CN118252481A - Blood pressure monitoring algorithm irrelevant to dicrotic notch - Google Patents
Blood pressure monitoring algorithm irrelevant to dicrotic notch Download PDFInfo
- Publication number
- CN118252481A CN118252481A CN202410603196.7A CN202410603196A CN118252481A CN 118252481 A CN118252481 A CN 118252481A CN 202410603196 A CN202410603196 A CN 202410603196A CN 118252481 A CN118252481 A CN 118252481A
- Authority
- CN
- China
- Prior art keywords
- blood pressure
- pressure
- diastolic
- ppg
- dicrotic notch
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000036772 blood pressure Effects 0.000 title claims abstract description 85
- 238000012544 monitoring process Methods 0.000 title claims abstract description 38
- 230000035487 diastolic blood pressure Effects 0.000 claims abstract description 43
- 230000035488 systolic blood pressure Effects 0.000 claims abstract description 29
- 230000035485 pulse pressure Effects 0.000 claims abstract description 19
- 238000000605 extraction Methods 0.000 claims abstract description 6
- 230000000747 cardiac effect Effects 0.000 claims description 24
- 230000003205 diastolic effect Effects 0.000 claims description 21
- 230000000877 morphologic effect Effects 0.000 claims description 6
- UXFQFBNBSPQBJW-UHFFFAOYSA-N 2-amino-2-methylpropane-1,3-diol Chemical compound OCC(N)(C)CO UXFQFBNBSPQBJW-UHFFFAOYSA-N 0.000 claims description 5
- 101150035093 AMPD gene Proteins 0.000 claims description 5
- 238000009530 blood pressure measurement Methods 0.000 claims description 5
- 239000012634 fragment Substances 0.000 claims description 5
- 238000001514 detection method Methods 0.000 claims description 3
- 230000004872 arterial blood pressure Effects 0.000 claims description 2
- 238000000034 method Methods 0.000 abstract description 25
- 238000013461 design Methods 0.000 abstract description 5
- 238000013135 deep learning Methods 0.000 abstract description 3
- 238000012217 deletion Methods 0.000 abstract description 3
- 230000037430 deletion Effects 0.000 abstract description 3
- 238000011156 evaluation Methods 0.000 abstract description 3
- 239000008280 blood Substances 0.000 description 18
- 210000004369 blood Anatomy 0.000 description 18
- 230000001965 increasing effect Effects 0.000 description 8
- 210000001765 aortic valve Anatomy 0.000 description 6
- 210000001367 artery Anatomy 0.000 description 6
- 206010020772 Hypertension Diseases 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 230000017531 blood circulation Effects 0.000 description 2
- 210000004204 blood vessel Anatomy 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- HVYWMOMLDIMFJA-DPAQBDIFSA-N cholesterol Chemical compound C1C=C2C[C@@H](O)CC[C@]2(C)[C@@H]2[C@@H]1[C@@H]1CC[C@H]([C@H](C)CCCC(C)C)[C@@]1(C)CC2 HVYWMOMLDIMFJA-DPAQBDIFSA-N 0.000 description 2
- 230000007423 decrease Effects 0.000 description 2
- 230000003247 decreasing effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000013186 photoplethysmography Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 210000000707 wrist Anatomy 0.000 description 2
- 206010019280 Heart failures Diseases 0.000 description 1
- 206010061481 Renal injury Diseases 0.000 description 1
- 208000006011 Stroke Diseases 0.000 description 1
- 210000001015 abdomen Anatomy 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 235000012000 cholesterol Nutrition 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000013136 deep learning model Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000011038 discontinuous diafiltration by volume reduction Methods 0.000 description 1
- 230000000004 hemodynamic effect Effects 0.000 description 1
- 230000001939 inductive effect Effects 0.000 description 1
- 208000037806 kidney injury Diseases 0.000 description 1
- 230000031700 light absorption Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 208000010125 myocardial infarction Diseases 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000002792 vascular Effects 0.000 description 1
Abstract
The invention provides a dicrotic notch-independent blood pressure monitoring algorithm, which is characterized by comprising the following steps of: diastolic blood pressure modeling; modeling pulse pressure; form-independent blood pressure feature extraction and a blood pressure monitoring algorithm; compared with the traditional PTT method or oscillometric method, the design method of the invention can continuously monitor blood pressure in the daytime and at night, and has high comfort level; compared with the latest deep learning method, the method can better adapt to the problem of dicrotic notch deletion; the estimated errors of the systolic pressure and the diastolic pressure are respectively 0.01+/-6.74 mmHg and 0.02+/-6.27 mmHg, the AAMI standard (the international standard of the evaluation electronic sphygmomanometer issued by the American medical instrument promotion society is less than or equal to 5+/-8 mmHg) is met, and the estimated errors of the diastolic pressure and the systolic pressure do not meet the AAMI standard after the relevant characteristics of the dicrotic notch are discarded by other methods.
Description
Technical Field
The invention belongs to the technical field of non-invasive arterial blood pressure monitoring, and particularly relates to a blood pressure monitoring algorithm irrelevant to dicrotic notch.
Background
Blood pressure is an important physiological indicator, and is related to various health complications such as stroke, heart attack, heart failure, kidney injury and the like; the world health organization reports that about one third of adults worldwide have hypertension; however, due to the concealment of hypertension, 80% of patients fail to get proper treatment, and thus a great deal of research is devoted to wearable-device-based blood pressure monitoring methods to achieve continuous, comfortable, and convenient blood pressure monitoring.
PPG sensors, as a commonly used sensor in wearable devices, can detect blood volume by illuminating skin/tissue and measuring light absorption; in recent years, many studies have been made to monitor blood pressure using a PPG sensor.
At present, most researches are conducted by constructing a deep learning model to model correlation between blood pressure and PPG data waveform and PPG data manual characteristics, and non-pressurized continuous blood pressure monitoring is achieved on wrist-worn equipment; but these models rely heavily on features related to dicrotic notch such as reflected wave transmission time, etc.
Dicrotic notch is critical and represents the moment when the aortic valve closes, marking the end of systole in a cardiac cycle; however, analysis of a large number of PPG data shows that dicrotic notch loss occurs in a significant proportion of individual PPG data due to factors exceeding 20 individual factors (e.g., cholesterol content, vascular stiffness, etc.), and data noise.
There are some studies in the prior art that do not rely on dicrotic notch, i.e. measuring blood pressure by means of pulse wave transit time, but they require additional sensors and active coordination by the user, which does not allow continuous, convenient, and non-inductive blood pressure monitoring.
Disclosure of Invention
In order to solve the technical problems, the invention provides a blood pressure monitoring algorithm irrelevant to dicrotic notch, which solves the problem that the dicrotic notch is missing in PPG data acquired by wrist, finger palm and fingertip through scientific architecture design.
A dicrotic notch-independent blood pressure monitoring algorithm comprising: diastolic blood pressure modeling; modeling pulse pressure; form-independent blood pressure feature extraction and a blood pressure monitoring algorithm;
step one, diastolic pressure modeling;
Frank-staring law states that cardiac output increases with increasing diastolic pressure; this means that the direct current component of the PPG, which reflects the end-diastolic arterial blood volume, also increases with increasing diastolic pressure; furthermore, by analyzing the continuous blood pressure data and the PPG data acquired simultaneously, we have found that the diastolic pressure (DBP) has a correlation with the systolic pressure (SBP).
Thus, the diastolic pressure is modeled from the PPG dc component; according to this theory, a linear equation 1 can be established to express the relationship between the diastolic pressure and the PPG dc component and systolic pressure:
Wherein: 、 Personalized blood pressure coefficients for DC and SBP, Personalized blood pressure deviation coefficient for DBP;
Step two, modeling pulse pressure;
The greater the pressure difference between the diastolic and systolic pressures, the greater the pulse pressure, the more the blood volume in the artery increases during systole, the longer the time required to recover to the previous blood volume, i.e., the longer the diastolic time; meanwhile, the blood flow velocity is related to the blood pressure, that is, the higher the systolic pressure is, the faster the increased blood volume in the artery is restored to the previous blood volume; according to this theory, a linear equation 2 is established to express the relationship between pulse pressure and diastolic time T and systolic pressure:
wherein: 、 personalized blood pressure coefficients for T and SBP, The personalized blood pressure deviation coefficient is PP;
Step three, extracting morphological irrelevant blood pressure characteristics;
① Extracting a PPG direct current component;
Firstly, dividing a PPG signal into each cardiac cycle based on peak points detected by a peak detection (AMPD) algorithm;
As an example, the AMPD may accommodate different heart rates without requiring specific threshold settings for different individuals.
Secondly, regarding the minimum value point of the PPG signal as the direct current component of the current PPG segment in each cardiac cycle;
② Diastolic time;
If no dicrotic notch is present, it is difficult to determine the time at which the aortic valve closes based on the PPG morphology, which is necessary to calculate the diastolic time; according to frank-stirling law, under normal conditions, the systolic ejection volume is equal to the diastolic filling volume, and if not, the heart is also rapidly adjusted to restore equilibrium; this inspires that we estimate the time at which the aortic valve closes by analyzing the area under the PPG alternating component, i.e. the volume increased during systole should be equal to the volume decreased during diastole;
Since the starting and ending position of the PPG fragment in one cardiac cycle may fluctuate, these fluctuations do not guarantee that the cardiac output in the current cardiac cycle is equal to the cardiac input; to accurately determine the diastolic time, the following steps are taken:
i) Determining a PPG fragment with a stable baseline over a cardiac cycle; to do this, it must be ensured that the difference between the start and end positions of the PPG segments is less than a certain threshold (e.g. 10%) of the PPG amplitude;
ii) find one to find as shown in FIG. 1 Dividing the PPG alternating current component into two parts with equal areas as representative vertical lines;
iii) With the end time of the current cardiac cycle Subtracting the vertical lineIs the position of (i.e.)Calculating the diastolic time；
Step four, a blood pressure monitoring algorithm;
since both equation 1 and equation 2 relate to the unknown variable SBP, according to And (2) to obtain a formula 3 by converting the formula 1 and the formula 2:
further, simplified bit equation 4 of equation 3 is as follows:
since there are differences in the elasticity and diameter of blood vessels from individual to individual, it is necessary to calibrate the individual coefficients of each individual;
by performing blood pressure measurement at least three times, the measurement values of diastolic blood pressure and pulse pressure can be obtained simultaneously, thereby establishing six formulas; these formulas can be used to fit six individualized blood pressure coefficients: 、、、、 And ; Once the six personalized blood pressure coefficients are determined, the diastolic and systolic pressures can be estimated using equation 4;
as an illustration, the systolic pressure may also be estimated by summing the diastolic pressure and the pulse pressure.
The invention has the beneficial effects that:
① Compared with the traditional PTT (pulse wave transmission time) method or oscillometric method, the design method of the invention can continuously monitor blood pressure in the daytime and at night and has high comfort level;
② Compared with the latest deep learning method, the method can better adapt to the problem of dicrotic notch deletion; the experimental results of 85 persons show that the estimated errors of the method for the systolic pressure and the diastolic pressure are respectively 0.01+/-6.74 mmHg and 0.02+/-6.27 mmHg, the AAMI standard (the international standard for evaluating the electronic sphygmomanometer issued by the American medical instrument promotion society is less than or equal to 5+/-8 mmHg) is met, and the estimated errors of the systolic pressure and the diastolic pressure after the relevant characteristics of the dicrotic notch are discarded by other methods do not meet the AAMI standard.
③ Furthermore, compared to the prior methods where the test was performed on no more than 35 subjects, we evaluated on 85 subjects and our method performed best in continuous blood pressure monitoring accuracy.
Drawings
FIG. 1 is a schematic diagram of the diastolic time extraction of a dicrotic notch-independent blood pressure monitoring algorithm according to the present invention.
FIG. 2 is a flowchart of a dicrotic notch-independent blood pressure monitoring algorithm according to the present invention.
FIG. 3 is a graph and correlation of an overall consistency evaluation of estimated systolic and diastolic pressures for a dicrotic notch-independent blood pressure monitoring algorithm of the present invention.
Detailed Description
Referring now to fig. 1-3, a dicrotic notch-independent blood pressure monitoring algorithm is shown, comprising: diastolic blood pressure modeling; modeling pulse pressure; form-independent blood pressure feature extraction and a blood pressure monitoring algorithm;
step one, diastolic pressure modeling;
Frank-staring law states that cardiac output increases with increasing diastolic pressure; this means that the direct current component of the PPG, which reflects the end-diastolic arterial blood volume, also increases with increasing diastolic pressure; furthermore, by analyzing the continuous blood pressure data and the PPG data acquired simultaneously, we have found that diastolic and systolic pressures have a correlation.
Thus, the diastolic pressure is modeled from the PPG dc component; according to this theory, a linear equation 1 can be established to express the relationship between the diastolic pressure and the PPG dc component and systolic pressure:
Wherein: 、 Personalized blood pressure coefficients for DC and SBP, Personalized blood pressure deviation coefficient for DBP;
Step two, modeling pulse pressure;
The greater the pressure difference between the diastolic and systolic pressures, the greater the pulse pressure, the more the blood volume in the artery increases during systole, the longer the time required to recover to the previous blood volume, i.e., the longer the diastolic time; meanwhile, the blood flow velocity is related to the blood pressure, that is, the higher the systolic pressure is, the faster the increased blood volume in the artery is restored to the previous blood volume; according to this theory, a linear equation 2 is established to express the relationship between pulse pressure and diastolic time T and systolic pressure:
wherein: 、 personalized blood pressure coefficients for T and SBP, The personalized blood pressure deviation coefficient is PP;
Step three, extracting morphological irrelevant blood pressure characteristics;
① Extracting a PPG direct current component;
Firstly, dividing a PPG signal into each cardiac cycle based on peak points detected by a peak detection (AMPD) algorithm;
As an example, the AMPD may accommodate different heart rates without requiring specific threshold settings for different individuals.
Secondly, regarding the minimum value point of the PPG signal as the direct current component of the current PPG segment in each cardiac cycle;
② Diastolic time;
If no dicrotic notch is present, it is difficult to determine the time at which the aortic valve closes based on the PPG morphology, which is necessary to calculate the diastolic time; according to frank-stirling law, under normal conditions, the systolic ejection volume is equal to the diastolic filling volume, and if not, the heart is also rapidly adjusted to restore equilibrium; this inspires that we estimate the time at which the aortic valve closes by analyzing the area under the PPG alternating component, i.e. the volume increased during systole should be equal to the volume decreased during diastole;
Since the starting and ending position of the PPG fragment in one cardiac cycle may fluctuate, these fluctuations do not guarantee that the cardiac output in the current cardiac cycle is equal to the cardiac input; to accurately determine the diastolic time, the following steps are taken:
i) Determining a PPG fragment with a stable baseline over a cardiac cycle; to do this, it must be ensured that the difference between the start and end positions of the PPG segments is less than a certain threshold (e.g. 10%) of the PPG amplitude;
ii) find one to find as shown in FIG. 1 Dividing the PPG alternating current component into two parts with equal areas as representative vertical lines;
iii) With the end time of the current cardiac cycle Subtracting the vertical lineIs the position of (i.e.)Calculating the diastolic time；
Step four, a blood pressure monitoring algorithm;
since both equation 1 and equation 2 relate to the unknown variable SBP, according to And (2) to obtain a formula 3 by converting the formula 1 and the formula 2:
further, simplified bit equation 4 of equation 3 is as follows:
since there are differences in the elasticity and diameter of blood vessels from individual to individual, it is necessary to calibrate the individual coefficients of each individual;
by performing blood pressure measurement at least three times, the measurement values of diastolic blood pressure and pulse pressure can be obtained simultaneously, thereby establishing six formulas; these formulas can be used to fit six individualized blood pressure coefficients: 、、、、 And ; Once the six personalized blood pressure coefficients are determined, the diastolic and systolic pressures can be estimated using equation 4;
as an illustration, the systolic pressure may also be estimated by summing the diastolic pressure and the pulse pressure.
For a better description of the design principle of the present invention, the operation steps of the present invention when applied will now be briefly described with reference to fig. 2 of the accompanying specification:
Example 1:
FIG. 2 is a flowchart of a blood pressure monitoring algorithm according to the present application;
Firstly, a user can require to perform an initialization operation of personalized blood pressure coefficients after wearing a ring, namely 3 PPG data acquisition and blood pressure measurement;
specifically, the user is required to level the finger wearing the smart ring with the heart for 30 seconds and simultaneously measure the blood pressure value for that period; this step will be repeated 3 times to solve the model of the 4 scaling coefficients and 2 deviation coefficients required for the blood pressure monitoring algorithm.
Secondly, after the initialization operation is finished, the blood pressure monitoring method provided by the application can realize continuous monitoring of blood pressure; specifically, the PPG sensor of the smart ring will continuously collect PPG data at the abdomen of the finger and input it into the morphological independent blood pressure feature extraction module to extract blood pressure related features, i.e. PPG dc component and diastolic time.
Finally, we input the above features into a blood pressure monitoring algorithm to estimate the diastolic and systolic pressures at the current time of the user.
Example 2:
Compared with the latest deep learning method, the method can better adapt to the problem of dicrotic notch deletion; the experimental results of 85 persons show that the estimated errors of the method for the systolic pressure and the diastolic pressure are respectively 0.01+/-6.74 mmHg and 0.02+/-6.27 mmHg, the AAMI standard (the international standard for evaluating the electronic sphygmomanometer issued by the American medical instrument promotion society is less than or equal to 5+/-8 mmHg) is met, and the estimated errors of the systolic pressure and the diastolic pressure after the relevant characteristics of the dicrotic notch are discarded by other methods do not meet the AAMI standard; furthermore, compared to the prior methods where the test was performed on no more than 35 subjects, we evaluated on 85 subjects and our method performed best in continuous blood pressure monitoring accuracy.
The detailed comparison is shown below by the specific statistics of table 1:
table 1: comparison of measured blood pressure properties for different products
| Contrast product | Seismo watch | Cell phone shell for measuring blood pressure | EBP blood pressure measuring earphone | Crisp-BP blood pressure measuring wrist strap | The application is that |
| Number of people tested | 13 | 30 | 35 | 35 | 85 |
| Usage scenarios | Daytime, the method comprises the steps of | Single shot | Daytime, the method comprises the steps of | Daytime and night time | Daytime and night time |
| Comfort level | In (a) | In (a) | Low and low | High height | High height |
| Versatility of | High height | High height | High height | Low and low | High height |
| Systolic pressure ME/STD | 4.8/- | 3.3/8.8 | 1.8/7.2 | 1.67/7.31 | 0.01/6.74 |
| Diastolic blood pressure ME/STD | 2.9/- | 5.6/7.7 | 3.1/7.9 | 0.86/6.55 | 0.02/6.27 |
Wherein: ME is the average error, see formula 5, STD is the standard deviation, see formula 6; For the algorithm to estimate the blood pressure, In order to actually measure the blood pressure,Is the number of samples.
From table 1 it can be seen that the present application performs best in terms of 5 usage scenarios (continuity), comfort and versatility, systolic and diastolic blood pressure estimation errors, compared to other prior art conventional methods, tested on multiple groups of subjects.
Example 3:
referring to the description and FIG. 3, in the consistency evaluation graph, more than 95% of the data points are within the consistency range of systolic and diastolic pressures (M.+ -. 1.96 STD);
As also shown in FIG. 3, ME (SBP: 0.01, DBP: 0.02) and STD (SBP: 6.74, DBP: 6.27) satisfy the AAMI-defined error boundaries, i.e., M.ltoreq.5 and STD.ltoreq.8.
This shows that the application has high accuracy in practical use.
Further, the correlation diagram shown in FIG. 3 again demonstrates the significant correlation between the estimated ABP and the reference ABP, calculated using equation 7, systolic blood pressureHas a value of 0.86 and diastolic blood pressureA value of 0.81;
；
For better illustrating the architecture scheme of the present invention, the following concepts are briefly described by the related principle knowledge introduction:
The invention designs a morphological irrelevant blood pressure monitoring algorithm based on PPG data, and according to Frank-Starlin law, the law considers that the change of cardiac output can be used for estimating blood pressure; i.e. during systole, the blood volume in the artery increases, resulting in an increase in arterial wall pressure, i.e. systolic pressure; as the aortic valve closes, blood volume gradually decreases during diastole, causing the arterial wall pressure to return to diastolic pressure.
According to frank-starlin's law:
(i) The increase/decrease in final remaining blood volume in the artery is related to diastolic pressure; such variations may be captured by a morphology-independent PPG Direct Current (DC) component in the PPG data;
(ii) The rate of blood volume reduction during diastole is related to pulse pressure (pp=sbp-DBP), which can be captured by the alternating current (ALTERNATING CURRENT, AC) component of PPG; thus, blood pressure can be estimated from the characteristics related to the change in blood volume without relying on the presence of a dicrotic notch.
The invention uses the heart dynamics law (Frank-staring law, frank-STARLING LAW) to cope with the problem of the deficiency of important morphological characteristics "dicrotic notch" of blood pressure in photoplethysmography (Photoplethysmography, PPG) data so as to realize continuous, comfortable and universal blood pressure monitoring; compared with the prior art, the invention focuses on utilizing hemodynamics to explore a blood pressure monitoring algorithm irrelevant to rebroadcast notch, and realizes more universal continuous and comfortable blood pressure monitoring.
The foregoing description of the preferred embodiments of the present invention has been presented only to facilitate the understanding of the principles of the invention and its core concepts, and is not intended to limit the scope of the invention in any way, however, any modifications, equivalents, etc. which fall within the spirit and principles of the invention should be construed as being included in the scope of the invention.
Claims (5)
1. A dicrotic notch-independent blood pressure monitoring algorithm, comprising: diastolic blood pressure modeling; modeling pulse pressure; form-independent blood pressure feature extraction and a blood pressure monitoring algorithm;
step one, diastolic pressure modeling;
Modeling the diastolic pressure according to the PPG direct current component; a linear equation 1 is established to express the relationship between diastolic pressure and PPG dc component and systolic pressure:
，
Wherein: 、 Personalized blood pressure coefficients for DC and SBP, Personalized blood pressure deviation coefficient for DBP;
Step two, modeling pulse pressure;
a linear equation 2 is established to express the relationship between pulse pressure and diastolic time T and systolic pressure:
，
wherein: 、 personalized blood pressure coefficients for T and SBP, The personalized blood pressure deviation coefficient is PP;
Step three, extracting morphological irrelevant blood pressure characteristics;
① Extracting a PPG direct current component;
Firstly, dividing a PPG signal into each cardiac cycle based on peak points detected by a peak detection algorithm;
secondly, regarding the minimum value point of the PPG signal as the direct current component of the current PPG segment in each cardiac cycle;
② Accurately determining the diastolic time;
Step four, a blood pressure monitoring algorithm;
since both equation 1 and equation 2 relate to the unknown variable SBP, according to And (2) to obtain a formula 3 by converting the formula 1 and the formula 2:
，
simplified bit equation 4 of equation 3 is as follows:
，
Calibrating the individualization coefficient of each individual; by performing at least three blood pressure measurements, the diastolic blood pressure and the pulse pressure measurements can be obtained simultaneously, thereby establishing six formulas for fitting six personalized blood pressure coefficients: 、、、、 And ; After six personalized blood pressure coefficients are determined, the diastolic and arterial pressures are estimated using equation 4.
2. The dicrotic notch-independent blood pressure monitoring algorithm according to claim 1, wherein said accurate determination of diastolic time takes the steps of:
i) Determining a PPG fragment with a stable baseline over a cardiac cycle; to do this, it must be ensured that the difference between the start and end positions of the PPG segments is less than a certain threshold of PPG amplitude;
ii) find a strip Dividing the PPG alternating current component into two parts with equal areas as representative vertical lines;
iii) With the end time of the current cardiac cycle Subtracting the vertical lineIs the position of (i.e.)Calculating the diastolic time。
3. A dicrotic notch-independent blood pressure monitoring algorithm according to claim 2, wherein the PPG amplitude has a certain threshold value of 10%.
4. A dicrotic notch-independent blood pressure monitoring algorithm according to claim 1, wherein the AMPD is adaptable to different heart rates without specific threshold settings for different individuals.
5. A dicrotic notch-independent blood pressure monitoring algorithm according to claim 1, wherein the systolic pressure is also estimated by summing the diastolic pressure and the pulse pressure.
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN118252481A true CN118252481A (en) | 2024-06-28 |
Family
ID=
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106974631B (en) | Blood pressure measuring method and device based on pulse wave waveform and electrocardiosignal | |
| Yoon et al. | Cuff-less blood pressure estimation using pulse waveform analysis and pulse arrival time | |
| Esmaili et al. | Nonlinear cuffless blood pressure estimation of healthy subjects using pulse transit time and arrival time | |
| CN106821356B (en) | Cloud continuous BP measurement method and system based on Elman neural network | |
| CN108185996B (en) | Arterial blood vessel age estimation model construction method and device | |
| JP5984088B2 (en) | Noninvasive continuous blood pressure monitoring method and apparatus | |
| Ye et al. | Estimation of systolic and diastolic pressure using the pulse transit time | |
| Kurylyak et al. | A Neural Network-based method for continuous blood pressure estimation from a PPG signal | |
| KR101210828B1 (en) | Apparatus and method improving accuracy of wrist blood pressure by using multiple bio-signal | |
| KR20210005644A (en) | Method for estimating blood pressure and arterial stiffness based on light volumetric variability recording (PPG) signal | |
| Jadooei et al. | Adaptive algorithm for continuous monitoring of blood pressure using a pulse transit time | |
| CN102499669B (en) | Heart parameter measuring method and device | |
| CN109512412B (en) | Central artery blood pressure measuring device | |
| US10694959B2 (en) | Image based blood pressure monitoring method | |
| WO2017092020A1 (en) | Blood pressure measurement method and apparatus | |
| CN114145724A (en) | Method for dynamically monitoring blood pressure based on ECG (electrocardiogram) and PPG (photoplethysmography) multiple physiological characteristic parameters | |
| US20200178820A1 (en) | Method and arrangement for continuously estimating blood pressure | |
| Kumar et al. | Cuffless BP measurement using a correlation study of pulse transient time and heart rate | |
| CN108926334A (en) | Blood pressure acquisition methods and its system and device based on pulse wave | |
| CA3122115C (en) | Method of predicting fluid responsiveness in patients | |
| CN109512410A (en) | A kind of more physiological signal Fusion Features without cuff continuous BP measurement method | |
| CN111839488A (en) | Non-invasive continuous blood pressure measuring device and method based on pulse wave | |
| CN103767694B (en) | Method for accurately extracting cuff pressure shockwave | |
| GB2552455A (en) | Blood monitoring | |
| Liu et al. | Assessment of stroke volume from brachial blood pressure using arterial characteristics |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication |