TW202207871A - Arteriosclerosis risk assessment system including a wearable detection device, a mobile monitoring device, and a cloud server - Google Patents

Abstract

The present invention provides an arteriosclerosis risk assessment system including: a wearable detection device for detecting a heart rate signal of a test subject; a mobile monitoring device wirelessly connected to the wearable detection device, wherein the mobile monitoring device is used for receiving the heart rate signal, and calculating an arteriosclerosis index, a heart rate variability index, and a ratio of the heavy sine wave according to the heart rate signal, comparing same with an arteriosclerosis index upper limit, a heart rate variability index threshold, and a heavy sine wave upper limit, respectively, and then calculating a risk index according to the comparison result; a cloud server, wirelessly connected to the mobile monitoring device, for receiving a plurality of the arteriosclerosis indexes of the test subjects, and calculating the the arteriosclerosis index upper limit, thereby providing users with the effect of simply and rapidly assessing the arteriosclerosis risk.

Description

Arteriosclerosis Risk Assessment System

An arteriosclerosis risk assessment system, especially an analysis and assessment system that converts a heart rate signal from time domain to frequency domain, can eliminate heavy sine waves and leave diploid waves, and calculate arteriosclerosis index.

Arteriosclerosis is a broad term that includes small arteries, arterial media, and atherosclerosis. The most serious atherosclerosis occurs in coronary arteries and middle arteries. If you do not receive regular cardiovascular examinations , patients may not know that they suffer from arteriosclerosis disease, and eventually lead to sudden myocardial infarction and stroke.

Therefore, the existing measurement device is a wearable device. General wearable devices are mostly worn on the wrist of the subject in the form of a bracelet or a watch, such as smart wearable devices such as Xiaomi Mi Band and Apple Watch. The wearable device can detect a heart rate signal (generally referred to as PPG signal) of the subject through photoplethysmography (PPG), and the information presented by the heart rate signal includes heartbeat and blood flow inside the blood vessel. state. With PPG measurement technology, the heartbeat and blood flow status of the subject can be quickly obtained, and the arteriosclerosis index (SI) of the subject can be easily measured to determine the degree of sclerosis of the subject's blood vessels, among which the arteriosclerosis index The calculation formula is:

SI=h/ΔT*100%; where h is the height of the subject, and ΔT is the time difference between the main peak of the pulse and the dichotomous wave in the PPG signal of the subject.

Referring to Fig. 15, the PPG signal in a heartbeat cycle contains two pulse waves, one is the main peak MP of the pulse wave, and the other is the dichotomous wave 40. The reason for the formation of the dichotomous wave 40 is that the pulse wave is transmitted to the lower body and reflected. (rebound) to the aortic valve and then transmitted to the sensing site, so the amplitude of the dichotomous wave 40 can show the dilatation capability of the arterial diameter. Once the energy of the dichotomous wave 40 is detected, the time difference ΔT between the main peak MP of the pulse and the dichotomous wave 40 can be further measured, and the arteriosclerosis index can be calculated according to the above formula.

Please refer to FIG. 16A , but in actual measurement, it is often found that the diploid wave of the subject's PPG signal is not obvious, and this waveform is called a heavy sine wave 50 , and FIG. Referring to FIG. 16B , waveform 501 can be obtained after performing a differentiation on the heavy sine wave 50, but still does not show the wave band of the blood vessel rebounding blood, and the exact position of the heavy sine wave cannot be found, instead, the position close to the trough is captured, and the trough occurs. The time will be later than the time of the occurrence of the dicrotic wave, so the ΔT becomes larger, resulting in a low arteriosclerosis index. The reason for the heavy sine wave 50 is that the subject's blood vessels are aged, hardened seriously, or due to poor blood circulation, after the heart compresses and pumps blood, although blood flows into the blood vessels, the blood vessels lose their ability to expand due to hardening and cannot rebound blood, resulting in inability to produce a dither wave. According to statistics, older people with a history of cardiovascular disease account for more than 50% of their PPG signals with a heavy sine wave of 50, and such patients are the most important group for tracking the arteriosclerosis index. In addition, there are also technologies on the market that claim to be able to monitor arteriosclerosis, mainly through the full-time monitoring of HRV performance to estimate cardiovascular status. The HRV reference indicators are SDNN, SDANN, SDNNIndex and other time domain indicators, but the time domain is used. For accurate judgment of indicators, 24-hour PPG signals are needed for statistics, and the disturbance of irregular heart rate caused by exercise or excessive activity of interactive sensory nerves due to a large number of activities must be excluded. It is only suitable for patients who are bedridden in the hospital. use. Therefore, how to allow individuals to easily assess the possibility of arteriosclerosis in their daily life has become an important goal in cardiovascular technology.

In order to allow users to easily assess the risk of arteriosclerosis in their daily life, the present invention provides an arteriosclerosis risk assessment system, which measures heart rate signals by wearing a detection device, and converts the heart rate signals into arteriosclerosis by a mobile monitoring device. Relevant data provides users with self-testing to obtain real-time and rapid arteriosclerosis risk detection results.

In order to achieve the above-mentioned purpose, the arteriosclerosis risk assessment system of the present invention comprises: a wearable detection device for detecting a heart rate signal of a subject; A mobile monitoring device wirelessly connected to the wearable detection device, the mobile monitoring device is used for receiving the heart rate signal, and calculates an arteriosclerosis index, a heart rate variability index and the ratio of the heavy sine wave according to the heart rate signal, and calculates the The arterial stiffness index, the heart rate variability index, and the ratio of the heavy sine wave are compared with an upper limit value of the arteriosclerosis index, a threshold value of a heart rate variability index, and an upper limit value of the heavy sine wave ratio, and then converted into a risk index according to the comparison result. ; a cloud server, wirelessly connected to the mobile monitoring device, and used for receiving the arteriosclerosis index of each of the plurality of subjects, and calculating the upper limit value of the arteriosclerosis index.

The present invention further calculates the arteriosclerosis index, the heart rate variability index and the ratio of the heavy sine wave according to the heart rate signal after obtaining the heart rate signal by photoplethysmography, and compares an upper limit value of the arteriosclerosis index, a heart rate The threshold value of the variation index and the proportional hazard value of a single sine wave; according to the results of the comparison, it can provide users with a risk reference for the possibility of their own arterial vascular sclerosis in life, so as to be able to be able to prevent the occurrence of emergencies. Seek medical attention as soon as possible.

Further, the present invention can further eliminate the heavy sine wave in the heart rate signal, leaving a diploic wave that can calculate the arteriosclerosis index, that is, it can eliminate the error calculated by using the heavy sine wave, and use the pure heavy sine wave to calculate this. Arterial stiffness index, can obtain a more accurate arterial stiffness index. The present invention can further add up multiple arteriosclerosis indices and take an average to obtain the mean arteriosclerosis index of the subject, thereby preventing a single arteriosclerosis index from being affected by measurement errors and improving measurement accuracy.

The invention discloses an arteriosclerosis risk assessment system, which can exclude the heavy sine wave in the heart rate signal and leave the heavy pulse wave for calculating the arteriosclerosis index, so as to improve the accuracy of calculating the arteriosclerosis index.

Referring to FIG. 1 , the arteriosclerosis risk assessment system of the present invention includes: a wearable detection device 10 , a motion monitoring device 20 and a cloud server 30 .

The wearable detection device 10 can be worn on a subject for detecting a heart rate signal of the subject, wherein the heart rate signal is presented as an amplitude-time waveform in the time domain. Generally speaking, the wear detection device 10 can be a smart bracelet, and the subject can wear the smart bracelet on the wrist. The wearable detection device 10 obtains the heart rate signal through photoplethysmography (hereinafter referred to as PPG), and the heart rate signal may include the heartbeat and the blood flow inside the blood vessel. Specifically, the wearing detection unit 10 may include a control unit 11 , a light emitting unit 13 and a light receiving unit 15 , the light emitting unit 13 and the light receiving unit 15 are respectively electrically connected to the wearing detection unit 10 . The control unit 11 is used for activating the light emitting unit 13 and the light receiving unit 15, and has a wireless transmission function; the light emitting unit 13 is used for emitting a continuous detection light to the blood in the blood vessel of the subject, The blood in the blood vessel reflects the detection light to form a reflected light; the light receiving unit 15 receives the reflected light and transmits it to the control unit 11 , and the control unit 11 converts the reflected light into the heart rate signal. Since the light emitting unit 13 continuously emits the detection light to the blood in the blood vessel, the reflected light is also continuous, and the control unit 11 converts the continuous reflected light into a signal with amplitude (intensity of the reflected light) and time. relationship, that is, the heart rate signal.

The mobile monitoring device 20 is wirelessly connected to the wearable detecting device 10 and is also held by the subject. The mobile monitoring device 20 can be a mobile phone or a tablet, and can be connected to the wearable detecting device 10 through Bluetooth. The mobile monitoring device 20 is used for receiving the heart rate signal, and calculating an arteriosclerosis index, a heart rate variability index and a ratio of the heavy sine wave according to the heart rate signal, and calculating the arterial stiffness index, the heart rate variability index and the heavy sine wave The ratio of atherosclerosis index, a heart rate variability index threshold value and a double sine wave ratio upper limit value are respectively compared, and then converted into a risk index according to the comparison results, wherein the risk index represents the subject suffering from atherosclerosis related to arteriosclerosis. risk of disease. The mobile monitoring device 20 can further analyze and monitor the subject's pulse, blood pressure, and heart rate variability indicators (eg, TP, HF, LF) for a long time according to the heart rate signal, and provide the monitored pulse, blood pressure, and heart rate variability indicators. Give it to the subject and let the subject assess his or her own health.

The cloud server 30 is wirelessly connected to the mobile monitoring device 20 for receiving and storing the arterial stiffness index, the user's pulse, blood pressure, and heart rate variability index.

The cloud server 30 can also collect the latest confirmed numbers of people suffering from arteriosclerosis-related diseases provided by credible health research institutions such as the Ministry of Health and Welfare, and compare it with the total population of the year ( Multiple subjects), calculate the proportion of the multiple subjects who do not suffer from arteriosclerosis-related diseases, and use the multiple subjects who do not suffer from it as the threshold for the degree of arteriosclerosis, and then use the collected arterial stiffness index data to linearly Regression calculation method is used to obtain the upper limit value of the arteriosclerosis index, the threshold value of the heart rate variability index and the upper limit value of the heavy sine wave ratio of the multiple subjects, and the range of the arteriosclerosis index higher than the upper limit value is regarded as the patient suffering from the disease. A high-risk group indicator for arteriosclerosis-related diseases. Among the multiple subjects, the proportion of men and women who do not suffer from arteriosclerosis-related diseases can be further calculated, and the proportion of men and women who do not suffer from arteriosclerosis can be used as the threshold of arteriosclerosis. By means of linear regression calculation, the upper limit value of the arteriosclerosis index, the threshold value of the heart rate variability index and the upper limit value of the heavy sine wave ratio of the multiple subjects of each age group and each gender are obtained. The range above the upper limit is used as an indicator of a high-risk group suffering from arteriosclerosis-related diseases. The formula for linear regression is:

，其中，

；

,in,

;

Although the upper limit is exceeded, it is likely to belong to the high-risk group of suffering from arteriosclerosis, but it is still impossible to know the location of arteriosclerosis. In this regard, experiments and studies related to heart rate variability pointed out that for patients with coronary artery disease, the total spectral power (TP) in the heart rate variability (HRV) is much smaller than that of normal people, so the reference value of whether the heart rate variability is abnormal , is to use the recommended reference value of heart rate variability (as shown in Table 1) to subtract three standard deviations from the total spectral power (TP) in the recommended normal range to obtain a threshold that the heart rate variability is lower than 99.85% of the public. In Table 1, 3466-1018*3=412. No matter how tired a healthy person is, the total spectral power (TP) cannot be lower than 412, so 412 is regarded as the lower threshold. Diseases arise.

3466±1018*3 LF

1170±416 HF

975±203 表1、心率變異頻域指數正常範圍 parameter unit The normal range obtained from the current experiment TP

3466±1018*3 LF

1170±416 HF

975±203 Table 1. The normal range of heart rate variability frequency domain index

Use the two thresholds of TP value and SI value to establish high-risk groups, medium-risk groups, and low-risk groups. For example, a citizen is 36 years old, and the SI value of 9.7 (the average of all SIs of the subjects) is greater than the confidence interval, If the TP value is also less than the lower limit of heart rate variability, it is determined to be a high-risk group; if the TP value falls within the normal range, it is determined to be a medium-risk group and is informed of the possibility of arteriosclerosis. More in-depth coronary examination should be carried out as soon as possible, and the judgment principles are shown in Table 2.

risk group condition high risk 1. The SI value is higher than the upper limit value 2. The TP value is lower than the set lower limit value medium risk 1. The SI value is above the upper limit 2. The TP falls within the normal range low risk 1. SI value is within normal range or lower than normal range 2. TP falls within normal range Table 2. Risk group comparison table

According to the judgment principle of Table 2, the present invention can be further applied to perform PPG measurement for patients of all ages and genders who have been diagnosed with cardiovascular disease and count the occurrence ratio of heavy sine waves, thereby determining a high risk ratio (such as 55% ), as a reference for further judging whether the subject is a high-risk group suffering from arteriosclerosis-related diseases. Finally, according to the proportion of SI exceeding the upper limit, the TP value and the occurrence proportion of heavy sine waves within the measured time (for example, 100 seconds), the risk level of the subject suffering from arteriosclerosis-related diseases is determined, as shown in Table 3 below, the higher the risk index It means that the subjects have a higher risk of developing arteriosclerosis-related diseases.

The proportion of SI distribution of the subjects in the measurement time Whether the TP is below the normal range Heavy sine wave ratio Hazard index All SIs are below the upper limit no 0% 0 All SIs are below the upper limit Yes 0% 0 but it is recommended to rest immediately More than 50% of the SI distribution ratios are below the upper limit no Below the high risk ratio 0 More than 50% of the SI distribution ratios are below the upper limit Yes Below the high risk ratio 1 (It is recommended to measure again after a full rest) More than 50% of the SI distribution ratios are below the upper limit no higher than the high risk ratio 2 More than 50% of the SI distribution ratios are below the upper limit Yes higher than the high risk ratio 3 (It is recommended to measure again after a full rest) More than 50% of the SI distribution ratios are higher than the upper limit no Below the high risk ratio 3 More than 50% of the SI distribution ratios are higher than the upper limit Yes Below the high risk ratio 4 (It is recommended to measure again after a full rest) More than 50% of the SI distribution ratios are higher than the upper limit no higher than the high risk ratio 4 More than 50% of the SI distribution ratios are higher than the upper limit Yes higher than the high risk ratio 5 Unable to measure 100% 5 Table 3. Reference table for the degree of arteriosclerosis

In application, the wearable detection device 10 is responsible for collecting the relevant physiological information of the individual in real time, storing the physiological information in the subject's mobile monitoring device 20, and analyzing and diagnosing through the latest big data analysis results of the cloud server, At the same time, personal physiological information is regularly provided to the cloud server 30 for big data analysis. Therefore, the cloud server 30 can not only collect big data through a large number of subjects' movement monitoring devices 20 , but also receive statistical data of various regional health care units at any time, so as to provide the subjects with the latest and complete reference indicators at any time.

In practical applications, the mobile monitoring device 20 can not only receive personal physiological data from the wearable detection device 10, and perform calculation processing to establish the monitoring of the subject's physical condition, cardiovascular disease risk, long-term depression/manic-depression/excessive In addition to the analysis of labor status, the updated information of the cloud server 30 can be used to update the parameters of various judgment functions in real time according to the increasingly complete big data data, so as to generate more accurate monitoring judgments.

With the above-mentioned arteriosclerosis index analysis system, the following describes the arteriosclerosis index analysis method of the present invention with reference to FIG. 2 to FIG. 5 .

The method of the present invention comprises the following steps:

S11: Divide the heart rate signal HR into continuous complex sub-heart rate signals HR1, and generate a frequency pulse signal HF1 for each sub-heart rate signal HR1 through Fourier transform;

S12: Calculate an S value of each sub-heart rate signal HR1, the S value represents:

；

;

where A _first-f is the intensity value of the frequency pulse signal HF1 at one frequency, and A _second-f is the intensity value of the frequency pulse signal HF1 at twice the frequency; if the S value is greater than or equal to a first threshold, remove the the sub-heart rate signal HR1; if the S value is less than the first threshold, execute the next step;

S13: Compare a time ratio with a second threshold; wherein a first time difference ΔT ₁ and a second time difference ΔT ₂ of the sub-heart rate signal HR1 are calculated, and the first time difference ΔT ₁ is divided by the second time difference ΔT ₂ to obtain the time ratio, wherein the first time difference ΔT ₁ represents the time difference between a heart rate main peak MP1 and a heart rate sub-peak SP1 of the sub-heart rate signal HR1, and the second time difference ΔT ₂ represents the heart rate main peak of the sub-heart rate signal HR1. The time difference between the peak MP1 and a main trough MH1; if the time ratio is less than a second threshold, execute the next step; if the time ratio is greater than or equal to a second threshold, return to step S11; calculate the _first time difference ΔT1 and The method of the second time difference ΔT ₂ can generate a differential signal by first differentiating the sub-heart rate signal, and find the slope of 0 in the differential signal, namely the main heart rate peak MP1, the heart rate sub-peak SP1 and the main trough MH1 The first time difference ΔT ₁ is calculated from the heart rate main peak MP1 to the heart rate sub-peak SP1, and the second time difference is calculated from the heart rate sub-peak SP1 to the main wave trough MH1. ΔT ₂ .

S14: Calculate an average arteriosclerosis index of the subject; if the time ratio is less than the second threshold, calculate the arteriosclerosis index (SI) according to the subject's height and the _first time difference ΔT1, and calculate After a plurality of the arteriosclerosis indices are obtained, an average value of the arteriosclerosis indices is obtained by taking the average of the arteriosclerosis indices, wherein the calculation formula of the arteriosclerosis index is:

，其中h為該受測者的身高；

, where h is the subject's height;

S15: Calculate the ratio of the heavy sine wave; divide the number of the sub-heart rate signal HR1 whose attribute is the heavy sine wave by the total number of all the sub-heart rate signals to obtain the ratio of the heavy sine wave.

Referring to FIG. 3A , in step S11 , the wearing detection device 10 detects the subject and sends the heart rate signal HR to the mobile monitoring device 20 , and the mobile monitoring device 20 divides the heart rate signal HR into consecutive plural numbers The sub-heart rate signal HR1, that is to say, the heart rate signal HR is composed of the complex sub-heart rate signals HR1. Referring further to FIG. 3B , the motion monitoring device 20 performs a Fourier transform on each sub-heart rate signal HR1 to obtain the frequency pulse signal HF1 . In other words, each sub-heart rate signal HR1 corresponds to a frequency pulse signal HF1 . In this step, the sub-heart rate signal HR1 in the time domain is subjected to Fourier transform to generate the frequency pulse signal HF1 in the frequency domain. It can be seen from FIG. 3B that the frequency pulse signal HF1 has two peaks (intensities), one of which is located at the occurrence of a double frequency, and the other peak is located at the occurrence of twice the frequency.

In step S12, the motion monitoring device 20 extracts the intensity value of the frequency pulse signal HF1 at one frequency and the intensity value at twice the frequency, and calculates the intensity value of the frequency pulse signal HF1 at one frequency and the frequency at two times. The S value is obtained by the ratio between the intensity values of the multiplied frequency. The mobile monitoring device 20 then calculates the magnitude relationship between the S value and the first threshold. When the S value is greater than or equal to the first threshold, the mobile monitoring device 20 discards the sub-heart rate signal HR1 corresponding to the S value; If the value is less than the first threshold, the motion monitoring device 20 retains the sub-heart rate signal HR1 corresponding to the S value and executes the next step. The reason for discarding the S value greater than or equal to the first threshold is that the S value greater than or equal to the first threshold represents that the difference between the intensity value of the frequency pulse signal HF1 at one frequency and the intensity value at twice the frequency is too large, indicating that The frequency pulse signal HF1 belongs to the heavy sine wave, and the heavy sine wave cannot be used to calculate the arteriosclerosis index (SI). ) accuracy.

The following takes the actual operation as an example to introduce. Please refer to FIG. 3A and FIG. 3B. In FIG. 3A, the heart rate signal HR obtained by the wearable detection device 10 using PPG technology is presented, and the detection object is a subject with normal blood vessel function. The heart rate signal is mostly composed of dichotomous waves. FIG. 3B is a frequency domain diagram of generating the frequency pulse signal HF1 after performing step S11 on one of the sub heart rate signals HR1. It can be seen that the double frequency is 1.75 Hz, and the peak intensity (A _first-f ) of the double frequency is 2168; the double frequency roughly falls at 3.375Hz, the peak intensity (A _second-f ) of the double frequency is 1590, the first threshold is preset to 3.5, the S value is 2168/1590=1.36, and 1.36<3.5, This means that the intensity value of one frequency is not much different from that of twice the intensity. The frequency pulse signal HF1 has two peaks of obvious intensity, which means that the sub-heart rate signal HR1 may be a dichotomous wave, so the heart rate signal HR has a certain degree of intensity. The reference value can continue to the next step.

Please refer to FIG. 4A and FIG. 4B . Also shown in FIG. 4A is the heart rate signal HR obtained by the wearable detection device 10 using PPG technology. The detection object is a subject with abnormal vascular function, so it can be seen in FIG. 4A The heart rate signal HR is mostly composed of sub-heart rate signals HR2 belonging to heavy sine waves, and FIG. 4B is a frequency domain diagram of the frequency pulse signal HF2 generated by performing step S11 on one of the sub-heart rate signals HR2. As can be seen in Figure 4B, the double frequency is 1.25 Hz, and the intensity of the double frequency (A _first-f ) is 3271; the double frequency falls roughly at 2.5 Hz, and the intensity of the double frequency (A _second-f ) is 791.6, the first threshold is preset to 3.5, and the S value is 3271/791.6=4.13≧3.5, which means that the difference between the intensity value of one frequency and the intensity value of twice is too large, and the frequency pulse signal HF2 only has an obvious The peak of the intensity represents that the sub-heart rate signal HR2 is a double sine wave, so the sub-heart rate signal HR2 has no reference value, and the motion monitoring device 20 discards the sub-heart rate signal HR2.

The first threshold is set to 3.5, which is a better value obtained according to the results of multiple experiments. On the premise that the first threshold is preset to 3.5, the sub-heart rate of the heavy sine wave and the dichotomous wave can be more accurately screened out. Signals HR1 and HR2, so in practical applications, the first threshold is preferably preset to 3.5.

Although in step S12, the sub-heart rate signal HR2 that is a heavy sine wave is found and discarded, the complex sub-heart rate signal HR1 that has not been discarded may also be a relatively inconspicuous heavy sine wave, so it is necessary to take out the unrejected sub-heart rate signal HR1. Step S13 is performed on the complex sub-heart rate signal HR1, so that the sub-heart rate signal HR1 that is really a dichotomous wave can be more accurately screened.

Referring to FIG. 5 , in step S13 , the motion monitoring device 20 differentiates the sub-heart rate signal HR1 retained in step S12 every time, so each sub-heart rate signal HR1 generates a differentiated signal. In terms of waveform, the sub-heart rate signal HR1 has the main heart rate peak MP1, the heart rate sub-peak SP1 and the main trough MH1. The motion monitoring device 20 calculates the first time difference ΔT ₁ from the heart rate main peak MP1 to the heart rate secondary peak SP1 , and the second time difference ΔT ₂ from the heart rate main peak MP1 to the main heart trough MH1 . The activity monitoring device 20 calculates the ratio of the first time difference ΔT ₁ and the second time difference ΔT ₂ to obtain the time ratio, and compares the time ratio with the second threshold. If the time ratio is smaller than the second threshold, it means In the sub-heart rate signal HR1, the first time difference ΔT ₁ between the main heart rate peak MP1 and the heart rate sub-peak SP1 is relatively long, which is equivalent to that the sub-heart rate signal HR1 has a relatively obvious main heart rate peak MP1 and the heart rate sub-peak SP1. If the wave peak SP1, it can be determined that the sub-heart rate signal HR1 belongs to the dichotomous wave, and the motion monitoring device 20 retains the sub-heart rate signal HR1; on the contrary, if the time ratio is greater than or equal to the second threshold, it means that the sub-heart rate signal HR1 is in the sub-heart rate signal HR1. , the first time difference ΔT ₁ between the main heart rate peak MP1 and the heart rate sub-peak SP1 is relatively short, which is equivalent to that the sub-heart rate signal HR1 has only one obvious peak (the peak cannot be distinguished as the main heart rate peak MP1 or the heart rate sub-peak SP1). Sub-peak SP1), it can be determined that the sub-heart rate signal HR1 belongs to a heavy sine wave, and the motion monitoring device 20 discards the sub-heart rate signal HR1. After many experiments and calculations, it is found that when the second threshold is 80%, it is more accurate to distinguish the diploid wave and the heavy sine wave. Therefore, in practical applications, the second threshold is preset to 80%.

In step S14 , the motion monitoring device 20 takes out the first time difference ΔT ₁ of the sub-heart rate signal HR1 retained, and substitutes the first time difference ΔT ₁ into the calculation formula of the arterial stiffness index and compares the multiple values of the arterial stiffness The index is averaged, that is, the average value of the arteriosclerosis index of the subject can be obtained. For example, in step S11, the wearable detection device 10 divides the heart rate signal HR into 10 sub-heart rate signals HR1, HR2, in step 12, eliminates one sub-heart rate signal HR2 whose attribute is a heavy sine wave, and in step In S13, one more stroke of the sub-heart rate signal HR2 whose attribute is a heavy sine wave is eliminated, so eight strokes of the sub-heart rate signal HR1 are reserved. The motion monitoring device 20 takes out the respective first time differences ΔT ₁ of the eight sub-heart rate signals HR1 and substitutes them into the calculation formula of the arterial stiffness index to obtain the eight arterial stiffness indices. The motion monitoring device 20 calculates the average value of the eight arterial stiffness indices, that is, obtains the average value of the arterial stiffness index of the subject.

)。In step S15, since the sub-heart rate signal HR1 belonging to the heavy sine wave and the sub-heart rate signal HR2 belonging to the heavy sine wave are distinguished in steps S12 and S13, the motion monitoring device 20 further identifies the sub-heart rate signal HR1 that belongs to the heavy sine wave. The number of this sub-heart rate signal HR2 is compared with the total number of all sub-heart rate signals HR1 and HR2 to obtain the ratio of the heavy sine wave. For example, in step S11, the wearable detection device 10 distinguishes the heart rate signal into 10 sub-heart rate signals HR1 and HR2, and in step 12, eliminates one sub-heart rate signal HR2 whose attribute is a double sine wave, and in step S13 Then eliminate a sub-heart rate signal HR2 whose attribute is a heavy sine wave, so there are two strokes of the sub-heart rate signal HR2 belonging to a heavy sine wave, and the ratio of the heavy sine wave can be obtained as 20% (

).

Using the above steps, the present invention can filter the heart rate signal HR, identify and exclude the wave band of the heavy sine wave, leave the wave band of the dichotomous wave, and use the first time difference ΔT ₁ of the dichotomous wave to calculate the average value of the arteriosclerosis index , to prevent the incorrect average value of the arteriosclerosis index from being calculated due to heavy sine waves. In step S12, firstly convert each sub-heart rate signal HR1 from the time domain to the frequency domain, and calculate the S value of each sub-heart rate signal, and then evaluate the size of the S value and the first threshold, the elimination intensity is too high The S value of , and then differentiate each sub-heart rate signal HR1 once to obtain a dichotomous wave, and calculate the first time difference ΔT ₁ between the main heart rate peak MP1 and the heart rate sub-peak SP1 in the dichotomous wave, and this The second time difference ΔT ₂ between the main heart rate peak MP1 and the main trough MH1 is compared to filter out the waveform of the heavy sine wave again, so that the heavy sine wave can be effectively found and discarded, the heavy beat wave is retained, and finally the heavy sine wave is used. The pulse wave calculates multiple accurate arteriosclerosis indices, and averages the multiple arteriosclerosis indices to obtain the correct mean value of the arteriosclerosis indices.

In addition to the above-mentioned arteriosclerosis risk assessment system, the arteriosclerosis index analysis system of the present invention can further perform another arteriosclerosis index measurement step that removes the sine wave. After the arteriosclerosis risk assessment system is executed, further execution The step can assist in removing the wave band of the heavy sine wave, ensuring that the remaining wave bands are all dithermic waves, which is more accurate when calculating the arteriosclerosis index (SI) and the average value of the arteriosclerosis index.

Please refer to FIG. 6 to FIG. 14B , another method for measuring the arterial stiffness index by removing the heavy sine wave of the present invention includes the following steps:

Please refer to FIG. 6 and FIG. 7A, S21: establish an auxiliary line SL of the sub-heart rate signal HR1, and the auxiliary line SL extends linearly from a main heart rate peak MP2 of the sub-heart rate signal HR1 to a main heart rate trough MH2; wherein the heart rate The main peak MP2 represents the maximum amplitude of the sub-heart rate signal HR1, and the main heart rate trough MH2 represents the lowest amplitude of the sub-heart rate signal HR1;

S22: Create an auxiliary line distance signal SLS and calculate the number of valleys of the auxiliary line distance signal SLS; please refer to FIG. 7B , the motion monitoring device 20 differentiates the sub-heart rate signal HR1 once to obtain the differentiated signal HD1, and takes the differentiated signal HD1 a first time point _t1 and a second time point _t2 , and calculate the distance from the auxiliary line SL to the sub-heart rate signal HR1 between the first time point _t1 and the second time point _t2 ; A time point _t1 is the occurrence time of the first trough of the differential signal HD1 along the positive direction of the time axis, and the second time point _t2 is the occurrence time of the main heart rate trough MH2. Referring to FIG. 7C , the auxiliary line distance signal SLS represents all vertical distances between the auxiliary line SL and the sub-heart rate signal HR1 in the interval from the first time point _t1 to the second time point _t2 . distance function.

7A to 7C present the process of actually performing steps S21 and S22 on the sub-heart rate signal HR1 belonging to the dichotomous wave. In FIG. 7C, it can be found that the auxiliary line distance signal SLS has two obvious valley points H1 and H2. It means that the sub-heart rate signal HR1 may belong to the dichotomous wave.

Hereinafter, steps S21 and S22 are performed using the sub-heart rate signal HR2 of the heavy sine wave as an actual example. Referring to FIG. 8A , in step S21 , the auxiliary line SL of the sub-heart rate signal HR2 is first established, and the auxiliary line SL extends linearly from the main heart rate peak MP2 of the sub-heart rate signal HR2 to the main heart rate trough MH2 . Referring to FIG. 8B, in step S22, the sub-heart rate signal HR2 is differentiated once to obtain the differentiated signal HD2, a first time point t ₁ and a second time point t ₂ of the differentiated signal HD2 are taken, and calculated at the first time The distance from the auxiliary line SL to the sub-heart rate signal HR2 between a time point _t1 and the second time point _t2 . It can be found in FIG. 8C that the distance between the auxiliary line and the signal SLS has only an obvious trough, which means that the sub-heart rate signal HR2 belongs to a double sine wave.

Although the sub-heart rate signals HR1 and HR2 can be distinguished in steps S21 and S22 as a dichotomous wave or a heavy sine wave, when the waveforms of the sub-heart rate signals HR1 and HR2 are actually measured, it is found that the sub-heart rate signals HR1 and HR2 may It is a heavy sine wave or an indistinct diploic wave (a waveform that approximates a heavy sine wave but is actually a dichotomous wave). Taking the actual sub-heart rate signal as an example, please refer to FIG. 9A. After the sub-heart rate signal HR3 establishes the auxiliary line SL, the auxiliary line distance signal SLS as shown in FIG. 9B is obtained. It can be found that the auxiliary line distance signal SLS has An obvious valley point H4, and an inflection point H5, whether the inflection point H5 is a valley point will affect the judgment of what kind of waveform the sub-heart rate signal HR3 belongs to. is a heavy sine wave. Therefore, it is necessary to further quote the calculation formula of the Bezier curve, and to adopt different judgment and calculation methods for the heavy beat wave, the inconspicuous heavy beat wave and the heavy sine wave. In the following introduction, step S23 is a method for calculating the Bezier curve, step S231 is a calculation process for diploid waves, and step S232 is a calculation process for inconspicuous diploid waves and heavy sine waves.

Please refer to Fig. 10A to Fig. 10C, S23: Calculate a Bezier curve, the calculation formula of the Bezier curve is:

；

;

And take the first time point t ₁ , that is, the first trough occurrence time (t ₁ ) of the auxiliary line distance signal SLS along the positive time axis, to obtain B(t ₁ ), and take the second time point t ₂ , that is, B(t ₂ ) is obtained from the occurrence time (t ₂ ) of the second trough of the auxiliary line distance signal SLS along the positive direction of the time axis.

；in,

;

Please refer to FIG. 10B , P ₀ is the first valley point of the sub-heart rate signal HR1 in the positive direction of the time axis after being differentiated once; please refer to FIG. 10A , P ₃ is the amplitude of a third time point t ₃ , the third Time point _t3 is the time when the main trough of the heart rate occurs; P1 is a _first control point, and the first trough occurrence time ( _t1 ) of the auxiliary line distance signal SLS along the positive time axis is substituted into the Bezier curve is obtained by the calculation formula of (that is, B(t ₁ )); P ₂ is a second control point, which is substituted into the second trough occurrence time (t ₂ ) of the auxiliary line distance signal SLS along the positive time axis. obtained from the formula for the Purcell curve (ie, B(t ₂ )).

S231: Substitute P ₀ , P ₃ , B(t ₁ ), and B(t ₂ ) into the calculation formula of the Bezier curve and perform three operations to obtain P ₁ , P ₂ , that is, according to P ₁ , P ₂ , P ₀ and P ₃ obtain the Bezier curve BC1 of the sub-heart rate signal HR1 (shown in FIG. 10A ). This method is applicable to the sub-heart rate signal HR1 (shown in FIG. 10C ) where the auxiliary line distance signal SLS has two obvious troughs, which means that the sub-heart rate signal HR1 is a dichotomous wave.

11A to 11C, S232: Substitute P ₀ , P ₃ , B(t ₁ ), and B(t ₂ ) into the calculation formula of the Bezier curve and perform three operations to obtain P ₁ and P ₂ , namely The Bezier curve BC2 (shown in FIG. 11A ) of the sub-heart rate signal HR2 can be obtained according to P ₁ , P ₂ , P ₀ , and P ₃ . where B(t ₂ ) is calculated as:

；

;

This method is applicable to the sub-heart rate signal HR2 (shown in FIG. 11C ) when the auxiliary line distance signal SLS has only an obvious trough, which means that the sub-heart rate signal HR2 is a double sine wave or an inconspicuous dichotomous wave.

S24: Compare the correlation between the Bezier curve and the heart rate signal, and if a correlation coefficient between the Bezier curve and the sub-heart rate signal HR1 is less than a third threshold, calculate the arterial stiffness index of the sub-heart rate signal HR1 (SI); if the correlation coefficient between the Bezier curve and the sub-heart rate signal HR2 is greater than or equal to a third threshold, the sub-heart rate signal HR2 is discarded. The third threshold may be set to 99.85%.

Among them, the calculation method of the correlation coefficient is:

；

;

為相關係數，n為取樣個數，x代表該心率信號在相同時點下的y軸座標值；y代表該貝賽爾曲線在相同時點下的y軸座標值。

is the correlation coefficient, n is the number of samples, x represents the y-axis coordinate value of the heart rate signal at the same time point; y represents the y-axis coordinate value of the Bezier curve at the same time point.

Referring to FIG. 12A, in an actual application, the solid line represents the heart rate signal HR1 actually measured by a subject, the dotted line is the Bezier curve BC1 derived from the heart rate signal HR1, the heart rate signal HR1 and the heart rate signal HR1 The correlation coefficient of the Bezier curve BC1 is 99.49%, which is less than the third threshold (99.85% by default), which means that the heart rate signal HR1 is not correlated with the Bezier curve BC1, and in FIG. 12A, the heart rate signal HR1 has no correlation. The range in which the amplitude is larger than the amplitude of the Bezier curve BC1 (the range between the two dotted chain lines) is the range in which the dichotomous wave occurs. Referring to FIG. 12B , the sub-peak SP1 of the heart rate of the dichotomous wave can be found by first-order differentiation of the interval, and the sub-peak SP1 of the heart rate can be substituted into step S13 to further calculate the arteriosclerosis index (SI). It can be seen from FIG. 12A and FIG. 12B that the position of the heart rate sub-peak SP1 is indeed in the range where the amplitude of the heart rate signal HR1 is greater than the amplitude of the Bezier curve BC1 .

Referring to FIG. 13A , in another practical application, the solid line represents the heart rate signal HR2 actually measured by a subject, and the dashed line represents the Bezier curve BC2 derived from the heart rate signal HR2. The correlation coefficient between the signal HR2 and the Bezier curve BC2 is 99.96%, which is greater than the third threshold (99.85% by default), indicating that the heart rate signal HR2 and the Bezier curve BC2 almost overlap, indicating that the heart rate signal HR2 is a heavy chord Therefore, the heart rate signal HR2 whose correlation coefficient with the Bezier curve BC2 is too high can be discarded. If the heart rate signal HR2 is differentiated once, an unknown point N as shown in FIG. 13B will be obtained, and the unknown point N does not fall within the overlapping interval between the heart rate signal HR2 and the Bezier curve BC2 (between the two dotted chain lines). range), but falls at the position of the non-Bézier curve, so whether the heart rate signals HR1 and HR2 are heavy sine waves can be accurately distinguished by using the Bezier curve.

The reason for calculating the Bezier curve is that the Bezier curve can be regarded as an imaginary double sine wave curve of the sub-heart rate signals HR1 and HR2. By calculating the correlation coefficient, it can be further known that the Bezier curve and the sub-heart rate signal HR1 , HR2 overlap rate (correlation level). If the overlap ratio between the Bezier curve and the sub-heart rate signal HR1 is low, the correlation coefficient of the sub-heart rate signal HR1 will be smaller than the third threshold, indicating that the Bezier curve has a low correlation with the sub-heart rate signal HR1, and the sub-heart rate signal HR1 has a low correlation. The heart rate signal HR1 is not exactly the same as the imaginary heavy sine wave curve, and the sub-heart rate signal HR1 is the heavy beat wave. If the overlap rate of the Bezier curve and the sub-heart rate signal HR2 is high, the correlation coefficient of the sub-heart rate signal HR2 will be greater than or equal to the third threshold, indicating that the Bezier curve has a high correlation with the sub-heart rate signal HR2, and the The sub-heart rate signal HR2 is almost identical to the imaginary double sine wave curve, and the sub heart rate signal HR2 is a double sine wave.

Please refer to FIG. 14A, S25: take the first interval along the positive time axis where the amplitude of the heart rate signal HR4 is greater than the amplitude of the Bezier curve BC4, differentiate the sub-heart rate signal HR4 in the interval once, and the interval along the time The first wave peak in the positive axis is the differential sub-peak, and the arterial stiffness index is calculated by taking the time difference between the primary heart rate sub-peak corresponding to the occurrence time of the differential sub-peak and the main heart rate peak.

Please refer to FIG. 14A and FIG. 14B , the heart rate signal HR4 includes a main heart rate peak MP4 , the heart rate secondary peak SP4 and a third heart rate peak TP4 . The frequency pulse signal HD4 is obtained after the heart rate signal HR4 is differentiated once, and the frequency pulse signal HD4 includes a main differential peak MP5 , a differential secondary peak SP5 and a third differential peak TP5 . Since the differential sub-peak SP5 is closer to the differential main peak MP5 than the third differential peak TP5, the heart rate sub-peak SP4 corresponding to the occurrence time of the differential sub-peak SP5 is a real dichotomous wave, and the heart rate sub-peak SP4 is taken as the heart rate sub-peak SP4. The arteriosclerosis index is calculated from the time difference between the peak SP4 and the main heart rate peak MP4 (the first time difference ΔT ₁ ).

The reason for performing step S25 is that some waveforms of the heart rate signal HR4 may oscillate, and the reason may be that they are affected by external forces during measurement. For example, the heart rate signal HR4 in FIG. The stroke range (the range in which the amplitudes of the two heart rate signals HR4 are greater than the amplitudes of the Bezier curve BC4), and the correlation coefficient between the heart rate signal HR4 and the Bezier curve BC4 is 99.73%, less than 99.85%, indeed Therefore, the peak closest to the differential main peak MP5, that is, the differential sub-peak SP5, must be taken as the position of the dichotomous wave, so as to avoid finding a wrong dichotomous wave peak due to waveform oscillation.

In the present invention, the wearable detection device 10 obtains the heart rate signal of the subject, and then uses the cloud data to calculate the upper limit of the arteriosclerosis index for different age groups. As long as the age and height are input, the subject can be calculated. The arteriosclerosis index, the heart rate variability characteristic value and the ratio of the heavy sine wave of the person, and compared with the upper limit value of the arteriosclerosis index, the heart rate variability index threshold value and the upper limit value of the heavy sine wave ratio to generate the The risk index of the subjects allows individuals to easily assess their own arteriosclerosis in their daily life, so as to seek medical treatment in time before emergencies occur. Furthermore, the method steps of the present invention can remove the heavy sine wave to avoid errors in the calculation of the arteriosclerosis index due to the heavy sine wave generated by the human body when calculating the arteriosclerosis index, and the calculated arterial stiffness index is more accurate.

10: Wearing detection device 11: Control unit 13: Light transmitting unit 15: Light receiving unit 20: Motion monitoring device 30: Cloud server 40: Dichotomous wave 50: Double sine wave 501: First derivative double sine wave HR: Heart rate Signal HR1, HR2, HR3, HR4: Sub heart rate signal HF1, HF2: Frequency pulse signal HD1, HD2: Differential signal ΔT ₁ : First time difference ΔT ₂ : Second time difference MP5: Differential main peak SP5: Differential sub-peak MH1: Main Trough SL: Auxiliary line MP1, MP2, MP4: Heart rate main peak SP1, SP2, SP4: Heart rate secondary peak TP4, TP5: Third heart rate peak MH2: Heart rate main trough SLS: Auxiliary line distance signal t ₁ : The first time point t ₂ : the second time point t ₃ : the third time point H1, H2, H4: the valley point H5: the inflection point P ₀ : the amplitude of the first time point P ₃ : the amplitude of the third time point P ₁ : the first control point P ₂ : Second control point BC1, BC2: Bezier curve N: Unknown point ΔT: Time difference between the main peak of the pulse and the dichotomous wave

FIG. 1 is a schematic block diagram of a system implementing the present invention. Fig. 2 is a flow chart of the steps of the arteriosclerosis index analysis method of the present invention. Figure 3A: The waveform diagram of the heart rate signal belonging to the dichotomous wave. FIG. 3B : Fourier-transformed waveform diagram of the heart rate signal belonging to the dichotomous wave. Figure 4A: The waveform diagram of the heart rate signal belonging to the heavy sine wave. FIG. 4B : Fourier-transformed waveform diagram of the heart rate signal belonging to the heavy sine wave. Figure 5: Waveform diagram of the heart rate signal belonging to the dichotomous wave. FIG. 6 is a flow chart of the steps of another method for measuring arteriosclerosis index by removing the heavy sine wave according to the present invention. Fig. 7A: The waveform diagram of the auxiliary line for the establishment of heart rate signals belonging to dichotomous waves. FIG. 7B is a waveform diagram of the first derivative of the heart rate signal of FIG. 7A . FIG. 7C is a waveform diagram of the auxiliary line distance signal established according to the heart rate signal of FIG. 7A . Fig. 8A: The waveform diagram of the auxiliary line for the establishment of the heart rate signal belonging to the heavy sine wave. FIG. 8B is a waveform diagram of the first derivative of the heart rate signal of FIG. 8A . FIG. 8C is a waveform diagram of the auxiliary line distance signal established according to the heart rate signal of FIG. 8A . Figure 9A: Heart rate signal and auxiliary line waveforms of insignificant dichotomous waves. FIG. 9B is a waveform diagram of the auxiliary line distance signal established according to the heart rate signal of FIG. 9A . Figure 10A: The heart rate signal and Bezier curve waveforms belonging to the dichotomous wave. FIG. 10B is a waveform diagram of the first derivative of the heart rate signal of FIG. 10A . FIG. 10C is a waveform diagram of the auxiliary line distance signal established according to the heart rate signal of FIG. 10A . Fig. 11A: A waveform diagram of a heart rate signal belonging to a heavy sine wave and a Bezier curve. FIG. 11B is a waveform diagram of the first derivative of the heart rate signal of FIG. 11A . FIG. 11C is a waveform diagram of the auxiliary line distance signal established according to the heart rate signal of FIG. 11A . Fig. 12A: The waveform diagram of the overlapping interval between the heart rate signal belonging to the dichotomous wave and the Bezier curve. FIG. 12B : a first-order differential waveform diagram of the heart rate signal belonging to the dichotomous wave and the Bezier curve overlapping section. Fig. 13A: The waveform diagram of the overlapping interval between the heart rate signal belonging to the heavy sine wave and the Bezier curve. FIG. 13B : a first-order differential waveform diagram of a heart rate signal belonging to a heavy sine wave and a Bezier curve overlapping interval. Figure 14A: Heart rate signal and Bezier curve waveform diagram with double beat waves. FIG. 14B is a first derivative waveform diagram of the heart rate signal of FIG. 14A . Figure 15: Waveform diagram of the heart rate signal belonging to the dichotomous wave. Fig. 16A: A waveform diagram of a heart rate signal belonging to a heavy sine wave. Fig. 16B: The first-order differential waveform of the heart rate signal of Fig. 16A

10: Wearing detection device

11: Control unit

13: Light Emitting Unit

15: Light receiving unit

20: Motion Monitoring Device

30: Cloud server

Claims (9)

An arteriosclerosis risk assessment system comprising: a wearable detection device for detecting a heart rate signal of a subject; A mobile monitoring device wirelessly connected to the wearable detection device, the mobile monitoring device is used for receiving the heart rate signal, and calculates an arteriosclerosis index, a heart rate variability index and the ratio of the heavy sine wave according to the heart rate signal, and calculates the The arterial stiffness index, the heart rate variability index, and the ratio of the heavy sine wave are compared with an upper limit value of the arteriosclerosis index, a threshold value of a heart rate variability index, and an upper limit value of the heavy sine wave ratio, and then converted into a risk index according to the comparison result. ; a cloud server, wirelessly connected to the mobile monitoring device, and used for receiving the arteriosclerosis index of each of the plurality of subjects, and calculating the upper limit value of the arteriosclerosis index. The arteriosclerosis risk assessment system of claim 1, wherein the motion monitoring device performs the following steps to obtain the arteriosclerosis index: dividing the heart rate signal into continuous complex sub-heart rate signals, and performing Fourier transform on each sub-heart rate signal Generate a frequency pulse signal; Calculate an S value of each sub-heart rate signal, the S value represents:

; Wherein A _first-f is the intensity value of the frequency pulse signal at one frequency, and A _second-f is the intensity value of the frequency pulse signal at twice the frequency; If the S value is less than the first threshold, compare a time ratio and a second threshold; wherein, a first time difference and a second time difference of the sub-heart rate signal are calculated, and the first time difference is divided by the second time difference to obtain the time ratio, and the first time difference represents the sub-heart rate The time difference between a heart rate main peak and a heart rate sub-peak of the signal, the second time difference represents the time difference between the heart rate main peak and a main trough of the sub-heart rate signal; Calculate the artery according to the height of a subject and the first time difference The hardening index, where the formula for calculating the arterial stiffness index is:

, where SI is the arterial stiffness index, h is the subject's height, and ΔT ₁ is the first time difference. According to the arteriosclerosis risk assessment system according to claim 2, the motion monitoring device adds up and averages a plurality of arteriosclerosis indices to obtain the average value of the arteriosclerosis indices. The arteriosclerosis risk assessment system as claimed in claim 3, wherein the motion monitoring device further performs a step of removing heavy sine waves, comprising: An auxiliary line of the sub-heart rate signal is established, and the auxiliary line extends straight from a main heart rate peak of the sub-heart rate signal to a main heart rate trough; wherein the main heart rate peak represents the maximum amplitude of the sub-heart rate signal, and the main heart rate trough Represents the lowest amplitude of the sub-heart rate signal; establishing an auxiliary line distance signal and calculating the number of valleys of the auxiliary line distance signal; Take a first time point and a second time point of a differential signal, and calculate the distance from the auxiliary line to the sub-heart rate signal between the first time point and the second time point, wherein the differential signal is the sub-heart rate signal The waveform of the first derivative of the heart rate signal, the first time point is the occurrence time of the first trough of the differential signal along the positive time axis, the second time point is the occurrence time of the main trough of the heart rate, and the auxiliary line distance signal represents the In the interval from the first time point to the second time point, the distance function formed by all the vertical distances between the auxiliary line and the differential signal; if the number of troughs is 2, it means that the sub-heart rate signal is a dichotomous wave, if the number of troughs is 2 If it is 1, it means that the sub-heart rate signal is a double sine wave. The arteriosclerosis risk assessment system according to claim 4, wherein the motion monitoring device further comprises the following steps when removing the heavy sine wave: Calculating a Bezier curve, the calculation formula of the Bezier curve is:

;

; Wherein, P ₀ is the first valley point along the positive direction of the time axis after the sub-heart rate signal is differentiated once; P ₃ is the amplitude of a third time point, and the third time point is the time when the main heart rate valley occurs; P ₁ is a first control point, obtained by substituting the first trough occurrence time (t ₁ ) of the auxiliary line distance signal along the positive time axis into the calculation formula of the Bezier curve; P ₂ is a second control point point, obtained by substituting the second trough occurrence time (t ₂ ) of the auxiliary line distance signal along the positive time axis into the calculation formula of the Bezier curve; comparing the correlation between the Bezier curve and the heart rate signal, If a correlation coefficient between the Bezier curve and the sub-heart rate signal is less than a third threshold, calculate the arterial stiffness index of the sub-heart rate signal; wherein, the calculation method of the correlation coefficient is:

;

is the correlation coefficient, n is the number of samples, x represents the y-axis coordinate value of the heart rate signal at the same time point; y represents the y-axis coordinate value of the Bezier curve at the same time point. According to the arteriosclerosis risk assessment system according to claim 5, when the motion monitoring device calculates the Bezier curve, if the auxiliary line distance signal has two troughs, P ₀ , P ₃ , B(t ₁ ), B(t ₂ ) is substituted into the calculation formula of the Bezier curve and operated three times to obtain P ₁ , P ₂ , and then the Bezier curve of the sub-heart rate signal is obtained according to P ₁ , P ₂ , P ₀ , P ₃ . According to the arteriosclerosis risk assessment system according to claim 5, when the motion monitoring device calculates the Bezier curve, if the auxiliary line distance signal has a trough, P ₀ , P ₃ , B(t ₁ ), B (t ₂ ) is substituted into the calculation formula of the Bezier curve and operated three times to obtain P ₁ , P ₂ , and then the Bezier curve of the sub-heart rate signal is obtained according to P ₁ , P ₂ , P ₀ , and P ₃ , where B(t ₂ ) is calculated as:

. The arteriosclerosis risk assessment system according to claim 6 or 7, when the motion monitoring device removes the heavy sine wave, further comprises the following steps: If the auxiliary line distance signal has two troughs, take the first interval along the positive time axis where the amplitude of the heart rate signal is greater than the amplitude of the Bezier curve, and differentiate the sub-heart rate signal in the interval once. The first wave peak in the positive direction of the time axis is the differential sub-peak, and the arteriosclerosis index is calculated by taking the time difference between the heart rate sub-peak and the heart rate main peak corresponding to the occurrence time of the differential sub-peak. According to the arteriosclerosis risk assessment system of claim 8, the mobile monitoring device further performs the following steps: Calculate the ratio of the heavy sine wave; divide the number of the sub-heart rate signals whose attribute is a heavy sine wave by the total number of all the sub-heart rate signals to obtain the ratio of the heavy sine wave.

Images