TWI702936B - Pulse condition analysis method, prediction model establishment method and system based on acupoint resistance and blood pressure wave - Google Patents

Abstract

A pulse condition analysis method, prediction model establishment method and system based on acupoint resistance and blood pressure wave. The system includes an acupoint resistance measuring device, a blood pressure wave measuring device and a judgment module. The acupoint resistance measuring device is used to obtain the acupoint resistance data of the subject; the blood pressure wave measuring device is used to obtain the blood pressure wave data of the subject; the judgment module is used to receive the acupoint resistance data and the blood pressure wave data, And output a pulse prediction result. Through the above design, it can be applied to predict the type of pulse of the testee, predict the future health status of the testee, or predict the disease risk of the testee.

Description

Pulse condition analysis method, prediction model establishment method and system based on acupoint resistance and blood pressure wave

The invention relates to a pulse condition analysis method and system; in particular, it refers to a pulse condition analysis method, a prediction model establishment method and a system based on acupoint resistance and blood pressure wave.

With the development and progress of science and medicine, many medical researches have also begun to try to summarize the symptoms, physiological indicators, clinical data, etc. of patients, trying to establish a model that can predict disease risk, or to find out the patients with the same or different diseases. The correlation between physiological states.

Therefore, how to perform physiological state assessment and disease prediction by measuring the physiological data or vital signs of the subject, so as to provide reference data for subsequent clinical diagnosis, or to provide early warning of abnormal physiological state of the user, is the inventor’s painstaking research One of the directions.

In the past, an industry has developed a pulse diagnosis instrument that can perform multi-point sensing, through which the pulse condition of the subject can be measured. However, this kind of multi-point sensing pulse diagnosis instrument is not only expensive, but also not portable, and it is difficult to popularize; in addition, according to the inventor's research results and experimental data, the pulse wave data or pulse wave measured by the pulse diagnosis instrument alone Data, without other physiological signals as the reference basis for interpretation, the correlation between the amount of data and the subject’s pulse or other physiological state is not sufficient to support accurate pulse prediction, physiological state interpretation and disease prediction, and needs improvement The place.

From the perspective of Chinese medicine, the occurrence of diseases is caused by the destruction of the balance of the human body, such as: heart and kidney failure, liver fire invading the stomach, etc. How to know that the balance of the human body is damaged can be inferred by observing the external signs of the human body. It has always been a very important method in TCM treatment by a Chinese physician to take the pulse of the patient and observe the changes in the pulse condition. It can be said to be one of the necessary methods for clinical diagnosis in TCM. In addition, scientific instruments are used to measure the skin resistance of the human body. According to the measurement results of skin resistance, the health assessment of the human body and the discussion of the correlation between skin resistance and disease are the subjects of the Western medicine system. Among them, Chinese medicine and Western medicine belong to two medical systems with completely different viewpoints. In the past research, the pulse wave (or pulse wave, blood pressure wave) and acupoint resistance data of the testee were not considered at the same time to predict the test The pulse condition of the patient or the technology for assessing the physiological state or predicting disease risk.

In view of this, the purpose of the present invention is to integrate the advantages of Chinese and Western medicine in a scientific way, to interpret the testee’s acupoint resistance and blood pressure wave data together, and to predict the pressure based on the measured acupoint resistance data and skin resistance data. The pulse condition of the examinee, or further based on the measurement data of acupoint resistance and skin resistance, determines the examinee's current physiological state, health status or disease risk prediction.

In order to achieve the above objective, the present invention provides a pulse analysis method based on acupoint resistance and blood pressure wave, which includes the following steps: a judgment module receives the acupoint resistance data and blood pressure wave data of a subject; the judgment module uses The acupoint resistance data and the blood pressure wave data are input, and the prediction result is judged based on a prediction model to obtain the pulse prediction result corresponding to the physiological state of the subject.

In order to achieve the above objective, the present invention also provides a method for establishing a prediction model based on the physiological state of the acupoint resistance and blood pressure wave, which includes the following steps: obtaining a plurality of training samples, the training samples record the acupoint resistance data and the blood pressure wave Data; the training samples are classified and trained by a classifier to obtain a plurality of sample characteristics; a prediction model is generated based on the sample characteristics, and the prediction model is used to input a testee’s acupoint resistance data and blood pressure wave data, And according to it, the result of the pulse prediction corresponding to the physiological state of the subject is obtained.

In order to achieve the above objective, the present invention also provides a physiological state analysis system based on acupoint resistance and blood pressure wave, which includes: an acupoint resistance measuring device to obtain acupoint resistance data of a subject; a blood pressure wave volume The detector is used to obtain the blood pressure wave data of the subject; a judging module is used to receive the acupoint resistance data and the blood pressure wave data, and output a pulse prediction result.

The effect of the present invention is that by reading the testee’s acupoint resistance data and blood pressure wave data, the pulse condition prediction result corresponding to the test subject can be obtained, and the pulse condition prediction result can be used to understand the subject’s current physiology Whether the state is normal, predict the future health status of the subject, or predict the risk of the subject’s disease.

In order to explain the present invention more clearly, an embodiment is given in conjunction with the drawings in detail as follows. Please cooperate with FIG. 1, the physiological state analysis system based on the acupoint resistance and blood pressure wave of the present invention basically includes an acupoint resistance measuring device 10, a blood pressure wave measuring device 20, and a judgment module 30. The measuring device 10 is used to obtain the acupoint resistance data of a subject; the blood pressure wave measuring device 20 is used to obtain the blood pressure wave data of the subject; the judgment module 30 is used to receive the acupoint resistance data and the blood pressure wave Data, and output a pulse prediction result. In one embodiment, the judging module 30 uses the acupoint resistance data and the blood pressure wave data as input to determine the prediction result based on a prediction model to obtain a pulse prediction result corresponding to the physiological state of the subject.

Wherein, the prediction model is obtained by pre-training. As shown in Figure 2, the method of establishing the prediction model includes the following steps: First, a plurality of training samples are obtained. These training samples are recorded with acupoint resistance data and blood pressure wave data. Wherein, the source of the training samples may be provided by the medical system, for example, clinical research or diagnostic data from hospitals, clinics, and medical research institutions, or nurses such as doctors, nurses, etc. who use palpation or equipment Or measured in other ways. In addition, the training samples may also be collected from the physiological state test results of multiple patients who have been diagnosed with the disease. Preferably, the patient’s physiological state test results include a medical examination conducted by a Chinese physician on the patient. The diagnosis result of pulse diagnosis is used to collect test results including at least one kind of pulse condition data, wherein the pulse condition may include, but is not limited to, floating pulse, scattered pulse, sinking pulse, Fu pulse, delayed pulse, slow pulse, Hua Pulse, xuan pulse, tight pulse, knot pulse, etc.

Then, the training samples are trained through a classifier to obtain a plurality of sample features. For example, referring to Figure 3, training can be performed based on multiple training samples (such as training sample 1, training sample 2, training sample 3,...) with known pulse condition results (such as Xuan mai, Ping mai, etc.), and then , And then generate a predictive model based on the sample characteristics, the predictive model is used to input the testee's acupoint resistance data and blood pressure wave data, and according to the testee's physiological state pulse prediction results. Wherein, the prediction model can perform different pulse condition prediction or pulse condition interpretation according to different training samples, and the above-mentioned pulse condition prediction results can be used to predict the pulse condition of the subject, when the training samples used are When the data of some patients who have been diagnosed with the disease are included, it can also be used to judge whether the subject has the disease or predict the risk of the disease. For example, when the training sample is obtained from physiological information measured by a patient with a cardiovascular disease, the physiological information can be, but not limited to, acupoint resistance value, blood pressure wave value, in addition to determining the type of pulse of the subject In addition, it can also be used to determine whether the subject has cardiovascular disease, or whether it is a potential high-risk group of cardiovascular disease; assuming that the training sample is derived from the physiological information measured by diabetic patients, in addition to judging the subject In addition to the types of pulse conditions, it can also be used to determine whether the subject has diabetes or whether they are potentially at high risk for diabetes. In other words, according to different training samples, predictive models can be established to interpret different types of pulse conditions, and even predictive models for judging whether the subject is suffering from disease or disease risk prediction. Predictive model of rectal cancer.

In this embodiment, the prediction model is used to predict the pulse type of the subject. Please cooperate with Figure 4 and Figure 5 of this case together. Before performing the pulse analysis method of the present invention, the test subject 1 is measured with the acupoint resistance measuring device and the blood pressure wave measuring device, and the subject 1 is obtained. Acupoint resistance data and blood pressure wave data.

Among them, the test subject 1 can use the smart phone 40 to measure by himself. For example, the smart phone 40 can be connected to the acupoint resistance measuring device 10 in the form of a patch, and then the acupuncture resistance measuring device 10 can be contacted to the subject 1 is derived from the skin at the acupoints of the meridians. For example, 24 representative acupoints of the twelve meridians on the left and right sides of the human body can be measured. In one embodiment, the acupoint resistance data can also be measured on a single side of the human body. The twelve acupuncture points of the twelve meridians (left or right), or measure at least one of the three yin meridians of the hand, at least one of the three yang meridians of the hand, and at least one of the three yang meridians of the foot And at least one of the three yin meridians of the foot, but preferably, the twelve acupuncture points of all twelve meridians are measured. In addition, in one embodiment, the acupoint resistance measuring device 10 can also be an independent set of equipment, and after measuring the acupuncture resistance data of the subject 1, it can be transmitted via wired or wireless transmission. Send to the smart phone 40.

In addition, the blood pressure wave (or pulse wave, pulse wave) data can be, but not limited to, the use of photoelectric measurement methods, such as PPG signals obtained by photoplethysmography (PPG), for example, In operation, it can be, but not limited to, using the lens on the smartphone 40 of the subject 1 to close the body of the subject 1, such as fingers, wrists or other parts. When the pulse beats, the blood vessels will produce small fluctuations, and then A lens is used to record the periodic changes of the light intensity of the blood flowing through the measurement site. The periodic changes of these light signals can represent the signals of the subject 1’s blood pressure and pulse, and then these light signals are converted into electrical signals to obtain the use The blood pressure wave data of the person. However, in other applications, the method of obtaining blood pressure wave data is not limited to this, and the blood pressure wave data of subject 1 can also be measured through wearable electronic devices such as Apple Watch.

After the smart phone 40 receives the acupoint resistance data and blood pressure wave data of the subject 1, it can be sent to the judgment module 30 for interpretation, and the prediction result is determined based on the prediction model, and the physiological state corresponding to the subject 1 is obtained The result of pulse prediction. For example, in one embodiment, the smart phone 40 can be connected to a cloud server 50, and the subject 1 can upload the acupoint resistance data and blood pressure wave data to the cloud server 50, and the judgment model of the cloud server 50 The group 30 determines the prediction result, and then replies the prediction result to the smart phone 40, and displays it on the screen of the smart phone 40 for reference by the testee 1. In this embodiment, the judgment result of the prediction result is based on the meridian theory and the blood circulation resonance theory proposed by Professor Wang Weigong at least some of the characteristics of C1-C10 and the characteristics in the time domain and/or frequency domain. The prediction results obtained from the classification of the classifier taking the support vector machine as an example. Among them, for the pulse type classification of this embodiment, there may be several possibilities for the pulse type interpretation result. For example, the probability of Ping mai is 85%, and the probability of Xian mai is 85%. The probability of pulse is 10%, the probability of thin pulse is 3%, and the probability of Shen pulse is 1%. Please follow the picture shown in Figure 6. Reply to the content displayed on the smartphone 40, which can be set to display only the highest probability The pulse condition (for example, only the prediction result is Ping mai), or in addition to the prediction result, the possibility of various pulse conditions and the probability can be displayed together for the reference of subject 1 or medical staff.

Wherein, after the determination module 30 receives the acupoint resistance data and the blood pressure wave data, it can be, but not limited to, extracting the signal characteristics in the time domain and/or frequency domain through an algorithm, for example, according to the blood circulation proposed by Professor Wang Weigong Resonance theory uses support vector machines (SVM) to classify the acupoint resistance data and blood pressure wave data provided by subject 1 to obtain prediction results.

For example, after the determination module 30 receives the data, before classification or prediction, the blood pressure wave data undergoes signal processing in the time domain and/or frequency domain. For example, in this embodiment, the Signal processing in the time domain includes but is not limited to: pre-processing the PPG signal to remove the noise in the signal, for example; to remove motion artifacts, the methods used can be, but not limited to, using cycles The moving average filter (Periodic Moving Average Filter, PMAF) eliminates noise to obtain an average waveform. For example, you can divide the original PPG signal into multiple waveforms, and perform PMAF with each multiple waveforms as a group to obtain the average waveform. Through Dynamic Time Warping (Dynamic Time Warping), the average waveform is compared with the original PPG signal waveform to remove noise that is significantly different from the original waveform. The foregoing pre-processing of the PPG signal can improve the accuracy of the subsequent extraction of the features of the PPG signal in the time domain, but it is not a necessary step. After the noise is eliminated, the waveform of the PPG signal in the time domain as shown in Figure 7 can be obtained, and the Pulse Wave Systolic Peak (PWSP), Pulse Wave Begin (PWB) and pulse Pulse Wave Diastolic Peak (PWDP). According to the above three points, please cooperate with Figure 7 and Figure 8, and calculate the eight characteristics of the PPG signal in the time domain according to the following formula. Among them, formula (1) is used to calculate the wave amplitude (Systolic Peak-Diastolic Peak Amplitude) between PWSP and PWDP. Formula (2) is used to calculate the wave amplitude between PWDP and PWE (Diastolic Peak-Pulse Wave End Amplitude). The formula (3) is to calculate the angle of the PWSP with the angle shown in Figure 8, where a is the distance from PWSP to the Arterial Notch (Dicrotic Notch), b is the distance from PWSP to PWB, c From PWB to PWDP. Formula (4) calculates the wave amplitude between PWSP and PWB to obtain the pulse wave amplitude (Pulse Wave Amplitude, PWA). Formula (5) calculates the distance between PWSP and PWB to obtain the Systolic Phase. Formula (6) calculates the distance between PWSP and PWE to obtain the diastolic phase (Diastolic Phase). Formula (7) calculates the distance between PWSP and PWDP to obtain the pulse propagation time (Pulse Propagation Time). Formula (8) is used to calculate the distance between two adjacent PWSPs to obtain the heartbeat interval (Interbeat Interval).

而在頻域上的訊號處理方面，包括有藉由快速傅立葉轉換將時域上的PPG訊號轉換為頻域，並根據王唯工教授所提出的血液循環共振理論，提取C1-C10中的至少幾個特徵，例如取得C1-C7等七個特徵，其中，C1-C7分別代表肝經、腎經、脾經、肺經、胃經、膽經、膀胱經的特徵值。於後，再藉由如支援向量機（SVM）將經訊號處理後的穴道電阻資料與血壓波資料進行分類，以獲得預測結果。其中，前述建立預測模型的方法所使用的訓練樣本中的血壓波資料同樣經過如同前述的時域及/或頻域上的訊號處理，於後再透過分類器對該些經訊號處理後的訓練樣本進行訓練。

In terms of signal processing in the frequency domain, the PPG signal in the time domain is converted to the frequency domain by fast Fourier transform, and based on the blood circulation resonance theory proposed by Professor Wang Weigong, at least a few of C1-C10 are extracted Features, such as obtaining seven features such as C1-C7, where C1-C7 respectively represent the characteristic values of the liver meridian, kidney meridian, spleen meridian, lung meridian, stomach meridian, gallbladder meridian, and bladder meridian. Then, the signal-processed acupoint resistance data and blood pressure wave data are classified by means of, for example, a support vector machine (SVM) to obtain prediction results. Among them, the blood pressure wave data in the training samples used in the aforementioned method of establishing a predictive model is also subjected to the aforementioned signal processing in the time domain and/or frequency domain, and then the signal processed training is performed through the classifier Samples for training.

表一： Please cooperate with Table 1 and Figure 7. The table below is the use of random decision forests to evaluate the time domain and frequency domain characteristics of the PPG signal. The higher the weight presented in the table, the greater the influence of the feature The higher the importance of the judgment result, the current ranking system is arranged in descending order of importance. The numbers (1)~(7) shown after the characteristics of Table 1 correspond to formulas (1)~(7) respectively.

Table I:

In addition, in one embodiment, the connection mechanism between the smart phone 40 and the cloud server 50 may be that a mobile application is installed on the smart phone 40, and the smart phone 40 uses the mobile application to compare the acupoint resistance data and blood pressure. The wave data is uploaded to the cloud server 50 to determine the prediction result. In addition, in one embodiment, as shown in Figure 9, the mobile application can be designed to provide multiple detection options for the subject 1 to choose, and the subject 1 can click on the detection item that he wants to detect. After that, the smartphone 40 can upload the test subject 1’s acupoint resistance data, blood pressure wave data, and test item information selected by subject 1 to the cloud server 50, and the cloud server 50 can then upload the test items to the cloud server 50. The information selects the corresponding predictive model, and then uses the acupoint resistance data and blood pressure wave data of the subject 1 to insert the test model to obtain the prediction result corresponding to the physiological state of the subject 1, and responds to the smartphone 40 For reference of subject 1.

In addition, the judgment module for interpreting the measured acupoint resistance data and blood pressure wave data is not limited to the judgment module 30 built in the cloud server 50. In one embodiment, the judgment module It can also be built into a smart phone, or use the smart phone’s processor to perform the function of the judgment module, and the prediction model can be stored in the smart phone, so that the user can operate the smart phone to The resistance data and blood pressure wave data are input into the prediction model stored in the smartphone, and the corresponding pulse condition prediction results are obtained.

In addition, please cooperate with the table 2 below, for the inventor to use a 5-fold cross-validation method to verify the accuracy of the pulse analysis method based on the acupoint resistance and blood pressure wave of the present invention. Among them, the first, second, and third groups are the control group, and the fourth, fifth, and sixth groups are the experimental groups. From the verification results of the first to third groups, it can be seen that when only the acupoint resistance feature or only the blood pressure wave is used When feature (frequency domain or time domain), its prediction accuracy is only about 62.5%, 65%. In contrast, when the analysis method of the present invention is applied, for example, when the fourth group uses eight blood pressure wave time domain features and twelve acupoint resistance features, the accuracy rate reaches 72.5%; the fifth group uses seven blood pressure wave frequencies The accuracy rate reaches 75% when the characteristics of the resistance domain and twelve acupuncture points are achieved; the sixth group uses 15 blood pressure wave characteristics including eight blood pressure wave time domain characteristics, seven blood pressure wave frequency domain characteristics, and twelve acupoints. When the resistance is characteristic, its accuracy rate reaches 90%. It can be seen that using the pulse analysis method based on the acupoint resistance and blood pressure wave of the present invention can significantly improve the prediction accuracy compared to only relying on the characteristics of the acupoint resistance and the blood pressure wave. Table II Group Feature Accuracy One 12 GSR features (12 GSR feature) 62.5% two 8 blood pressure wave time domain features (8 PPG time domain feature) 65% three 7 PPG frequency domain feature 62.5% four 8 blood pressure wave time domain features and 12 acupoint resistance features (8 PPG time domain & 12 GSR feature) 72.5% Fives 7 blood pressure wave frequency domain features and 12 acupoint resistance features (7 PPG frequency domain & 12 GSR feature) 75% six 8 blood pressure wave time domain features, 7 blood pressure wave frequency domain features, and 12 acupoint resistance features (15 PPG (time/frequency domain) feature & 12 GSR feature) 95%

Please cooperate with Figure 10, which is a broken line graph of the electromyographic amplitude of multiple acupoints measured by the pulse analysis system of the present invention and the commercial instrument ARDK (Auto-Reflex-Diagnostic-Kinetics). It can be seen that the pulse analysis system of the present invention has a very high correlation with the commercial instrument ARDK in measuring the acupoint resistance, which can prove that the pulse analysis system of the present invention has high accuracy.

Please cooperate with Figure 11 to Figure 13, the pulse analysis system of the present invention and the ANS Watch wrist-type bio-monitor (model TS-0411) to measure the line graph of the PPG signal. Among them, Figure 11 to Figure 13 respectively compare the heart rhythm (HR), the standard deviation of the heartbeat interval (SDNN) and the sympathetic/parasympathetic balance index (LF/HF). It can be seen from the diagram that the pulse analysis system of the present invention In measuring the PPG signal, it has a very high correlation with the ANS Watch wrist-type bio-monitor, which can prove that the pulse analysis system of the present invention has high accuracy.

The invention innovatively uses the human body’s acupoint resistance and blood pressure wave data as the basis for interpreting the type of pulse of the subject, successfully combines the advantages of traditional Chinese medicine and western medicine, and provides users with convenient pulse analysis, and even physiological analysis based on the results of pulse analysis. The evaluation of the state or disease prediction does not require the use of bulky and large measuring instruments, and does not require a specific environment or place to perform the evaluation. Anyone needs a mobile device such as a smartphone and acupoint resistance no matter when and where. The measuring device can use the system provided by the present invention to predict the pulse condition, or further analyze the physiological state or predict the disease. Moreover, the pulse condition analysis system constructed by the above-mentioned simple equipment can obtain commercial detection instruments that are comparable to those currently on the market, which shows that the present invention has a breakthrough innovation. In addition, the pulse condition analysis method, prediction model establishment method and system provided by the present invention can be applied to predict pulse condition as well as other applications, such as but not limited to predicting diseases, such as predicting cardiovascular diseases. , Diabetes, liver disease and other diseases. For example, the inventor applied the pulse analysis system of the present invention to predict heart failure. Based on 127 test subjects, the accuracy rate of using the pulse analysis system of the present invention to estimate that the subject is indeed suffering from heart failure is also as high as 98%. Indeed, the present invention can also be applied to predict or assess whether a patient has cardiovascular diseases such as heart failure.

The above are only the preferred and feasible embodiments of the present invention. Any equivalent changes made by applying the specification of the present invention and the scope of the patent application should be included in the patent scope of the present invention.

[this invention] 1: Subject 10: Acupoint resistance measuring device 20: Blood pressure wave measuring device 30: Judgment module 40: smart phone 50: Cloud server

Figure 1 is a block diagram of a pulse analysis system according to an embodiment of the present invention. FIG. 2 is a flowchart of a method for establishing a prediction model according to an embodiment of the present invention. Figure 3 is a schematic diagram of a neural network such as the application of the present invention. Fig. 4 is a flowchart of a pulse analysis method according to an embodiment of the present invention. Figure 5 is a schematic diagram of an embodiment of the present invention applying a pulse condition analysis system. Figure 6 is a schematic diagram of a smart phone, revealing the screen of the smart phone. Figure 7 is a schematic diagram of the eight characteristics of the PPG signal in the time domain. Figure 8 is a schematic diagram of the angle of PWSP. Figure 9 is a schematic diagram of a smart phone, revealing the screen of the smart phone. Figure 10 is a broken line diagram of the electromyographic amplitude of multiple points measured by the pulse analysis system of the present invention and the commercial instrument ARDK. Figures 11 to 13 are respectively the line graphs of the PPG signal measured by the pulse analysis system of the present invention and the ANS Watch wrist-type bio-monitor.

10: Acupoint resistance measuring device

20: Blood pressure wave measuring device

30: Judgment module

Claims (10)

A pulse condition analysis method based on acupoint resistance and blood pressure wave, which includes the following steps: A. A judgment module receives the acupoint resistance data and blood pressure wave data of the subject. The acupoint resistance data includes Dazhong, Daling, Shenmen, and Shu The resistance data of Gu, Chongyang, Taichong, Taibai, Yangxi, Yangchi, Yanggu, Qiuxu and Taiyuan; B. The judgment module uses the acupoint resistance data and the blood pressure wave data as input at the same time, based on a The prediction model is used to determine the prediction results, and cross-validate the acupoint resistance data and the blood pressure wave data to obtain the pulse prediction results corresponding to the physiological state of the subject. The pulse condition analysis method based on acupoint resistance and blood pressure wave as described in claim 1, wherein the acupoint resistance data is obtained by measuring the skin at the meridian points of the subject. The pulse condition analysis method based on the acupoint resistance and blood pressure wave as described in claim 1, wherein the acupoint resistance data and the blood pressure wave data are collected from the physiological state detection results of a plurality of patients, and the physiological state detection results of the patients Including the results of pulse diagnosis by Chinese physicians. The pulse condition analysis method based on acupoint resistance and blood pressure wave as described in claim 1, wherein before step B, the blood pressure wave data is processed by signals in the time domain and/or frequency domain, and the blood pressure wave data contains light The volume change describes multiple characteristic values of a waveform (PPG) signal in the time domain, and/or multiple characteristic values of the PPG signal in the frequency domain. The pulse condition analysis method based on acupoint resistance and blood pressure wave as described in claim 4, wherein the multiple characteristic values of the PPG signal in the time domain are obtained by the following eight formulas, among which formula (1) is used to calculate the pulse wave The amplitude between the peak systole (PWSP) and the peak diastolic pulse (PWDP); formula (2) is used to calculate the amplitude from the PWDP to the pulse end point (PWE); formula (3) the peak angle of PWSP; formula ( 4) It is used to calculate the amplitude between PWSP and the PWB at the beginning of the pulse wave; formula (5) is used to calculate the distance between PWSP and PWB to obtain the contraction phase; formula (6) is used to calculate the distance between PWSP and PWE to Obtain the diastolic phase; formula (7) is used to calculate the distance between PWSP and PWDP to obtain the pulse wave propagation time; formula (8) is used to calculate the distance between two adjacent PWSPs to obtain the heartbeat interval

Formula (5)=PWSP(x)-PWB(x) Formula (6)=PWSP(x)-PWE(x) Formula (7)=PWSP(x)-PWDP(x) Formula (8)=Next PWSP( x,y)-PWSP(x,y). A method for establishing a prediction model applied to the pulse analysis method based on acupoint resistance and blood pressure wave as described in claim 1, which includes the following steps: A. Obtain a plurality of training samples, the training samples record acupoint resistance data and Blood pressure wave data. The acupoint resistance data includes the resistance data of Dazhong, Daling, Shenmen, Shugu, Chongyang, Taichong, Taibai, Yangxi, Yangchi, Yanggu, Qiuxu and Taiyuan; B. Through a classification The processor performs classification training on these training samples to obtain multiple sample features; C. Generate a predictive model based on the characteristics of the samples. The predictive model is used to input the testee’s acupoint resistance data and blood pressure wave data at the same time, cross-validate the acupoint resistance data and the blood pressure wave data, and obtain the corresponding response The result of the pulse condition prediction of the physiological state of the tester. The method for establishing a predictive model based on the physiological state of acupoint resistance and blood pressure wave as described in claim 6, wherein the blood pressure wave data includes multiple characteristic values of the PPG signal in the time domain, and/or the PPG signal in the frequency domain Of multiple characteristic values. The pulse condition analysis method based on acupoint resistance and blood pressure wave as described in claim 7, wherein multiple characteristic values of the PPG signal in the time domain are obtained by the following eight formulas, among which formula (1) is used to calculate the pulse wave The amplitude between the peak systole (PWSP) and the peak diastolic pulse (PWDP); formula (2) is used to calculate the amplitude from the PWDP to the pulse end point (PWE); formula (3) the peak angle of PWSP; formula ( 4) Used to calculate the amplitude between PWSP and PWB; formula (5) is used to calculate the distance between PWSP and PWB to obtain the systolic phase; formula (6) is used to calculate the distance between PWSP and PWE to obtain the diastolic phase ; Formula (7) is used to calculate the distance between PWSP and PWDP to obtain the pulse wave propagation time; Formula (8) is used to calculate the distance between two adjacent PWSPs to obtain the heartbeat interval. A pulse condition analysis system based on acupoint resistance and blood pressure wave, which includes: an acupoint resistance measuring device to obtain acupoint resistance data of a subject. The acupoint resistance data includes Dazhong, Daling, Shenmen, Shugu, Resistance data of Chongyang, Taichong, Taibai, Yangxi, Yangchi, Yanggu, Qiuxu and Taiyuan; a blood pressure wave measuring device to obtain blood pressure wave data of the subject; a judgment module, It is used to simultaneously receive the acupoint resistance data and the blood pressure wave data, cross-validate the acupoint resistance data and the blood pressure wave data, and output a pulse prediction result. The physiological state analysis system based on acupoint resistance and blood pressure wave according to claim 9, wherein the judgment module uses the acupoint resistance data and the blood pressure wave data as input In, based on a prediction model, the prediction result is judged to obtain the pulse condition prediction result corresponding to the physiological state of the subject.

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