saturation data within an acquisition period, forming a first sequence and a second sequence respectively;

[0006] A suspected abnormal point acquisition module is used to record the area between the trough points on both sides of the peak point in the first sequence as the fluctuation area of ​​the peak point; calculate the suspected abnormal feature value of the data point in the fluctuation area according to the number of times the peak point appears in the first sequence, the average value of the first sequence and the data points in the fluctuation area; and obtain the suspected abnormal point based on the suspected abnormal feature value;

[0007] The associated characteristic value acquisition module is used to calculate the change characteristic value of the suspected abnormal point according to the difference between the suspected abnormal point and the adjacent data points before and after; calculate the change characteristic value of the data point at the same time as the suspected abnormal point in the second sequence, and record it as the associated change characteristic value of the suspected abnormal point; calculate the associated characteristic value according to the change characteristic value of the suspected abnormal point and the associated change characteristic value;

[0008] An abnormal point acquisition module is used to calculate the abnormal degree of the suspected abnormal point according to the suspected abnormal feature value and the associated feature value of the suspected abnormal point; screen the suspected abnormal point according to the abnormal degree to obtain the abnormal point, and similarly obtain the abnormal points in the second sequence;

[0009] The exception processing module processes the abnormal points in the first sequence and the second sequence, and saves the processed first sequence and the second sequence.

[0010] Preferably, the calculation formula for the suspected abnormal characteristic value of the data point in the fluctuation area is:

[0011] ,

[0012] in, Represents the suspected abnormal characteristic value of the data point in the qth fluctuation area in the first sequence; represents the data value of the qth peak point in the first sequence; represents the mean of the data in the first series; Represents the number of data points between the qth peak point and the left trough point in the first sequence; Represents the value of the i-th data point among the data points between the q-th peak point and the left trough point in the first sequence; Represents the value of the i-1th data point among the data points between the qth peak point and the left trough point in the first sequence; Represents the number of data points between the qth peak point and the right trough point in the first sequence; Represents the value of the jth data point between the qth peak point and the right trough point in the first sequence; Represents the value of the j-1th data point between the qth peak point and the right trough point in the first sequence; Represents the number of data points in the first sequence that have the same value as the qth peak point.

[0013] Preferably, obtaining suspected abnormal points based on suspected abnormal feature values ​​includes:

[0014] A first threshold is set, and the suspected abnormal feature value of the data point in the fluctuation area is normalized; if the normalized suspected abnormal feature value of the data point in the fluctuation area is greater than or equal to the first threshold, the data point in the fluctuation area is a suspected abnormal point.

[0015] Preferably, the calculation formula for the change characteristic value of the suspected abnormal point is:

[0016] ,

[0017] in, Indicates the change characteristic value of the pth suspected abnormal point in the first sequence; Represents the data value of the pth suspected outlier in the first sequence; Represents the data value of the left adjacent data point of the pth suspected abnormal point in the first sequence; Represents the data value of the right adjacent data point of the pth suspected abnormal point in the first sequence; and Represent the maximum and minimum values ​​in the first sequence respectively.

[0018] Preferably, calculating the associated characteristic value according to the change characteristic value of the suspected abnormal point and the associated change characteristic value includes:

[0019] The absolute value of the difference between the change characteristic value of the suspected outlier point and the associated change characteristic value is calculated, and the reciprocal of the absolute value is calculated to obtain the associated characteristic value of the suspected outlier point.

[0020] Preferably, calculating the abnormality degree of the suspected abnormal point according to the suspected abnormal feature value and the associated feature value of the suspected abnormal point includes:

[0021] Normalize the suspected abnormal feature values ​​of the suspected abnormal points; use an exponential function with a natural constant as the base to negatively map the associated feature values ​​of the suspected abnormal points to obtain a mapping result; the average of the mapping result and the normalized suspected abnormal feature value sum is the abnormal degree of the suspected abnormal point.

[0022] Preferably, the suspected abnormal points are screened according to the abnormality degree to obtain the abnormal points, including:

[0023] A second threshold is set, and if the abnormality degree of the suspected abnormal point in the first sequence is greater than or equal to the abnormality degree, the suspected abnormal point is an abnormal point.

[0024] Preferably, processing the outliers in the first sequence and the second sequence includes:

[0025] The value of the outlier point is replaced by the mean of the adjacent data points before and after the outlier point.

[0026] The embodiments of the present invention have at least the following beneficial effects: the present invention obtains heart rate data and blood oxygen saturation data, respectively forming a first sequence and a second sequence, and then obtains a fluctuation area, obtains suspected abnormal characteristic values ​​of data points in the fluctuation area according to the distribution characteristics of the peak points in the fluctuation area, the data points in the fluctuation area, and the data points in the entire first sequence, and then obtains suspected abnormal points; further, based on the first sequence, the change characteristic values ​​of the suspected abnormal points are calculated, and then the associated change characteristic values ​​of the suspected abnormal points are calculated based on the second sequence composed of blood oxygen saturation data, and the associated change characteristic values ​​of the suspected abnormal points are combined with the suspected abnormal characteristic values ​​to obtain the abnormal degree of the suspected abnormal points, and obtain accurate abnormal points. Here, the high correlation between the effects of cardiopulmonary diseases on heart rate and blood oxygen saturation is used to further accurately verify whether the suspected abnormal data points are formed by signal interference, which can improve the accuracy of abnormal point acquisition in cardiopulmonary rehabilitation data; then the abnormal points in the cardiopulmonary rehabilitation data are compensated and corrected to obtain cardiopulmonary rehabilitation transmission remote data with higher accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. 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 creative work.

[0028] Figure 1 A system block diagram of a remote monitoring-based efficient transmission system for traditional Chinese medicine cardiopulmonary rehabilitation data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation method, structure, characteristics and effects of a remote monitoring-based traditional Chinese medicine cardiopulmonary rehabilitation data efficient transmission system proposed by the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics in one or more embodiments may be combined in any suitable form.

[0030] 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.

[0031] The specific scheme of a remote monitoring-based efficient transmission system for traditional Chinese medicine cardiopulmonary rehabilitation data provided by the present invention is described in detail below with reference to the accompanying drawings.

[0032] Example:

[0033] The main application scenarios of the present invention are:

[0034] For patients with cardiopulmonary diseases who are unable to move or require long-term monitoring, their cardiopulmonary rehabilitation-related data is collected through wearable devices and remotely transmitted to hospital terminals. Due to various signal interferences during the transmission process, the transmitted data is distorted, and the data needs to be cleaned after transmission to ensure that there is no signal interference noise data in the data. This application combines the distribution difference characteristics of cardiopulmonary rehabilitation data and signal interference abnormal data and the correlation characteristics of cardiopulmonary data to reduce the defects of data distortion in the signal transmission of cardiopulmonary data.

[0035] See also Figure 1 , which shows a system block diagram of a remote monitoring-based efficient transmission system for traditional Chinese medicine cardiopulmonary rehabilitation data provided by an embodiment of the present invention, the system includes the following modules:

[0036] The data acquisition module is used to acquire heart rate data and blood oxygen saturation data within an acquisition period, forming a first sequence and a second sequence respectively.

[0037] For patients whose cardiopulmonary activities need to be monitored, their cardiopulmonary health data needs to be monitored in real time. In the embodiment of the present invention, the cardiopulmonary health data includes heart rate data and blood oxygen saturation data.

[0038] The acquisition cycle is set. In the embodiment of the present application, the acquisition cycle is 30 minutes. In one acquisition cycle, the acquisition frequency is set. Preferably, the acquisition frequency in the embodiment of the present invention is 1 time per second. The heart rate data in each acquisition cycle constitutes a sequence, recorded as the first sequence, and the blood oxygen saturation data constitutes a sequence, recorded as the second sequence. The collected cardiopulmonary health data is transmitted once at the end of each acquisition cycle. After the terminal receives the data, it needs to perform further cleaning processing, and then the patient's data is stored in the database corresponding to the patient in the database.

[0039] The suspected abnormal point acquisition module is used to record the area between the trough points on both sides of the peak point in the first sequence as the fluctuation area of ​​the peak point; calculate the suspected abnormal feature value of the data point in the fluctuation area according to the number of times the peak point appears in the first sequence, the average value of the first sequence and the data points in the fluctuation area; and obtain the suspected abnormal point based on the suspected abnormal feature value.

[0040] The following data processing is performed on the first sequence and the second sequence after transmission respectively. In the received transmission data, the data in each sequence is projected into a separate plane rectangular coordinate system, with the horizontal axis being the data acquisition timing and the vertical axis being the data value of the heart rate or blood oxygen saturation.

[0041] There are some fluctuating data points in the data transmitted to the hospital terminal. These data points may be abnormal heart rate and blood oxygen saturation caused by the patient's physical illness, or they may be data abnormalities caused by noise interference during the transmission process. Therefore, it is necessary to analyze the transmitted data and obtain abnormal points suspected of transmission signal interference based on the distribution characteristics of the data.

[0042] Here, the characteristic values ​​of the data points are obtained by constructing a distribution feature model from multiple aspects. First, the degree of outlier of the data point is obtained. The farther the data point is from the data point set, the more outlier the data point is. Because the frequency of noise interference in the channel is highly random, and the heart rate and blood oxygen data caused by cardiopulmonary disease will not have distant outliers, the farther the data point is from the outlier, the more likely it is an abnormal point caused by transmission signal interference. The degree of change in the arrangement of the fluctuating data points before and after is obtained. Because noise interference is sudden, and the occurrence of the disease is procedural, the more drastic the change in the front and back parts of the fluctuating data points is, the more consistent it is with the characteristics of signal noise interference. The number of times the same value of the fluctuating data point appears in the data set is obtained. Because the heart rate and blood oxygen abnormalities caused by cardiopulmonary disease may appear periodically and repeatedly in a short period of time, and the data abnormalities caused by transmission signal interference are less likely to repeat with other data in the data set, the more times the same value of the fluctuating data point appears, the more likely the corresponding point is a cardiopulmonary disease abnormality, otherwise it is an abnormal point caused by transmission signal interference.

[0043] The peak point is a characteristic point in the curve that can represent the degree of fluctuation. Since the change of a peak is a process quantity, and the peak point is a representative point in the peak, a peak point can represent the changing characteristics of the entire peak. Therefore, the discrete change characteristics of the peak point and the changes in the data points of the entire peak where the peak point is located can be analyzed to reflect the characteristics of the data points of the entire peak. A peak represents the area between the trough points on the left and right sides of the peak point, and thus the area between the trough points on both sides of the peak point is recorded as the fluctuation area of ​​the peak point.

[0044] Furthermore, the suspected abnormal characteristic value of the data point in the fluctuation area is calculated according to the number of times the peak point appears in the first sequence, the average value of the first sequence and the data points in the fluctuation area. The specific calculation formula is:

[0045] ,

[0046] in, Represents the suspected abnormal characteristic value of the data point in the qth fluctuation area in the first sequence; represents the data value of the qth peak point in the first sequence; represents the mean of the data in the first series; Represents the number of data points between the qth peak point and the left trough point in the first sequence; Represents the value of the i-th data point among the data points between the q-th peak point and the left trough point in the first sequence; Represents the value of the i-1th data point among the data points between the qth peak point and the left trough point in the first sequence; Represents the number of data points between the qth peak point and the right trough point in the first sequence; Represents the value of the jth data point between the qth peak point and the right trough point in the first sequence; Represents the value of the j-1th data point among the data points between the qth peak point and the right trough point in the first sequence; It represents the number of data points in the first sequence that have the same value as the qth peak point, that is, the number of times the qth peak point appears in the first sequence.

[0047] It should be noted that, in the embodiment of the present invention, the trough points on the left and right sides of the crest point refer to the two trough points that are most adjacent to the crest point. It is the difference between the qth peak point in the first sequence and the overall level of the data points in the first sequence. The larger the difference is, the stronger the degree of outlier of the peak point is, and the greater the possibility that the data points in the fluctuation area where the peak point is located are abnormal points, and the more likely it is that it is random noise caused by signal interference. , is the difference between adjacent data points between the trough points on the left and right sides of the peak point. and It represents the mean of the cumulative changes of the data points before and after the qth peak point, or the mean of the cumulative changes of the data points in the fluctuation area. The more and more drastic the changes of data points in the fluctuation area, the greater the mean of the change, and the data points in the fluctuation area corresponding to the qth peak point are more likely to be abnormal points caused by noise interference; is the number of points with the same value as the qth peak point. The smaller the number, the more likely the data points in the fluctuation area where the peak point is located are abnormal points caused by transmission signal interference, and the stronger the suspected transmission signal interference is.

[0048] Thus, the suspected abnormal characteristic values ​​of the data points in the fluctuation area corresponding to all the peak points in the first sequence can be obtained. Furthermore, a first threshold is set, and the suspected abnormal characteristic values ​​of the data points in the fluctuation area are normalized, wherein the value of the first threshold can be adjusted by the implementer according to the actual situation through statistical analysis. In the embodiment of the present invention, the value of the first threshold is 0.6. If the suspected abnormal characteristic value of the data point in the fluctuation area after normalization is greater than or equal to the first threshold, the data point in the fluctuation area is a suspected abnormal point. Thus, the suspected abnormal points in the first sequence can be obtained.

[0049] The associated characteristic value acquisition module is used to calculate the change characteristic value of the suspected outlier point based on the difference between the suspected outlier point and the adjacent data points before and after; calculate the change characteristic value of the data point at the same time as the suspected outlier point in the second sequence, and record it as the associated change characteristic value of the suspected outlier point; calculate the associated characteristic value based on the change characteristic value of the suspected outlier point and the associated change characteristic value.

[0050] When patients show signs of cardiopulmonary disease, heart rate data and blood oxygen saturation data will be affected at the same time, showing abnormal changes. Therefore, abnormal data without signal interference is highly correlated in both heart rate data and blood oxygen saturation data. Therefore, we can combine the high correlation of data changes to obtain more accurate abnormal data detection results in the distribution characteristics of suspected abnormal data points.

[0051] In order to obtain a more accurate correlation degree of data points, the data change characteristic value of the suspected abnormal point is constructed here to better describe the difference between the change characteristics of the heart rate and blood oxygen saturation data caused by cardiopulmonary diseases and the abnormal data change characteristics caused by signal noise interference. Through this characteristic value, the correlation degree obtained is more accurate, and it is easier to distinguish the abnormal heart rate data and blood oxygen saturation data from the abnormal data caused by signal noise interference.

[0052] By taking the difference between the vertical axis values ​​of the suspected abnormal point and the adjacent data points before and after as the change value, the proportion of the change value in the maximum and minimum change values ​​of the vertical axis value of the overall data is obtained to unify the magnitude of the change degree in the heart rate and blood oxygen saturation as the change degree before and after the abnormal point. The sum of the change degrees before and after is taken as the cumulative sum of the change degrees before and after the suspected abnormal data point, which represents the overall change degree at the point; the difference between the change degrees before and after is taken as the difference of the change values ​​before and after, which represents the regular characteristics of the change degree before and after the point. For abnormal heart rate and blood oxygen saturation data caused by cardiopulmonary diseases, when the patient has signs of cardiopulmonary diseases, the heart rate data and blood oxygen saturation data will be affected at the same time. For example, when the pathological symptoms cause the blood oxygen saturation to decrease, the heart rate will increase accordingly; or when the pathological symptoms cause the heart rate to decrease, the blood oxygen saturation will decrease accordingly. The degree of change directly affects each other and has similar change characteristics. If the data points are not affected by noise, in the first sequence (heart rate data), the degree of change between a data point and the data point before it should be similar to the degree of change between the data point at the same time and the previous data point in the second sequence (blood oxygen saturation data). Similarly, the degree of change between the data point in the first sequence and the data point at the same time in the second sequence in both dimensions should also be similar. The more similar the distribution patterns of the degree of change before and after the data points at the same time in the two sequences are, the less likely the data point is to be affected by noise.

[0053] Therefore, the change characteristic value of the suspected outlier point is calculated based on the difference between the suspected outlier point and the adjacent data points. The specific calculation formula is:

[0054] ,

[0055] in, Indicates the change characteristic value of the pth suspected abnormal point in the first sequence; Represents the data value of the pth suspected outlier in the first sequence; Represents the data value of the left adjacent data point of the pth suspected abnormal point in the first sequence; Represents the data value of the right adjacent data point of the pth suspected abnormal point in the first sequence; and Represent the maximum and minimum values ​​in the first sequence respectively.

[0056] In the above calculation formula, and are the change values ​​between the suspected outlier point and its adjacent data points before it and the change values ​​between the suspected outlier point and its adjacent data points after it in the first sequence, respectively. and It is the ratio of the change value before and after the suspected abnormal point in the overall data value range. In the heart rate and blood oxygen saturation data, the magnitude of the change values ​​in the two dimensions of data can be unified by the ratio to obtain the degree of change before and after the suspected abnormal point. It is the cumulative sum of the changes before and after the suspected abnormal point, indicating the overall change degree of the suspected abnormal point; It is the difference between the degree of change of the suspected outlier point before and after, indicating the distribution law of the degree of change at the suspected outlier point.

[0057] Furthermore, data points at the same time as the suspected abnormal points in the first sequence are obtained in the second sequence, and the change characteristic values ​​of the data points at the same time in the second sequence are obtained by the same calculation method, which are recorded as the associated change characteristic values ​​of the suspected abnormal points. For example, the change characteristic value of the pth suspected abnormal point in the first sequence is , then the associated change characteristic value calculated based on the data point in the second sequence at the same time as the pth suspected abnormal point in the first sequence is , that is, the associated change characteristic value of the pth suspected abnormal point is .

[0058] The change feature value is used to obtain the correlation feature value of the abnormal data point in the two dimensions of heart rate and blood oxygen saturation. The more similar the change feature value of the suspected abnormal point is to the change feature value of the data point in another dimension at the same time, the higher the correlation between the two dimensions at that point, and the more likely the suspected abnormal point is a data abnormal point caused by cardiopulmonary disease. Conversely, the lower the correlation, the more likely it is a data abnormal point caused by transmission signal interference.

[0059] The absolute value of the difference between the change characteristic value of the suspected outlier and the associated change characteristic value is obtained, and the inverse of the absolute value is obtained to obtain the associated characteristic value of the suspected outlier. The specific calculation formula is:

[0060] ,

[0061] in, represents the associated eigenvalue of the pth suspected outlier point in the first sequence; Indicates the change characteristic value of the pth suspected abnormal point in the first sequence; The associated change characteristic value of the p-th suspected abnormal point in the first sequence is calculated based on the data points before and after the data point at the same time as the p-th suspected abnormal point in the first sequence in the second sequence. is the difference in the characteristic value between the suspected abnormal point in the first sequence and the data point in the second sequence at the same time. The smaller the difference, the greater the degree of correlation of the characteristic value of the pth suspected abnormal data point in the first sequence, and the less likely it is an abnormal point. In this way, the correlation characteristic value of each suspected abnormal point in the first sequence can be obtained.

[0062] The abnormal point acquisition module is used to calculate the abnormal degree of the suspected abnormal point according to the suspected abnormal feature value and the associated feature value of the suspected abnormal point; the suspected abnormal point is screened according to the abnormal degree to obtain the abnormal point, and the abnormal point in the second sequence is obtained similarly.

[0063] After obtaining the suspected abnormal feature value and the associated feature value of the suspected abnormal point, it is necessary to comprehensively obtain the abnormal degree of the suspected abnormal point by combining the suspected abnormal feature value and the associated feature value of the suspected abnormal point. Specifically, the suspected abnormal feature value of the suspected abnormal point is normalized; the associated feature value of the suspected abnormal point is negatively correlated and mapped using an exponential function with a natural constant as the base to obtain a mapping result; the average of the mapping result and the normalized suspected abnormal feature value is the abnormal degree of the suspected abnormal point; the specific calculation formula is:

[0064] ,

[0065] in, Indicates the abnormality degree of the pth suspected abnormal point in the first sequence; represents the suspected abnormal feature value of the pth suspected abnormal point in the first sequence; represents the associated eigenvalue of the pth suspected outlier point in the first sequence; It indicates the mapping result after negative correlation mapping of the associated eigenvalues ​​of suspected abnormal points using an exponential function with a natural constant as the base. The larger the suspected abnormal eigenvalue, the more the distribution of suspected abnormal points conforms to the data distribution caused by abnormal interference of transmission signals. The weaker the associated eigenvalue, the lower the possibility that the suspected abnormality is caused by cardiopulmonary disease, and the higher the possibility that it is caused by transmission signal interference.

[0066] The second threshold is thus set. When the abnormality degree of the suspected abnormal point in the first sequence is greater than or equal to the abnormality degree, the suspected abnormal point is an abnormal point that is interfered with by transmission. Similarly, the abnormal points in the second sequence can be obtained according to the same method. When obtaining the abnormal points in the second sequence, the first sequence needs to be used as a reference to calculate the associated characteristic values ​​of the suspected abnormal points. At this point, the abnormal points in the heart rate data and blood oxygen saturation transmitted to the hospital terminal can be obtained. Preferably, the value of the second threshold in this application is 0.8, which can be adjusted according to actual conditions.

[0067] The exception processing module processes the abnormal points in the first sequence and the second sequence, and saves the processed first sequence and the second sequence.

[0068] Furthermore, for the first sequence containing heart rate data and the second sequence containing blood oxygen saturation data, the abnormal points therein are obtained, and the values ​​of the abnormal points are replaced by the mean values ​​of the adjacent data points before and after the abnormal points, so as to achieve the purpose of smoothing the first sequence and the second sequence, and obtain the compensated and corrected first sequence and second sequence. In this way, all the transmitted and received sequences are cleaned and corrected to obtain the cleaned and corrected patient cardiopulmonary rehabilitation transmission data. The transmission data thus obtained eliminates the data distortion problem caused by signal interference that may exist in the signal transmission process, obtains more accurate transmission data, and improves the accuracy of the patient's remote transmission data.

[0069] In summary, this application proposes an efficient transmission system for traditional Chinese medicine cardiopulmonary rehabilitation data based on remote monitoring. By analyzing the distribution characteristics of cardiopulmonary rehabilitation data, data change characteristic values ​​are constructed to obtain data points that may be affected by interference noise and cause abnormalities, and then these abnormal data points are processed to improve the accuracy of cardiopulmonary rehabilitation data and prevent distortion during the transmission process from affecting the subsequent use of cardiopulmonary rehabilitation data.

[0070] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0071] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0072] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An efficient transmission system for TCM cardiopulmonary rehabilitation data based on remote monitoring, characterized in that: The system includes: A data acquisition module, used to acquire heart rate data and blood oxygen saturation data within an acquisition period, forming a first sequence and a second sequence respectively; A suspected abnormal point acquisition module is used to record the area between the trough points on both sides of the peak point in the first sequence as the fluctuation area of ​​the peak point; calculate the suspected abnormal feature value of the data point in the fluctuation area according to the number of times the peak point appears in the first sequence, the average value of the first sequence and the data points in the fluctuation area; and obtain the suspected abnormal point based on the suspected abnormal feature value; The associated characteristic value acquisition module is used to calculate the change characteristic value of the suspected abnormal point according to the difference between the suspected abnormal point and the adjacent data points before and after; calculate the change characteristic value of the data point at the same time as the suspected abnormal point in the second sequence, and record it as the associated change characteristic value of the suspected abnormal point; calculate the associated characteristic value according to the change characteristic value of the suspected abnormal point and the associated change characteristic value; An abnormal point acquisition module is used to calculate the abnormal degree of the suspected abnormal point according to the suspected abnormal feature value and the associated feature value of the suspected abnormal point; screen the suspected abnormal point according to the abnormal degree to obtain the abnormal point, and similarly obtain the abnormal points in the second sequence; An exception processing module processes the abnormal points in the first sequence and the second sequence, and saves the processed first sequence and the second sequence; The calculation formula of the change characteristic value of the suspected abnormal point is: ， in, Indicates the change characteristic value of the pth suspected abnormal point in the first sequence; Represents the data value of the pth suspected outlier in the first sequence; Represents the data value of the left adjacent data point of the pth suspected abnormal point in the first sequence; Represents the data value of the right adjacent data point of the pth suspected abnormal point in the first sequence; and Represent the maximum and minimum values ​​in the first sequence respectively.

2. According to claim 1, a remote monitoring-based traditional Chinese medicine cardiopulmonary rehabilitation data efficient transmission system is characterized in that: The calculation formula for the suspected abnormal characteristic value of the data point in the fluctuation area is: ， in, Represents the suspected abnormal characteristic value of the data point in the qth fluctuation area in the first sequence; represents the data value of the qth peak point in the first sequence; represents the mean of the data in the first series; Represents the number of data points between the qth peak point and the left trough point in the first sequence; Represents the value of the i-th data point among the data points between the q-th peak point and the left trough point in the first sequence; Represents the value of the i-1th data point among the data points between the qth peak point and the left trough point in the first sequence; Represents the number of data points between the qth peak point and the right trough point in the first sequence; Represents the value of the jth data point between the qth peak point and the right trough point in the first sequence; Represents the value of the j-1th data point among the data points between the qth peak point and the right trough point in the first sequence; Represents the number of data points in the first sequence that have the same value as the qth peak point.

3. According to the remote monitoring-based efficient transmission system for TCM cardiopulmonary rehabilitation data of claim 1, it is characterized in that: The suspected abnormal point is obtained based on the suspected abnormal feature value, including: A first threshold is set, and the suspected abnormal feature value of the data point in the fluctuation area is normalized; if the normalized suspected abnormal feature value of the data point in the fluctuation area is greater than or equal to the first threshold, the data point in the fluctuation area is a suspected abnormal point.

4. According to the remote monitoring-based efficient transmission system for TCM cardiopulmonary rehabilitation data of claim 1, it is characterized in that: The calculating of the associated characteristic value according to the change characteristic value of the suspected abnormal point and the associated change characteristic value includes: The absolute value of the difference between the change characteristic value of the suspected outlier point and the associated change characteristic value is calculated, and the inverse of the absolute value is calculated to obtain the associated characteristic value of the suspected outlier point.

5. According to the remote monitoring-based efficient transmission system for TCM cardiopulmonary rehabilitation data of claim 1, it is characterized in that: The calculating the abnormality degree of the suspected abnormal point according to the suspected abnormal feature value and the associated feature value of the suspected abnormal point includes: Normalize the suspected abnormal feature values ​​of the suspected abnormal points; use an exponential function with a natural constant as the base to negatively map the associated feature values ​​of the suspected abnormal points to obtain a mapping result; the average of the mapping result and the normalized suspected abnormal feature value sum is the abnormal degree of the suspected abnormal point.

6. According to the remote monitoring-based efficient transmission system for TCM cardiopulmonary rehabilitation data of claim 1, it is characterized in that: The step of screening the suspected abnormal points according to the abnormality degree to obtain the abnormal points includes: A second threshold is set, and if the abnormality degree of the suspected abnormal point in the first sequence is greater than or equal to the abnormality degree, the suspected abnormal point is an abnormal point.

7. According to claim 1, a remote monitoring-based traditional Chinese medicine cardiopulmonary rehabilitation data efficient transmission system is characterized in that: The processing of the abnormal points in the first sequence and the second sequence includes: The value of the outlier point is replaced by the mean of the adjacent data points before and after the outlier point.

Images