CN109512410A - A kind of more physiological signal Fusion Features without cuff continuous BP measurement method - Google Patents
A kind of more physiological signal Fusion Features without cuff continuous BP measurement method Download PDFInfo
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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
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- A61B5/02108—Measuring pressure in heart or blood vessels from analysis of pulse wave characteristics
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Abstract
The invention discloses a kind of more physiological signal Fusion Features without cuff continuous BP measurement method, comprising: 1. are gone out the correlation models between arterial pressure and human body photoplethaysmography pulse signal (PPG) and ECG signal (ECG) characteristic quantity by Moens-Korteweg pulse transit model and arterial baroreceptor reflex (ABR) model inference；2., by comparing calibration experiment with traditional cuff type sphygmomanometer, determining the individual difference alienation parameter in the model according to the model；3. using the peg model, arterial pressure estimated value can be obtained by measuring PPG and ECG.By the above-mentioned means, the present invention can obtain the continuous blood pressure value of patient without traditional cuff type sphygmomanometer.
Description
Technical field
The invention belongs to the field of medical instrument technology, in particular to a kind of more physiological signal Fusion Features it is continuous without cuff
Blood pressure measuring method.
Background technique
Continuous BP measurement is of great significance to the clinical diagnosis and treatment of measuring of human health and cardiovascular disease,
It provides a kind of long-term cardiovascular system of human body dynamic assessment index.Traditional blood pressure measuring method such as Korotkoff's Sound is auscultated
Using inflation/deflation cuff extruding blood vessel, the equilibrium valve for passing through measurement cuff pressure and arterial pressure carries out blood pressure survey for method and oscillographic method
Amount.But due to the time interval that cuff inflation/deflation needs, this measurement method necessarily will cause the intermittence of blood pressure observation.And
Squeezing blood vessel for a long time will cause the physiology discomfort of patient and influences measurement accuracy.
Summary of the invention
Goal of the invention: in view of the foregoing drawbacks, the present invention provides a kind of inflation/deflation cuff without traditional blood and can
Accurately continuously measurement human body artery pressure value more physiological signal Fusion Features without cuff continuous BP measurement method.
Technical solution: the present invention propose a kind of more physiological signal Fusion Features without cuff continuous BP measurement method, packet
Include following steps:
(1) by Moens-Korteweg, that is, M-K pulse transit model and arterial baroreceptor reflex ABR model foundation
Weighted Fusion between arterial pressure P and photoplethaysmography pulse signal PPG and core signal ECG characteristic quantity returns mould
Type；
(2) according to the Weighted Fusion regression model, by comparing calibration experiment with traditional cuff type sphygmomanometer,
With the individual difference parameter in interative least square method calibration Weighted Fusion regression model；
(3) special by measurement PPG and ECG with the Weighted Fusion regression model using obtained individual difference parameter
Sign amount obtains the estimated value of individual arterial pressure.
Further, the Correlation model of arterial pressure P Yu PPG and ECG characteristic quantity are established in the step (1), are had
Steps are as follows for body:
Wherein PTT and HR is physiological signal characteristic quantity relevant to blood pressure, and PTT is pulse wave translation time, and HR is heart rate；
K1, K2, HRh, HRl, Pn, ε is model individual difference parameter to be calibrated；K1, K2For model regression parameter；HRh, HRlIt is ABR tune
Highest heart rate and HR min when section section；PnThe corresponding pressure value of section heart rate median is adjusted for ABR, ε is ABR adjusting
The slope in section.
Further, specific step is as follows for peg model individual difference parameter in the step (2):
(2.1) nominal data acquisition experiment: it is divided into dynamic experiment and static experiment；
(2.2) signal characteristic abstraction: feature extraction is carried out to collected signal, including the pulse under each heart rate beat
Conduction time PTT, heart rate HR, systolic pressure SBP and diastolic pressure DBP；
(2.3) physiological data according to dynamic experiment acquisition calculates oblique in the linear regulation section in ABR regulation-control model
Rate ε, HRh, HRlAnd Pn；Then the regression parameter K that SBP and DBP are corresponded in model is determined with iterative least square algorithm1s,
K2sAnd K1d,K2d。
Further, specific step is as follows for nominal data acquisition experiment in the step (2.1):
(2.1.1) static experiment: all experimenters sit quietly 10 minutes first and adapt to environment, then use under the state of sitting quietly
Cuff sphygmomanometer, finger tip photoelectric sensor, electrocardio electrode plate synchronous acquisition blood pressure, pulse and electrocardiosignal 10 minutes；
(2.1.2) dynamic experiment: all experimenters carry out 5 minutes running trainings on a treadmill with the speed of 8km/h；
Then the synchronous acquisition blood pressure again under the state of sitting quietly at once, pulse and electrocardiosignal 10 minutes.
Further, specific step is as follows for the calculating of model subjects difference parameter in the step (2.3):
Slope ε, HR in linear regulation section in (2.3.1) ABR regulation-control modelh, HRlAnd Pn: ε be defined as blood pressure with
Related slope when heart rate changes in the same direction, specific calculation are as follows: the first-order difference sequence of coring rate HR and systolic pressure SBP respectively
DHR [i]=HR [i+1]-HR [i] and dSBP [i]=SBP [i+1]-SBP [i] is arranged, establishes coordinate using dHR and dSBP as coordinate
System, takes the linearly dependent coefficient of a three quadrant data point as ε；Maximum heart rate HR is found in a three quadrant data point simultaneouslyh
With HR min HRl, and the corresponding blood pressure P of heart rate mediann；
Regression parameter K in (2.3.2) model1And K2: to K1And K2Calibration use iterative least square algorithm:
In initial phase, Definition Model waits for training parameter matrixMode input data matrixWith output data matrix
Then using 60s as the period, the mean μ of PTT data point in calculating cyclePTTAnd variances sigmaPTT, rejected according to 3 σ criterion
Abnormal data was both rejected | PTT- μPtT|>3σPTTPTT data point and corresponding blood-pressure measurement data point；
The Least-squares minimization target of model parameter θ are as follows:Its globally optimal solution is
Define single input dataμN+1=[PTT-1(iN+1) 1], in iteration minimum
Single iteration least square solution in two multiplication algorithms are as follows:
Whereinλ is that forgetting factor coefficient is set as
0.95；All data inputs, which are iterated, is calculated last regression parameter matrix θ.
The present invention by adopting the above technical scheme, has the advantages that
1. the present invention gets rid of the constraint of inflation cuff, uninterrupted continuous BP measurement can be truly realized.
2. Weighted Fusion regression model proposed by the present invention combines physics relevant to blood pressure and Physiological effect model, tool
There are better applicability and precision.
3. in model calibration, the iterative least square algorithm that the present invention uses, it is possible to prevente effectively from cumbersome is calibrated
Journey, calibration only need to acquire low volume data and can complete every time.
4. signal acquisition and processing unit of the invention can be completed by miniature computing chip, can easily be made into
Wearable device and progress Function Extension.
Detailed description of the invention
Fig. 1 is flow chart of the invention；
Fig. 2 is the arterial pressure reflection model schematic diagram that the present invention uses in specific embodiment；
Fig. 3 is the characteristic quantity schematic diagram that the present invention uses in specific embodiment；
Fig. 4 is the flow chart of nominal data collection process in specific embodiment.
Specific embodiment
Combined with specific embodiments below, the present invention is furture elucidated, it should be understood that these embodiments are merely to illustrate the present invention
Rather than limit the scope of the invention, after the present invention has been read, those skilled in the art are to various equivalences of the invention
The modification of form falls within the application range as defined in the appended claims.
As shown in Figure 1: a kind of more physiological signal Fusion Features without cuff continuous BP measurement method, including following step
It is rapid:
Step 1: for different measurands, as Fig. 4 measures the object each 10 minutes after sitting quietly and moving respectively
Blood pressure, photoplethaysmography pulse signal (PPG) and electrocardiosignal (ECG).It is passed as Fig. 3 calculates the pulse wave between eartbeat interval
Lead time PTT (is defined as: from R wave of electrocardiosignal vertex to the time of next pulse signal ascent stage gradient maxima point
Every) and heart rate HR (is defined as: the inverse of R wave of electrocardiosignal interval time RR).
Step 2: using peg model described in (1.3), the dynamic experimental data peg model measured using step 1
Middle ABR adjusts the parameter of model, comprising: ABR linear adjustment Interval Slope ε, highest heart rate HRh, HR min HRlAnd in heart rate
The corresponding blood pressure P of place valuen.Then pass through iteration weighted least square algorithm mark using static experiment data and dynamic experimental data
Determine Model Parameter K1And K2；
In the step 2 after the static state and dynamic synchronization physiological signal data for having measured person under test, dynamic is being used
When data scaling ABR adjusts model, such as Fig. 2, Interval Slope ε are defined as related slope when blood pressure changes in the same direction with heart rate, tool
Body calculation is as follows: respectively coring rate HR and systolic pressure SBP first-order difference sequence dHR [i]=HR [i+1]-HR [i] and
DSBP [i]=SBP [i+1]-SBP [i], establishes coordinate system using dHR and dSBP as coordinate, takes the linear phase of a three quadrant data point
Relationship number is as ε.Maximum heart rate HR is found in a three quadrant data point simultaneouslyhWith HR min HRl, and heart rate median
Corresponding blood pressure Pn.To K1And K2Calibration use iterative least square algorithm, wait for training parameter in initial phase Definition Model
MatrixMode input data matrix
With output data matrixThen using 60s as the period, PTT data point in calculating cycle
Mean μPTTAnd variances sigmaPTT, according to 3 σ criterion rejecting abnormalities data, both | PTT- μPTT|>3σPTTPTT data point and correspondence
Blood-pressure measurement data point.The Least-squares minimization target of model parameter θ are as follows: It is global most
Excellent solution isTo indicate convenient, single input data is defined
μN+1=[PTT-1(iN+1) 1], single iteration least square solution are as follows: Whereinλ is that forgetting factor coefficient is set as 0.95.All data are defeated
Enter to be iterated and last regression parameter matrix θ is calculated.
Step 3: electrocardio ECG letter can easily be measured by electrocardio electrode plate and finger tip photoelectric sphyg sensor
Number and pulse PPG signal, and extract corresponding blood pressure correlated characteristic amount PTT and HR.Using PTT proposed by the present invention, HR and
The correlation map model of blood pressure P can realize the dynamic measurement of pressure value without traditional inflation cuff.
Claims (5)
1. a kind of more physiological signal Fusion Features without cuff continuous BP measurement method, which comprises the steps of:
(1) by Moens-Korteweg, that is, M-K pulse transit model and arterial baroreceptor reflex ABR model foundation artery
Weighted Fusion regression model between blood pressure P and photoplethaysmography pulse signal PPG and core signal ECG characteristic quantity；
(2) it is used according to the Weighted Fusion regression model by comparing calibration experiment with traditional cuff type sphygmomanometer
Interative least square method demarcates the individual difference parameter in Weighted Fusion regression model；
(3) using obtained individual difference parameter, pass through measurement PPG and ECG characteristic quantity with the Weighted Fusion regression model
Obtain the estimated value of individual arterial pressure.
2. a kind of more physiological signal Fusion Features according to claim 1 without cuff continuous BP measurement method, it is special
Sign is, the Correlation model of arterial pressure P Yu PPG and ECG characteristic quantity are established in the step (1), the specific steps are as follows:
Wherein PTT and HR is physiological signal characteristic quantity relevant to blood pressure, and PTT is pulse wave translation time, and HR is heart rate；K1,
K2, HRh, HRl, Pn, ε is model individual difference parameter to be calibrated；K1, K2For model regression parameter；HRh, HRlIt is ABR regulatory region
Between when highest heart rate and HR min；PnThe corresponding pressure value of section heart rate median is adjusted for ABR, ε is that ABR adjusts section
Slope.
3. a kind of more physiological signal Fusion Features according to claim 1 without cuff continuous BP measurement method, it is special
Sign is that specific step is as follows for peg model individual difference parameter in the step (2):
(2.1) nominal data acquisition experiment: it is divided into dynamic experiment and static experiment；
(2.2) signal characteristic abstraction: feature extraction is carried out to collected signal, including the pulse-transit under each heart rate beat
Time PTT, heart rate HR, systolic pressure SBP and diastolic pressure DBP；
(2.3) physiological data according to dynamic experiment acquisition calculates the slope ε in the linear regulation section in ABR regulation-control model,
HRh, HRlAnd Pn；Then the regression parameter K that SBP and DBP are corresponded in model is determined with iterative least square algorithm1s,K2sWith
And K1d,K2d。
4. a kind of more physiological signal Fusion Features according to claim 3 without cuff continuous BP measurement method, it is special
Sign is that specific step is as follows for nominal data acquisition experiment in the step (2.1):
(2.1.1) static experiment: all experimenters sit quietly 10 minutes first and adapt to environment, and cuff is then used under the state of sitting quietly
Sphygmomanometer, finger tip photoelectric sensor, electrocardio electrode plate synchronous acquisition blood pressure, pulse and electrocardiosignal 10 minutes；
(2.1.2) dynamic experiment: all experimenters carry out 5 minutes running trainings on a treadmill with the speed of 8km/h；Then
The synchronous acquisition blood pressure again under the state of sitting quietly at once, pulse and electrocardiosignal 10 minutes.
5. a kind of more physiological signal Fusion Features according to claim 3 without cuff continuous BP measurement method, it is special
Sign is that specific step is as follows for the calculating of model subjects difference parameter in the step (2.3):
Slope ε, HR in linear regulation section in (2.3.1) ABR regulation-control modelh, HRlAnd Pn: ε is defined as blood pressure and heart rate
Related slope when variation, specific calculation are as follows in the same direction: the first-order difference sequence of coring rate HR and systolic pressure SBP respectively
DHR [i]=HR [i+1]-HR [i] and dSBP [i]=SBP [i+1]-SBP [i], establishes coordinate system using dHR and dSBP as coordinate,
Take the linearly dependent coefficient of a three quadrant data point as ε；Maximum heart rate HR is found in a three quadrant data point simultaneouslyhMost
Low heart rate HRl, and the corresponding blood pressure P of heart rate mediann；
Regression parameter K in (2.3.2) model1And K2: to K1And K2Calibration use iterative least square algorithm:
In initial phase, Definition Model waits for training parameter matrix
Mode input data matrixWith output data matrix
Then using 60s as the period, the mean μ of PTT data point in calculating cyclePTTAnd variances sigmaPTT, according to 3 σ criterion rejecting abnormalities
Data were both rejected | PTT- μPTT|>3σPTTPTT data point and corresponding blood-pressure measurement data point；
The Least-squares minimization target of model parameter θ are as follows:
Its globally optimal solution is
Define single input dataμN+1=[PTT-1(iN+1) 1], in iterative least square
Single iteration least square solution in algorithm are as follows:
Whereinλ is that forgetting factor coefficient is set as 0.95；Institute
There is data input to be iterated and last regression parameter matrix θ is calculated.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109965862A (en) * | 2019-04-16 | 2019-07-05 | 重庆大学 | A kind of continuous blood pressure non-invasive monitoring method when no cuff type is long |
| CN112057063A (en) * | 2019-06-10 | 2020-12-11 | 苹果公司 | Predicting blood pressure measurements with limited pressurization |
| CN113197561A (en) * | 2021-06-08 | 2021-08-03 | 山东大学 | Low-rank regression-based robust noninvasive sleeveless blood pressure measurement method and system |
| CN114145724A (en) * | 2021-12-08 | 2022-03-08 | 四川北易信息技术有限公司 | Method for dynamically monitoring blood pressure based on ECG (electrocardiogram) and PPG (photoplethysmography) multiple physiological characteristic parameters |
| CN115462769A (en) * | 2022-09-27 | 2022-12-13 | 浙江善时生物药械(商丘)有限公司 | Noninvasive continuous real-time integrated device for blood pressure, hemodynamics, electrocardio, heart sound and cardiac function and data calculation method thereof |
| CN115844352A (en) * | 2022-11-08 | 2023-03-28 | 苏州瑞芯元医疗科技有限公司 | Micro-pressure real-time dynamic continuous blood pressure measuring device |
-
2018
- 2018-12-26 CN CN201811596951.4A patent/CN109512410A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109965862A (en) * | 2019-04-16 | 2019-07-05 | 重庆大学 | A kind of continuous blood pressure non-invasive monitoring method when no cuff type is long |
| CN112057063A (en) * | 2019-06-10 | 2020-12-11 | 苹果公司 | Predicting blood pressure measurements with limited pressurization |
| CN113197561A (en) * | 2021-06-08 | 2021-08-03 | 山东大学 | Low-rank regression-based robust noninvasive sleeveless blood pressure measurement method and system |
| CN114145724A (en) * | 2021-12-08 | 2022-03-08 | 四川北易信息技术有限公司 | Method for dynamically monitoring blood pressure based on ECG (electrocardiogram) and PPG (photoplethysmography) multiple physiological characteristic parameters |
| CN115462769A (en) * | 2022-09-27 | 2022-12-13 | 浙江善时生物药械(商丘)有限公司 | Noninvasive continuous real-time integrated device for blood pressure, hemodynamics, electrocardio, heart sound and cardiac function and data calculation method thereof |
| CN115844352A (en) * | 2022-11-08 | 2023-03-28 | 苏州瑞芯元医疗科技有限公司 | Micro-pressure real-time dynamic continuous blood pressure measuring device |
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