CN114098660A - Arteriosclerosis risk assessment system - Google Patents
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/02007—Evaluating blood vessel condition, e.g. elasticity, compliance
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- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
- A61B5/02405—Determining heart rate variability
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
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- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
- A61B5/02438—Detecting, measuring or recording pulse rate or heart rate with portable devices, e.g. worn by the patient
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Abstract
The invention provides an arteriosclerosis risk assessment system, comprising: a wearable detection device for detecting a heart rate signal of a subject; the action detection device is used for receiving the heart rate signal, calculating according to the heart rate signal to obtain an arteriosclerosis index, a heart rate variation index and a proportion of heavy string waves, respectively comparing an arteriosclerosis index upper limit value, a heart rate variation index threshold value and a proportion upper limit value of heavy string waves, and converting into a danger index according to a compared result; and the cloud server is wirelessly connected with the action detection device and is used for receiving the arteriosclerosis indexes of a plurality of testees and calculating the upper limit value of the arteriosclerosis indexes, so that the effect of easily and quickly evaluating the arteriosclerosis risk of a user is provided.
Description
Technical Field
An arteriosclerosis risk assessment system, in particular to an analysis assessment system which converts a heart rate signal from a time domain to a frequency domain, can eliminate heavy string waves and leave heavy pulse waves to calculate arteriosclerosis indexes.
Background
Arteriosclerosis is a broad term including arteriole, arteria media, Atherosclerosis (Atherosclerosis), most seriously, Atherosclerosis occurs in coronary artery and middle artery, and if the patients do not receive cardiovascular examination regularly, the patients may not know the diseases of arteriosclerosis, and finally, sudden myocardial infarction and stroke are caused.
Therefore, one type of measuring device is a wearable device. A general wearable device is often worn on a wrist of a subject in the form of a bracelet or a Watch, for example, an intelligent wearable device such as a millet bracelet or Apple Watch. The wearable device can detect a heart rate signal (generally referred to as a PPG signal) of the subject by a Photoplethysmography (PPG), and information presented by the heart rate signal includes heartbeat and a blood flow state inside a blood vessel. Through the PPG measurement technology, the heartbeat and the blood flow state of a testee can be rapidly obtained, the arteriosclerosis index (SI) of the testee can be simply measured, and the arteriosclerosis degree of the blood vessel of the testee can be judged, wherein the calculation formula of the arteriosclerosis index is as follows:
SI ═ h/Δ T100%; wherein h is the height of the tested person, and Δ T is the time difference between the main wave peak and the dicrotic wave of the pulse in the PPG signal of the tested person.
Referring to fig. 15, the PPG signal of a heartbeat cycle contains two pulse waves, one is the main peak MP of the pulse wave and the other is the dicrotic wave 40, the formation of the dicrotic wave 40 is caused by the pulse wave being transmitted to the lower body, reflected (rebounded) to the aortic valve and then transmitted to the sensing portion, so the amplitude of the dicrotic wave 40 can show the expansion capability of the artery diameter. When the pulse wave 40 is measured, the time difference delta T between the main peak MP of the pulse and the pulse wave 40 can be further measured, and the arteriosclerosis index can be calculated according to the formula.
Please refer to fig. 16A, but in actual measurement, the subject often finds that the dicrotic wave of the PPG signal is not obvious, this waveform is called heavy sine wave 50, fig. 16A is the waveform of heavy sine wave 50. Referring to fig. 16B, the waveform 501 can be obtained by performing a first differentiation on the sine wave 50, but the wave band of blood rebounded from the blood vessel is still not displayed, the exact position of the counterpulsation wave cannot be found, and instead, the position close to the wave trough is captured, and the time of the wave trough occurrence is later than the time of the counterpulsation wave occurrence, so that the Δ T becomes large, resulting in too low arteriosclerosis index. The reason why the heavy sine wave 50 is generated is that the blood vessel of the subject is aged and hardened seriously or the blood circulation is poor, so that after the heart compresses and pumps blood, although the blood flows into the blood vessel, the blood vessel loses the expansion capability due to hardening, and the blood cannot rebound, so that the heavy sine wave cannot be generated. Statistically, the occurrence of the heavy string wave 50 in the PPG signal of more than 50% of older people with a history of cardiovascular disease is the most important group for tracking the arteriosclerosis index. In addition, technologies claiming to be capable of detecting arteriosclerosis are also available in the market, wherein the cardiovascular state is mainly estimated by detecting HRV performance at all times, reference indexes of the HRV are time domain indexes such as SDNN, SDANN, SDNNIndex and the like, but for accurate judgment by using the time domain indexes, statistics is required to be carried out on PPG signals at all times 24 hours a day, interference caused by irregular heart rate or overactive interaction nerves due to a large amount of activities due to exercise needs to be eliminated, and the method is only suitable for patients who are bedridden in hospitals. Therefore, how to allow an individual to easily evaluate the possibility of suffering from arteriosclerosis in daily life becomes an important goal in cardiovascular science and technology.
Disclosure of Invention
In order to enable a user to easily evaluate the risk of suffering from arteriosclerosis in daily life, the invention provides an arteriosclerosis risk evaluation system.
To achieve the above object, the arteriosclerosis risk assessment system of the present invention comprises:
a wearable detection device for detecting a heart rate signal of a subject;
the action detection device is wirelessly connected with the wearable detection device and is used for receiving the heart rate signal, calculating according to the heart rate signal to obtain an arteriosclerosis index, a heart rate variation index and a proportion of a heavy string wave, respectively comparing the arteriosclerosis index, the heart rate variation index and the proportion of the heavy string wave with an arteriosclerosis index upper limit value, a heart rate variation index threshold value and a heavy string wave proportion upper limit value, and converting into a danger index according to a compared result;
and the cloud server is wirelessly connected with the action detection device and is used for receiving the arteriosclerosis index of each testee in a plurality of testees and calculating the upper limit value of the arteriosclerosis index.
According to the heart rate signal obtained by a photoplethysmography method, the arteriosclerosis index, the heart rate variation index and the proportion of the heavy string wave are further calculated according to the heart rate signal, and an arteriosclerosis index upper limit value, a heart rate variation index threshold value and a heavy string wave proportion risk value are compared; according to the compared result, a risk reference can be provided for the possibility of the arteriosclerosis of the self artery of the user in the life so as to seek medical treatment as early as possible before the sudden situation occurs.
Furthermore, the invention can further eliminate the heavy string wave in the heart rate signal and leave the heavy pulse wave which can calculate the arteriosclerosis index, namely, the error of calculation by using the heavy string wave can be eliminated, the arteriosclerosis index can be calculated by using the pure heavy pulse wave, and the more accurate arteriosclerosis index can be obtained. The invention can further sum up a plurality of arteriosclerosis indexes and then average the arteriosclerosis indexes to obtain the average value of the arteriosclerosis indexes of the testee, thereby avoiding the influence of measurement errors on a single arteriosclerosis index and improving the measurement accuracy.
Drawings
FIG. 1: a block diagram of a system for implementing the present invention.
FIG. 2: the invention provides a flow chart of the steps of an arteriosclerosis index analysis method.
FIG. 3A: belongs to the heart rate signal oscillogram of the dicrotic wave.
FIG. 3B: a Fourier transformed waveform of a heart rate signal belonging to a dicrotic wave.
FIG. 4A: the heart rate signal oscillogram belongs to a heavy string wave.
FIG. 4B: a Fourier transformed waveform of a heart rate signal belonging to a heavy string wave.
FIG. 5: waveform of a heart rate signal belonging to a dicrotic wave.
FIG. 6: the invention also discloses a method for measuring the arteriosclerosis index by removing the heavy string waves.
FIG. 7A: the heart rate signals belonging to the dicrotic wave establish the oscillogram of the auxiliary line.
FIG. 7B: the first differentiated waveform of the heart rate signal of fig. 7A.
FIG. 7C: an auxiliary line distance signal waveform plot established from the heart rate signal of fig. 7A.
FIG. 8A: the heart rate signals belonging to the heavy string waves create a waveform diagram of the aid line.
FIG. 8B: the first differentiated waveform of the heart rate signal of fig. 8A.
FIG. 8C: an auxiliary line distance signal waveform plot established from the heart rate signal of fig. 8A.
FIG. 9A: heart rate signals and auxiliary line oscillograms without significant dicrotic waves.
FIG. 9B: an auxiliary line distance signal waveform plot established from the heart rate signal of fig. 9A.
FIG. 10A: belongs to a heart rate signal and a Bessel curve oscillogram of a dicrotic wave.
FIG. 10B: the first differentiated waveform of the heart rate signal of fig. 10A.
FIG. 10C: an auxiliary line distance signal waveform plot established from the heart rate signal of fig. 10A.
FIG. 11A: the heart rate signal belonging to the heavy string wave and the Bessel curve oscillogram.
FIG. 11B: the first differentiated waveform of the heart rate signal of fig. 11A.
FIG. 11C: an auxiliary line distance signal waveform plot established from the heart rate signal of fig. 11A.
FIG. 12A: the heart rate signal belonging to the dicrotic wave and the Bessel curve superposition interval oscillogram.
FIG. 12B: the heart rate signal belonging to the dicrotic wave and the primary differential oscillogram of the superposition interval of the Bezier curve.
FIG. 13A: and (3) a heart rate signal belonging to the heavy string wave and a Bessel curve superposition interval oscillogram.
FIG. 13B: the heart rate signal belonging to the heavy string wave and the Bessel curve superposition interval primary differential oscillogram.
FIG. 14A: heart rate signals with two beat waves are plotted against bezier curves.
FIG. 14B: the first derivative waveform of the heart rate signal of fig. 14A.
FIG. 15: waveform of a heart rate signal belonging to a dicrotic wave.
FIG. 16A: the heart rate signal oscillogram belongs to a heavy string wave.
FIG. 16B: first order differential waveform diagram for heart rate signal of FIG. 16A
Detailed Description
The invention discloses an arteriosclerosis risk evaluation system which can eliminate heavy string waves in a heart rate signal and leave heavy string waves for calculating an 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 comprises: a wearing detection device 10, a motion detection device 20 and a cloud server 30.
The wearable detection device 10 is wearable on a subject for detecting a heart rate signal of the subject, wherein the heart rate signal is presented in an amplitude-time waveform in a time domain. Generally, the wearing detection device 10 can be a smart bracelet that the subject can wear on his wrist. The wearable sensing device 10 obtains the heart rate signal through Photoplethysmography (PPG), which may include the condition of the heart beat 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, and the light emitting unit 13 and the light receiving unit 15 are electrically connected to the wearing detection unit 10, respectively. 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 detecting light to the blood in the blood vessel of the subject, and the blood in the blood vessel reflects the detecting light to form a reflected light; the light receiving unit 15 receives the reflected light and transmits the reflected light 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 detecting 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 the relationship of amplitude (intensity of the reflected light) and time, i.e. the heart rate signal.
The action detection device 20 is wirelessly connected to the wearable detection device 10 and also held by the subject, and the action detection device 20 can be a mobile phone or a tablet, and can be connected to the wearable detection device 10 via bluetooth. The action detection device 20 is configured to receive the heart rate signal, calculate a ratio of an arteriosclerosis index, a heart rate variability index and a weight sine wave according to the heart rate signal, compare an upper limit value of the arteriosclerosis index, a threshold value of the heart rate variability index and an upper limit value of a weight sine wave ratio with the ratio of the arteriosclerosis index, the heart rate variability index and the weight sine wave, and convert the result into a risk index according to the comparison result, wherein the risk index represents a risk probability of the subject suffering from arteriosclerosis-related diseases. The motion detection device 20 can further analyze and detect the pulse, blood pressure, and heart rate variability (such as TP, HF, LF) of the subject according to the heart rate signal, and provide the detected pulse, blood pressure, and heart rate variability to the subject, so that the subject can evaluate his/her health condition.
The cloud server 30 is wirelessly connected to the motion detection device 20 for receiving and storing the arteriosclerosis index, the pulse, the blood pressure, and the heart rate variation index of the user.
The cloud server 30 may also collect the latest diagnosis number of patients with arteriosclerosis-related diseases provided by the public trust health research institutions such as the health welfare department, and compare the current-year general population (multiple subjects) counted by the authoritative institutions such as the internal administration department, calculate the ratio of the multiple subjects without arteriosclerosis-related diseases, use the ratio of the multiple subjects without arteriosclerosis as the threshold of arteriosclerosis degree, obtain the upper limit of the arteriosclerosis index, the threshold of heart rate variability index and the upper limit of the weight chord wave ratio of the multiple subjects by linear regression calculation based on the collected data of the arteriosclerosis index, and use the range in which the arteriosclerosis index is higher than the upper limit as the high risk group index of arteriosclerosis-related diseases. In the multiple testees, the ratio of the male and female in each age group not suffering from the arteriosclerosis-related diseases can be further calculated, the ratio of the male and female in each age group not suffering from the arteriosclerosis-related diseases is used as an arteriosclerosis degree threshold, the upper limit value of the arteriosclerosis index, the threshold value of the heart rate variation index and the upper limit value of the weight chord wave ratio of the multiple testees in each age group and each sex are obtained by linear regression calculation through the collected data of the arteriosclerosis indexes, and the range of the arteriosclerosis indexes higher than the upper limit value is used as a high risk group index of suffering from the arteriosclerosis-related diseases. Wherein the formula of the linear regression is:
y-ax + b, wherein,
although exceeding the upper limit value is likely to belong to a high risk group of arteriosclerosis, the location of arteriosclerosis is still unknown. In contrast, heart rate variability related experiments and studies indicate 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 individuals, so that the reference value of whether heart rate variability is abnormal or not is determined by subtracting three standard deviations from the total spectral power (TP) in the recommended normal range using the recommended heart rate variability reference value (as shown in table 1), and a threshold that heart rate variability is lower than 99.85% of the public is obtained. In table 1, 3466-.
| Parameter(s) | Unit of | Normal range obtained by current experiment |
| TP | ms2 | 3466±1018*3 |
| LF | ms2 | 1170±416 |
| HF | ms2 | 975±203 |
TABLE 1 Normal range of frequency domain index of heart rate variability
The TP value and the SI value are used to establish a high risk group, a medium risk group, and a low risk group, for example: the people age 36 years, the SI value is 9.7 (the average value of all SI values of the testees) is greater than the confidence interval, and the TP value is also less than the lower limit value of the heart rate variation, and then the people are judged to be a high risk group; if the TP value falls within the normal range, it is determined as a medium risk group and the possibility of arteriosclerosis is notified, and the above-mentioned method is used to suggest whether the subject should perform an early inspection into the coronary artery, and the determination rule is shown in Table 2.
TABLE 2 hazard group comparison Table
According to the determination principles of Table 2, the present invention can be further applied to the PPG measurement and statistics of the weight-chord generation ratio for patients of all ages and all sexes with confirmed diagnosis of cardiovascular diseases, thereby determining the high risk ratio (e.g. 55%) as a reference for further determining whether the subject is a high risk group with arteriosclerosis-related diseases. Finally, the risk grade of the subject suffering from the arteriosclerosis-related diseases is determined according to the proportion of SI exceeding the upper limit, TP value and the proportion of occurrence of the heavy string waves of the subject within the measurement time (for example, 100 seconds), as shown in the following Table 3, wherein a higher risk index indicates a higher risk of the subject suffering from the arteriosclerosis-related diseases.
TABLE 3 reference table of arteriosclerosis
In application, the wearable detection device 10 is responsible for collecting the relevant personal physiological information in real time, storing the physiological information in the action detection device 20 of the subject, performing analysis and diagnosis through the latest big data analysis result of the cloud server, and providing the personal physiological information to the cloud server 30 frequently for big data analysis. Therefore, the cloud server 30 can not only collect big data through a large number of the testee's action detection devices 20, but also receive statistical data of health care units in each area at any time, and provide the testee with the latest and complete reference index at any time.
In practical applications, the action detection device 20 can receive personal physiological data from the wearable detection device 10, and perform calculation processing to establish physical condition detection of the person to be detected, cardiovascular disease risk, and long-term depression/irritability/overstrain analysis, and can update parameters of various judgment functions in real time according to the more complete data through the updated information of the cloud server 30 to generate more accurate detection and judgment.
With the arteriosclerosis index analyzing system described above, the arteriosclerosis index analyzing method of the present invention will be described below with reference to fig. 2 to 5.
The method of the invention comprises the following steps:
s11: dividing the heart rate signal HR into a plurality of continuous sub-heart rate signals HR1, and generating a pulse frequency signal HF1 for each sub-heart rate signal HR1 through Fourier transformation;
s12: calculating an S-value for each sub-heart rate signal HR1, the S-value representing:
wherein A isfirst-fThe intensity value of the frequency pulse signal HF1 at one time frequency, Asecond-fThe intensity value of the frequency pulse signal HF1 at twice frequency; if the S value is larger than or equal to a first threshold value, removing the sub-heart rate signal HR 1; if the S value is smaller than the first threshold value, executing the next step;
s13: comparing a time ratio with a second threshold; wherein a first time difference Δ T of the sub-heart rate signal HR1 is calculated1And a second time difference DeltaT2And comparing the first time difference DeltaT1Divided by the second time difference Δ T2Obtaining the time ratio, wherein the first time difference is Δ T1Representing the time difference between a main heart rate peak MP1 and a sub-heart rate peak SP1 of the sub-heart rate signal HR1, the second time difference Δ T2The time difference between the main peak MP1 and the main trough MH1 of the heart rate signal HR 1; if the time ratio is smaller than a second threshold value, executing the next step; if the time ratio is greater than or equal to a second threshold, go back to step S11; calculating the first time difference DeltaT1And the second time difference DeltaT2The method can generate a differential signal by differentiating the sub-heart rate signal once, find the position with a slope of 0 in the differential signal, i.e. the occurrence time of the main heart rate peak MP1, the sub-heart rate peak SP1 and the main heart rate valley MH1, and calculate the time from the main heart rate peak MP1 to the sub-heart rate peak SP1, i.e. the first time difference Δ T1Calculating the time from the heart rate secondary peak SP1 to the main wave trough MH1 as the second time difference Delta T2。
S14: calculating an average value of the arteriosclerosis indexes of the testee; if the time ratio is smaller than the second threshold, the height of the subject and the first time difference Δ T are determined1Calculating the arteriosclerosis index (SI), averaging the arteriosclerosis indexes to obtain an arteriosclerosis index average value after calculating a plurality of arteriosclerosis indexes, wherein the arteriosclerosis index is calculated by the following formula:
s15: calculating the proportion of the heavy sine waves; the number of sub-heart rate signals HR1 attributed to a heavy string wave is divided by the total number of sub-heart rate signals to obtain the proportion of the heavy string 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 action detection device 20, and the action detection device 20 divides the heart rate signal HR into a plurality of continuous sub-heart rate signals HR1, that is, the heart rate signal HR is formed by combining the plurality of sub-heart rate signals HR 1. Referring to fig. 3B, the motion detection apparatus 20 performs a fourier transform on each sub-rate signal HR1 to obtain the pulse frequency signal HF1, i.e., each sub-rate signal HR1 corresponds to a pulse frequency signal HF 1. In this step, the sub-heart rate signal HR1 belonging to the time domain is fourier-transformed to generate the frequency pulse signal HF1 belonging to the frequency domain. As can be seen from fig. 3B, the clock signal HF1 has two peaks (intensities), one peak being at the occurrence of one multiple of the frequency and the other peak being at the occurrence of two multiple of the frequency.
In step S12, the motion detection device 20 extracts the intensity values of the clock signal HF1 at one time and at two times, and calculates the ratio of the intensity values of the clock signal HF1 at one time and two times to obtain the S value. The motion detection device 20 then calculates the magnitude relationship between the S value and the first threshold, and when the S value is greater than or equal to the first threshold, the motion detection device 20 discards the sub-heart rate signal HR1 corresponding to the S value; when the S value is smaller than the first threshold, the motion detection device 20 retains the sub-heart rate signal HR1 corresponding to the S value and executes the next step. The reason why the S value greater than or equal to the first threshold is discarded is that the S value greater than or equal to the first threshold represents that the intensity value of the clock signal HF1 at twice the frequency is too different from the intensity value at twice the frequency, which indicates that the clock signal HF1 belongs to a heavy sine wave, and the heavy sine wave cannot be used for calculating the arteriosclerosis index (SI), so that the clock signal HF1 belonging to the heavy sine wave is discarded, which helps to improve the accuracy of calculating the arteriosclerosis index (SI).
The following description will take actual operations as an example. Referring to fig. 3A and 3B, it is shown in fig. 3A that the heart rate signal HR obtained by the wearable detection device 10 using PPG technology detects that the subject is a normal blood vessel function subject, and therefore it can be seen in fig. 3A that the heart rate signal is mostly composed of dicrotic waves. FIG. 3B is a frequency domain diagram of the pulse signal HF1 generated after the step S11 is performed on one of the sub-heart rate signals HR1, and the peak intensity (A) at one time of the frequency is seen to be 1.75Hzfirst-f) 2168; double frequency falls approximately at 3.375Hz, peak intensity of double frequency (A)second-f) 1590, the first threshold is preset to 3.5, the S value is 2168/1590 ═ 1.36, and 1.36<3.5, which means that the intensity value of one time frequency is not much different from the intensity value of two times, the pulse signal HF1 has two peaks with distinct intensity, which represents that the sub-heart rate signal HR1 may be a dicrotic wave, so that the heart rate signal HR has a certain reference value, and the next step can be carried on.
Referring to fig. 4A and 4B, it is also shown in fig. 4A that the heart rate signal HR obtained by the wearable detection device 10 using PPG technology is detected to be a subject with abnormal blood vessel function, so that fig. 4A shows that the heart rate signal HR is mostly composed of sub-heart rate signals HR2 belonging to heavy string waves, and fig. 4B is a frequency domain diagram of the frequency pulse signal HF2 generated after one of the sub-heart rate signals HR2 is executed in step S11. Can be seen in FIG. 4BTo a frequency of 1.25Hz, an intensity at the frequency of one (A)first-f) Is 3271; double frequency falls approximately at 2.5Hz, intensity of double frequency (A)second-f) 791.6, the first threshold is preset to be 3.5, the S value is 3271/791.6 ≧ 4.13 ≧ 3.5, which means that the difference between the intensity value of one-time frequency and the intensity value of two-time frequency is too large, the pulse signal HF2 has only a peak with a significant intensity, which represents that the sub-rate signal HR2 is a heavy string wave, so the sub-rate signal HR2 has no reference value, and the motion detection apparatus 20 discards the sub-rate signal HR 2.
The first threshold value is set to 3.5, which is a preferable value obtained from the results of multiple experiments, and on the premise that the first threshold value is preset to 3.5, the sub-heart rate signals HR1 and HR2 of the heavy string wave and the heavy beat wave can be more accurately screened out, so in practical applications, the first threshold value is preferably preset to 3.5.
Although in step S12, the sub-heart rate signals HR2 that are heavy sine waves are found and discarded, the sub-heart rate signals HR1 that are not discarded may also be relatively insignificant heavy sine waves, and therefore, the sub-heart rate signals HR1 that are not discarded must be extracted to execute step S13, so as to more precisely screen out the sub-heart rate signal HR1 that is really a heavy sine wave.
Referring to fig. 5, in step S13, the motion detection device 20 differentiates the sub-heart rate signal HR1 every time it remains after step S12, so that each sub-heart rate signal HR1 generates a differential signal. In terms of waveforms, the sub-heart rate signal HR1 has the main heart rate peak MP1, the sub-heart rate peak SP1 and the main heart rate valley MH 1. The movement detection device 20 calculates the first time difference Δ T from the main heart rate peak MP1 to the secondary heart rate peak SP11And the second time difference Δ T from the main peak MP1 to the main valley MH12. The action detection device 20 calculates the first time difference Δ T1And the second time difference DeltaT2The ratio of the first time to the second time is obtained, and the time ratio is compared with the second threshold, if the time ratio is smaller than the second threshold, the first time between the main heart rate peak MP1 and the minor heart rate peak SP1 in the sub-heart rate signal HR1 is representedDifference Δ T1The length is longer, which is equivalent to the sub-heart rate signal HR1 having a more obvious main peak MP1 and sub-peak SP1 of the heart rate, i.e. the sub-heart rate signal HR1 is determined to belong to a dicrotic wave, and the motion detection device 20 retains the sub-heart rate signal HR 1; conversely, if the time ratio is greater than or equal to the second threshold, it represents a first time difference Δ T between the main heart rate peak MP1 and the sub-heart rate peak SP1 in the sub-heart rate signal HR11The sub-heart rate signal HR1 is short and has only one distinct peak (cannot be distinguished from the peak being the main heart rate peak MP1 or the secondary heart rate peak SP1), that is, the sub-heart rate signal HR1 is determined to be a heavy sine wave, and the sub-heart rate signal HR1 is discarded by the motion detection device 20. After multiple experiments and calculations, it is found that when the second threshold is 80%, the dicrotic wave and the dicrotic wave are accurately distinguished, so that in practical application, the second threshold is preset to be 80%.
In step S14, the motion detection device 20 retrieves the first time difference Δ T of the retained sub-heart rate signal HR11And comparing the first time difference DeltaT1Substituting into the calculation formula of the arteriosclerosis index and averaging multiple arteriosclerosis indexes to obtain the average value of the arteriosclerosis index of the subject. For example, in step S11, the wear detection device 10 divides the heart rate signal HR into 10 sub-heart rate signals HR1, HR2, eliminates one sub-heart rate signal HR2 with the property of a heavy string wave in step S12, and eliminates another sub-heart rate signal HR2 with the property of a heavy string wave in step S13, so that eight sub-heart rate signals HR1 are retained. The motion detection device 20 extracts the respective first time differences Δ T of the eight sub-heart rate signals HR11And substituting the calculation formula of the arteriosclerosis indexes to calculate eight arteriosclerosis indexes. The action detection device 20 calculates the average value of the eight arteriosclerosis indexes, and then obtains the average value of the arteriosclerosis indexes of the testee.
In step S15, since the sub-heart rate signals HR1 belonging to the dicrotic wave and the sub-heart rate signals HR2 belonging to the heavy string wave are identified in steps S12 and S13, the behavior detection apparatus 20 further compares the number of the sub-heart rate signals HR2 belonging to the heavy string waves with the total number of all the sub-heart rate signals HR1, HR2 to obtain the proportion of the heavy string waves. For example, in step S11, the wearing detection device 10 divides the heart rate signal into 10 sub-heart rate signals HR1 and HR2, eliminates one sub-heart rate signal HR2 with the property of a heavy string wave in step S12, and eliminates another sub-heart rate signal HR2 with the property of a heavy string wave in step S13, so that two sub-heart rate signals HR2 in total belong to the heavy string wave, and the ratio of the heavy string wave is 20% (2/10 × 100% — 20%).
By utilizing the steps, the heart rate signal HR can be filtered, the wave band of the heavy string wave is identified and eliminated, the wave band of the heavy string wave is left, and the first time difference delta T of the heavy string wave is utilized1The average value of the arteriosclerosis indexes is calculated, so that the average value of the arteriosclerosis indexes is prevented from being calculated incorrectly due to the weight sine waves. In step S12, the sub-heart rate signals HR1 are converted from time domain to frequency domain, the S value of each sub-heart rate signal is calculated, the S value and the first threshold are evaluated, the S value with too high intensity is eliminated, the sub-heart rate signals HR1 are differentiated once to obtain a dicrotic wave, and the first time difference Δ T between the main heart rate peak MP1 and the sub-heart rate peak SP1 in the dicrotic wave is calculated1The second time difference Δ T from the main peak MP1 and the main valley MH1 of the heart rate2And finally, calculating a plurality of more accurate arteriosclerosis indexes by using the counterpulsation waves, and averaging after summing the arteriosclerosis indexes to obtain a correct arteriosclerosis index average value.
In addition to the arteriosclerosis risk assessment system, the arteriosclerosis index analysis system of the present invention can further perform another arteriosclerosis index measurement step of removing heavy sinusoidal waves, and after the arteriosclerosis risk assessment system is completed, the further steps are performed to assist in removing the wave bands of the heavy sinusoidal waves, so as to ensure that the reserved wave bands are the heavy pulsating waves, and the arteriosclerosis index (SI) and the arteriosclerosis index average value are more accurate.
Referring to fig. 6 to 14B, another method for measuring arteriosclerosis without heavy string waves according to the present invention comprises the following steps:
please refer to fig. 6 and fig. 7A, S21: establishing an auxiliary line SL of the sub-heart rate signal HR1, the auxiliary line SL extending from a main heart rate peak MP2 of the sub-heart rate signal HR1 to a main heart rate trough MH 2; wherein the heart rate main peak MP2 represents the amplitude maximum of the sub-heart rate signal HR1, and the heart rate main peak MH2 represents the amplitude minimum of the sub-heart rate signal HR 1;
s22: establishing an auxiliary line distance signal SLS and calculating the number of troughs of the auxiliary line distance signal SLS; referring to FIG. 7B, the motion detection device 20 differentiates the sub-heart rate signal HR1 once to obtain the differential signal HD1, and takes a first time t of the differential signal HD11And a second time point t2And calculating at the first time point t1And the second time point t2The distance of the auxiliary line SL from the sub-heart rate signal HR 1; wherein the first time point t1The first valley occurrence time of the differentiated signal HD1 along the positive direction of the time axis2The occurrence time of the heart rate trough MH 2. Referring to FIG. 7C, the auxiliary line distance signal SLS represents at the first time t1To the second time point t2All vertical distances between the auxiliary line SL and the sub-heart rate signal HR1 form a distance function.
Fig. 7A to 7C show the actual execution of the steps S21, S22 on the sub-rate signal HR1 belonging to the dicrotic wave, in fig. 7C it can be seen that the auxiliary line distance signal SLS has two distinct valleys H1, H2, representing that the sub-rate signal HR1 may belong to the dicrotic wave.
The following steps S21, S22 are performed using the sub-heart rate signal HR2 of the heavy string wave as a practical example. Referring to fig. 8A, in step S21, the auxiliary line SL of the sub-heart rate signal HR2 is first established, and extends from the main heart rate peak MP2 of the sub-heart rate signal HR2 to the main heart rate trough MH 2. Referring to FIG. 8B, in step S22, the heart rate sub-signal HR2 is differentiated once to obtain the differentiated signal HD2, and a first time t of the differentiated signal HD2 is taken1And a second time point t2And calculating at the first time point t1And the second time point t2The distance of the auxiliary line SL from the sub-heart rate signal HR 2. In fig. 8C it can be seen that the aid line distance signal SLS has only one distinct trough, representing that the sub-heart rate signal HR2 belongs to a heavy chord wave.
Although it can be identified in steps S21, S22 that the sub-heart rate signals HR1, HR2 belong to a dicrotic wave or a dicrotic wave, it is found when the waveforms of the sub-heart rate signals HR1, HR2 are actually measured that the sub-heart rate signals HR1, HR2 may be dicrotic waves or insignificant dicrotic waves (approximate dicrotic waves are actually the waveforms of the dicrotic waves). 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 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, and whether the inflection point H5 is a valley point affects the determination of what kind of waveform the sub-heart rate signal HR3 belongs to, so it is difficult to distinguish whether the sub-heart rate signal HR3 belongs to a dicrotic wave or a sinusoidal wave. Therefore, it is necessary to further refer to the calculation formula of the bezier curve and adopt different judgment and calculation methods for the dicrotic wave, the unobvious dicrotic wave and the dicrotic wave. In the following description, step S23 is a method of calculating a bezier curve, step S231 is a calculation flow for a dicrotic wave, and step S232 is a calculation flow for an insignificant dicrotic wave and a heavy string wave.
Please refer to fig. 10A to 10C, S23: calculating a bezier curve, wherein the calculation formula of the bezier curve is as follows:
B(t)＝P0(1-t)3+3P1(1-t)2t+3P2(1-t)t2+P3t3；
and take the first time point t1I.e. the first valley occurrence time (t) in the positive direction of the time axis by the auxiliary line distance signal SLS1) To obtain B (t)1) And the second time point t is taken2I.e. the second valley occurrence time (t) in the positive direction of the time axis from the auxiliary line distance signal SLS2) To obtain B (t)2)。
Wherein the content of the first and second substances,
0≤t≤1；
please refer to fig. 10B, P0The first valley point of the sub-heart rate signal HR1 along the positive direction of the time axis after one-time differentiation; please refer to FIG. 10A, P3At a third time point t3The third time point t3The time when the heart rate dominant trough occurs; p1Is a first control point, and the first valley occurrence time (t) of the auxiliary line distance signal SLS along the time axis1) Substituted into the formula for calculating the Bessel curve (i.e. B (t)1))；P2A second control point, a second valley occurrence time (t) in the positive direction of the time axis from the auxiliary line distance signal SLS2) Substituted into the formula for calculating the Bessel curve (i.e. B (t)2))。
S231: will P0、P3、B(t1)、B(t2) Substituting into the calculation formula of the Bessel curve and calculating for three times to obtain P1、P2I.e. according to P1、P2、P0、P3The bezier curve BC1 (shown in fig. 10A) of the sub-heart rate signal HR1 was obtained. This method is applicable to the sub-rate signal HR1 (shown in fig. 10C) where the auxiliary line distance signal SLS has two distinct valleys, representing that the sub-rate signal HR1 is a dicrotic wave.
Referring to fig. 11A to 11C, S232: will P0、P3、B(t1)、B(t2) Substituting into the calculation formula of the Bessel curve and calculating for three times to obtain P1、P2I.e. according to P1、P2、P0、P3The bezier curve BC2 (shown in fig. 11A) of the sub-heart rate signal HR2 is obtained. Wherein B (t)2) The calculation method is as follows:
this method is applicable to the sub-rate signal HR2 (shown in FIG. 11C) where the auxiliary line distance signal SLS has only one distinct trough, representing that the sub-rate signal HR2 is a heavy chord wave or an insignificant dicrotic wave.
S24: comparing the correlation between the Bessel curve and the heart rate signal, and if a correlation coefficient between the Bessel curve and the sub-heart rate signal HR1 is smaller than a third threshold, calculating the arteriosclerosis index (SI) of the sub-heart rate signal HR 1; 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%.
Wherein, the calculation mode of the correlation coefficient is as follows:
gamma is a correlation coefficient, n is the number of samples, and 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 practical applications, a solid line represents the heart rate signal HR1 actually measured by a certain subject, a dashed line represents the bezier curve BC1 derived from the heart rate signal HR1, the correlation coefficient between the heart rate signal HR1 and the bezier curve BC1 is 99.49%, and is smaller than the third threshold (preset to be 99.85%), which represents that the heart rate signal HR1 is not correlated with the bezier curve BC1, and in fig. 12A, an interval (a range between two dot chain lines) in which the amplitude of the heart rate signal HR1 is larger than that of the bezier curve BC1 is a range in which a dicrotic wave occurs. Referring to fig. 12B, the heart rate sub-peak SP1 of the dicrotic wave can be found out by performing a first differentiation on the interval, and the arteriosclerosis index (SI) can be further calculated by substituting the step S13. As can be seen from fig. 12A and 12B, the position of the heart rate sub-peak SP1 indeed falls within the interval in which the amplitude of the heart rate signal HR1 is greater than the amplitude of the bezier curve BC 1.
Referring to fig. 13A, in another practical application, similarly, a solid line represents the heart rate signal HR2 actually measured by a certain subject, a dashed line represents the bezier curve BC2 derived from the heart rate signal HR2, the correlation coefficient between the heart rate signal HR2 and the bezier curve BC2 is 99.96%, which is greater than the third threshold (preset to be 99.85%), which represents that the heart rate signal HR2 and the bezier curve BC2 are almost overlapped, which represents that the heart rate signal HR2 is a heavy chord wave, so that the heart rate signal HR2 with the correlation coefficient being too high with the bezier curve BC2 can be discarded. If the heart rate signal HR2 is differentiated once, an unknown point N is obtained as shown in fig. 13B, and the unknown point N does not fall in the overlapping section (the range between two one-point curves) of the heart rate signal HR2 and the bezier curve BC2, but falls at a position other than the bezier curve, so that whether the heart rate signals HR1 and HR2 are heavy string waves can be accurately determined by using the bezier curve.
The reason for calculating the bezier curve is that the bezier curve can be regarded as a virtual heavy sinusoidal curve of the sub-heart rate signals HR1 and HR2, and the calculation of the correlation coefficient can further determine the overlapping rate (correlation level) of the bezier curve and the sub-heart rate signals HR1 and HR 2. If the overlap rate of the sub-heart rate signal HR1 and the sub-heart rate signal HR1 is lower than the third threshold, which represents that the correlation between the sub-heart rate signal HR1 and the sub-heart rate signal HR1 is low, the sub-heart rate signal HR1 is not completely the same as the imaginary heavy-chord wave curve, and the sub-heart rate signal HR1 is the dicrotic 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 is greater than or equal to the third threshold value, which represents that the correlation between the bezier curve and the sub-heart rate signal HR2 is high, the sub-heart rate signal HR2 is almost identical to an imaginary heavy-chord curve, and the sub-heart rate signal HR2 is a heavy-chord wave.
Please refer to fig. 14A, S25: the amplitude of the first heart rate signal HR4 in the positive direction of the time axis is taken to be larger than the amplitude of the Bessel curve BC4, the first peak of the interval in the positive direction of the time axis is taken as the differential secondary peak of the sub-heart rate signal HR4 in the interval, and the time difference between the first heart rate secondary peak corresponding to the occurrence time of the differential secondary peak and the heart rate main peak is taken to calculate the arteriosclerosis index.
Referring to fig. 14A and 14B, the heart rate signal HR4 includes a main heart rate peak MP4 and the heart rate secondary wavePeak SP4 and a third heart rate peak TP 4. The heart rate signal HR4 is differentiated once to obtain the pulse frequency signal HD4, and the pulse frequency signal HD4 includes a main differentiated peak MP5, a sub differentiated peak SP5 and a third differentiated peak TP 5. Since the differential sub-peak SP5 is closer to the main differential peak MP5 than the third differential peak TP5, the sub-peak SP4 corresponding to the time of occurrence of the differential sub-peak SP5 is a real dicrotic wave, and the time difference between the sub-peak SP4 and the main peak MP4 is taken (the first time difference Δ T)1) The arteriosclerosis index was calculated.
The reason for performing step S25 is that some of the waveforms of the heart rate signal HR4 may oscillate due to external force during measurement, for example, the heart rate signal HR4 of fig. 14A, so that the bezier curve BC4 has two ranges of dicrotic waves (the amplitudes of the two heart rate signals HR4 are greater than the range of the amplitudes of the bezier curve BC 4), and the correlation coefficients of the heart rate signal HR4 and the bezier curve BC4 are 99.73%, less than 99.85%, and indeed are dicrotic waves, so the peak closest to the differential main peak MP5 must be taken, that is, the differential sub-peak SP5 is the dicrotic wave position, so as to avoid finding out an erroneous dicrotic peak due to waveform oscillation.
According to the invention, the heart rate signal of the testee is obtained through the wearable detection device 10, the upper limit value of the arteriosclerosis indexes of different age layers is calculated by using cloud data, the arteriosclerosis indexes, the heart rate variation characteristic value and the proportion of the heavy string waves of the testee can be calculated as long as the ages and the heights are input, and the upper limit value of the arteriosclerosis indexes, the heart rate variation index threshold value and the upper limit value of the proportion of the heavy string waves are compared to generate the risk index of the testee, so that the personal can easily evaluate the arteriosclerosis condition in daily life, and the personal can conveniently see a doctor in time before an emergency occurs. Furthermore, the steps of the method can remove the heavy sine waves, so that the error of calculation of the arteriosclerosis index caused by the heavy sine waves generated by the human body is avoided when the arteriosclerosis index is calculated, and the calculated arteriosclerosis index is more accurate.
Claims (9)
1. An arteriosclerosis risk assessment system, comprising:
a wearable detection device for detecting a heart rate signal of a subject;
the action detection device is wirelessly connected with the wearing detection device and is used for receiving the heart rate signal, calculating according to the heart rate signal to obtain an arteriosclerosis index, a heart rate variation index and a proportion of a heavy string wave, respectively comparing the arteriosclerosis index, the heart rate variation index and the proportion of the heavy string wave with an arteriosclerosis index upper limit value, a heart rate variation index threshold value and a heavy string wave proportion upper limit value, and converting into a danger index according to a comparison result;
the cloud server is wirelessly connected with the action detection device and used for receiving the arteriosclerosis indexes of each of the testees and calculating the upper limit value of the arteriosclerosis indexes.
2. The arteriosclerosis risk assessment system according to claim 1, wherein the action detection means performs the following steps to obtain the arteriosclerosis index:
dividing the heart rate signal into a plurality of continuous sub-heart rate signals, and generating a frequency pulse signal for each sub-heart rate signal through Fourier transformation;
calculating an S-value for each sub-heart rate signal, the S-value representing:
if the S value is smaller than the first threshold value, comparing a time ratio with a second threshold value; calculating a first time difference and a second time difference of the sub-heart rate signal, and dividing the first time difference by the second time difference to obtain the time ratio, wherein the first time difference represents a time difference between a main heart rate peak and a secondary heart rate peak of the sub-heart rate signal, and the second time difference represents a time difference between the main heart rate peak and a main heart rate trough of the sub-heart rate signal;
calculating the arteriosclerosis index according to the height of a tested person and the first time difference, wherein the arteriosclerosis index is calculated according to the following formula:
3. The arteriosclerosis risk assessment system according to claim 2, wherein the action detection means averages a plurality of arteriosclerosis indexes by summation to obtain the average value of arteriosclerosis indexes.
4. The arteriosclerosis risk assessment system according to claim 3, wherein the action detection device further performs a step of removing heavy string waves, comprising:
establishing an auxiliary line of the sub-heart rate signal, wherein the auxiliary line extends from a heart rate main peak of the sub-heart rate signal to a heart rate main trough in a straight line; wherein the heart rate dominant peak represents the amplitude maximum of the sub-heart rate signal, and the heart rate dominant trough represents the amplitude minimum of the sub-heart rate signal;
establishing an auxiliary line distance signal and calculating the number of wave troughs of the auxiliary line distance signal;
taking a first time point and a second time point of a differential signal, and calculating 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 a waveform obtained by once differentiating the sub-heart rate signal, the first time point is the first trough occurrence time of the differential signal along the positive direction of a time axis, the second time point is the main trough occurrence time of the heart rate, and the auxiliary line distance signal represents a distance function formed by all vertical distances from the auxiliary line to the differential signal in the interval from the first time point to the second time point; if the number of the wave troughs is 2, the sub-heart rate signal is represented as a dicrotic wave, and if the number of the wave troughs is 1, the sub-heart rate signal is represented as a heavy string wave.
5. The arteriosclerosis risk assessment system according to claim 4, wherein the action detection means further comprises the following steps when removing the heavy string wave:
calculating a bezier curve, wherein the calculation formula of the bezier curve is as follows:
B(t)＝P0(1-t)3+3P1(1-t)2t+3P2(1-t)t2+P3t3；
wherein, P0The first valley point of the sub-heart rate signal after first differentiation along the positive direction of a time axis; p3Is the amplitude at a third time point, the third time point being the time at which the heart rate dominant trough occurs; p1Is a first control point, and the first valley occurrence time (t) in the positive direction of the time axis is determined by the auxiliary line distance signal1) Substituting the calculation formula of the Bezier curve to obtain the Bezier curve; p2A second control point, a second valley occurrence time (t) in the positive direction of the time axis from the auxiliary line distance signal2) Substituting the calculation formula of the Bezier curve to obtain the Bezier curve;
comparing 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 is smaller than a third threshold value, calculating the arteriosclerosis index of the sub-heart rate signal; wherein, the calculation mode of the correlation coefficient is as follows:
6. The arteriosclerosis risk assessment system according to claim 5, wherein the behavior detection means calculates the Bezier curve by adding P if the auxiliary line distance signal has two valleys0、P3、B(t1)、B(t2) Substituting into the calculation formula of the Bezier curve and calculating for three times to obtain P1、P2Then according to P1、P2、P0、P3Obtaining the Bessel curve of the sub-heart rate signal.
7. The arteriosclerosis risk assessment system according to claim 5, wherein said behavior detection means calculates the Bezier curve by adding P if there is a trough in said auxiliary line distance signal0、P3、B(t1)、B(t2) Substituting into the calculation formula of the Bezier curve and calculating for three times to obtain P1、P2Then according to P1、P2、P0、P3Obtaining the Bessel curve of the sub-heart rate signal, wherein B (t)2) The calculation method is as follows:
8. the arteriosclerosis risk assessment system according to claim 6 or 7, wherein when the action detection means removes the heavy chord wave, further comprising the steps of:
if the auxiliary line distance signal has two troughs, taking an interval in which the amplitude of the first heart rate signal in the positive direction of a time axis is larger than the amplitude of the Bezier curve, performing primary differentiation on the sub-heart rate signal in the interval, taking the first peak in the positive direction of the time axis in the interval as the differential sub-peak, and 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 to calculate the arteriosclerosis index.
9. The arteriosclerosis risk assessment system according to claim 8, wherein the action detection device further performs the following steps:
calculating the proportion of the heavy sine waves; and dividing the number of the sub-heart rate signals with the attribute of the heavy string waves by the total number of all the sub-heart rate signals to obtain the proportion of the heavy string waves.
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| US20160198963A1 (en) * | 2010-11-30 | 2016-07-14 | Nellcor Puritan Bennett Ireland | Methods and systems for recalibrating a blood pressure monitor with memory |
| CN103886173A (en) * | 2012-12-19 | 2014-06-25 | 财团法人工业技术研究院 | Pulse wave and physical health risk assessment system and method |
| CN104138253A (en) * | 2013-05-11 | 2014-11-12 | 吴健康 | Noninvasive continuous arterial blood pressure measuring method and equipment |
| KR20160126232A (en) * | 2015-04-23 | 2016-11-02 | 경희대학교 산학협력단 | Method of assessing cardiovascular health and cardiorespiratory fitness and apparatus thereof |
| EP3117766A1 (en) * | 2015-07-16 | 2017-01-18 | Preventicus GmbH | Processing biological data |
| CN107920763A (en) * | 2015-07-16 | 2018-04-17 | 普瑞温迪克斯有限责任公司 | Handle biological data |
| CN107405087A (en) * | 2016-01-08 | 2017-11-28 | 心匠有限公司 | A kind of Wearable and its method for being used to assess the possibility of heart arrest generation |
| TWM542444U (en) * | 2016-11-08 | 2017-06-01 | 黃勝榮 | Wearable pulse contour analysis device with multiple diagnositic functions |
| CN109394187A (en) * | 2018-11-08 | 2019-03-01 | 重庆医科大学 | Wearable cardiovascular health based on monomer side signal detection monitors system |
| TWM578864U (en) * | 2019-02-13 | 2019-06-01 | 司博創意科技有限公司 | Health management system for wearable device |
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