CN113229787A - Blood vessel age estimation and effectiveness evaluation method based on pulse signal shape characteristics - Google Patents
Blood vessel age estimation and effectiveness evaluation method based on pulse signal shape characteristics Download PDFInfo
- Publication number
- CN113229787A CN113229787A CN202110303670.0A CN202110303670A CN113229787A CN 113229787 A CN113229787 A CN 113229787A CN 202110303670 A CN202110303670 A CN 202110303670A CN 113229787 A CN113229787 A CN 113229787A
- Authority
- CN
- China
- Prior art keywords
- age
- blood vessel
- pulse signal
- effectiveness
- risk
- 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
- 210000004204 blood vessel Anatomy 0.000 title claims abstract description 83
- 238000011156 evaluation Methods 0.000 title claims abstract description 41
- 238000000034 method Methods 0.000 claims abstract description 15
- 238000007405 data analysis Methods 0.000 claims abstract description 7
- 230000001419 dependent effect Effects 0.000 claims abstract description 4
- 238000007781 pre-processing Methods 0.000 claims abstract description 4
- 238000012545 processing Methods 0.000 claims abstract description 4
- 230000002792 vascular Effects 0.000 claims description 25
- 230000032683 aging Effects 0.000 claims description 22
- 208000024172 Cardiovascular disease Diseases 0.000 claims description 19
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 210000002302 brachial artery Anatomy 0.000 claims description 3
- 210000001715 carotid artery Anatomy 0.000 claims description 3
- 238000003745 diagnosis Methods 0.000 claims description 3
- 238000012417 linear regression Methods 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 claims description 3
- 210000002321 radial artery Anatomy 0.000 claims description 3
- 210000000707 wrist Anatomy 0.000 claims description 3
- 239000000203 mixture Substances 0.000 claims 1
- 208000029078 coronary artery disease Diseases 0.000 description 7
- 238000001514 detection method Methods 0.000 description 6
- 201000010099 disease Diseases 0.000 description 4
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000012502 risk assessment Methods 0.000 description 3
- 239000000284 extract Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000036772 blood pressure Effects 0.000 description 1
- 208000026106 cerebrovascular disease Diseases 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 206010012601 diabetes mellitus Diseases 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000009323 psychological health Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000000391 smoking effect Effects 0.000 description 1
- 230000008719 thickening Effects 0.000 description 1
- 208000019553 vascular disease Diseases 0.000 description 1
Images
Classifications
-
- 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
-
- 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
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
- A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
- A61B5/726—Details of waveform analysis characterised by using transforms using Wavelet transforms
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7271—Specific aspects of physiological measurement analysis
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Public Health (AREA)
- Surgery (AREA)
- Veterinary Medicine (AREA)
- Physiology (AREA)
- General Health & Medical Sciences (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Animal Behavior & Ethology (AREA)
- Artificial Intelligence (AREA)
- Psychiatry (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Signal Processing (AREA)
- Mathematical Physics (AREA)
- Cardiology (AREA)
- Evolutionary Computation (AREA)
- Fuzzy Systems (AREA)
- Vascular Medicine (AREA)
- Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
Abstract
The invention discloses a blood vessel age estimation and effectiveness evaluation method based on pulse signal shape characteristics, which comprises the following steps: s11: acquiring pulse signals, collecting original pulse signals, and establishing a data set; s12: data preprocessing, namely performing primary processing on an original pulse signal to reduce the influence of interference factors on the pulse signal; s13: extracting the shape characteristics of the pulse signals by utilizing a functional data analysis method; s14: establishing a blood vessel age estimation model, and taking the shape characteristics of the pulse signals as independent variables; establishing a regression model by taking the time age as a dependent variable; s15: calculating the age of the blood vessel, namely calculating the predicted age of any sample by using the established regression model, and taking the predicted age as the estimated value of the age of the blood vessel; the invention aims to estimate the age of a blood vessel by analyzing the shape characteristics of a pulse signal and evaluate the scientificity and effectiveness of the age of the blood vessel.
Description
Technical Field
The invention belongs to the technical field of cardiovascular disease risk assessment, and particularly relates to a blood vessel age estimation and effectiveness evaluation method based on pulse signal shape characteristics.
Background
The vascular diseases are the first killers threatening the physical and psychological health of people, and even the most advanced rehabilitation means is adopted, the life of most cerebrovascular disease people is difficult to be managed. Therefore, the assessment of the risk of cardiovascular diseases, the implementation of corresponding measures in the absence of disease or in the early stages of disease, is of great significance. Meanwhile, cardiovascular diseases are one of the most common aging diseases, and are frequently seen in people over 50 years old. Current studies also indicate that chronological age is an independent risk factor for cardiovascular disease, with higher prevalence of cardiovascular disease.
In order to assess the risk of cardiovascular diseases and to intervene early in people at high risk for cardiovascular diseases to prevent the occurrence of diseases, many countries or regions have developed corresponding cardiovascular disease risk screening scales, such as: Framingham-CHD, EURO-SCORE, Q-RISK2, PCRE, ICVD, China-PAR, etc. Kannel et al designed and developed the Framingham-CHD Risk assessment Scale, which was recognized as the earliest cardiovascular disease risk assessment model, as early as 1976. The Framingham Heart study originally proposed the concept of risk factors, and the risk score of coronary heart disease or death in the next 10 years was obtained by combined scoring of risk factors, and the model was optimized and adjusted many times in subsequent studies. The subjects studied for Framingham-CHD were European Americans without coronary heart disease, and the risk factors for inclusion mainly included: age, sex, blood pressure, diabetes, smoking history, etc. The remaining scales are all roughly similar to the Framingham-CHD scale.
Body aging is a normal physiological phenomenon, and vascular aging is also accompanied by the normal physiological phenomenon, and reduction in vascular elasticity, deterioration in compliance, thickening of intima, and the like are important manifestations of vascular aging. Researchers have used various modern devices to detect these changes in vascular aging and have proposed a number of indirect indicators of vascular aging. The common blood vessel aging degree indirect evaluation indexes based on pulse signals are as follows: pulse Wave Velocity (PWV), reflection enhancement Index (AIx), diastole enhancement Index (DAI), Pulse Transit Time (PTT), and the like. These indirect evaluation indexes are not completely uniform, but they are all used to indirectly evaluate the aging degree of blood vessels from the respective angles, and the measurement units are different.
The direct index of aging is age, and the vascular age is a direct index of vascular aging assessment, but there is no currently accepted method of estimating vascular age. In fact, vascular age is a virtual concept describing the degree of vascular aging. The blood vessel aging indirect evaluation parameter based on the pulse signal can be directly obtained by medical instruments and equipment, and each index can be interpreted from the physiological perspective, so that the parameter is easily accepted from the perspective of medical care personnel. However, the general public does not have professional medical science and science knowledge, and the general public can understand the vascular aging parameters more deeply, so that the compliance is improved.
Disclosure of Invention
The invention aims to provide a blood vessel age estimation and effectiveness evaluation method based on pulse signal shape characteristics, which aims to estimate the blood vessel age by analyzing the shape characteristics of a pulse signal and evaluate the scientificity and effectiveness of the blood vessel age.
The purpose of the invention can be realized by the following technical scheme:
a blood vessel age estimation based on pulse signal shape characteristics, the blood vessel age estimation comprising the steps of:
s11: acquiring pulse signals, collecting original pulse signals, and establishing a data set;
s12: data preprocessing, namely performing primary processing on an original pulse signal to reduce the influence of interference factors on the pulse signal;
s13: extracting the shape characteristics of the pulse signals by utilizing a functional data analysis method;
s14: establishing a blood vessel age estimation model, and taking the shape characteristics of the pulse signals as independent variables; establishing a regression model by taking the time age as a dependent variable;
s15: and calculating the blood vessel age, calculating the predicted age of any sample by using the established regression model, and taking the predicted age as the estimated value of the blood vessel age.
As a further scheme of the invention: the positions of pulse signal acquisition in S11 are the wrist radial artery, the carotid artery and the brachial artery.
As a further scheme of the invention: in S13, the pulse signal is converted into a continuously changing function model, and the shape feature of the pulse signal is extracted by a function data analysis method.
As a further scheme of the invention: the function model comprises a mixed Gaussian function model, a Fourier basis function model or a wavelet basis function model.
As a further scheme of the invention: the regression model in S14 includes multiple linear regression, support vector regression, neural network, or deep regression.
As a further scheme of the invention: the effectiveness evaluation method for estimating the blood vessel age based on the pulse signal shape characteristics specifically comprises the following steps:
s21: carrying out blood vessel age effectiveness evaluation according to the indirect evaluation parameter of the blood vessel aging degree, and calculating the correlation between the blood vessel age and the time age and the indirect evaluation parameter, wherein the correlation between the blood vessel age and the indirect evaluation parameter is higher than the correlation between the time age and the indirect evaluation parameter, which indicates that the blood vessel age can represent the aging degree of the blood vessel;
s22: evaluating the effectiveness of the vascular age according to the risk of cardiovascular diseases, evaluating the risk of cardiovascular diseases according to a risk evaluation scale formulated by risk factors by utilizing the principle that the greater the vascular age, the higher the risk of cardiovascular diseases, and for the people in the same age period, the consistency of the evaluation result of the risk evaluation scale and the vascular age indicates the effectiveness of evaluating the vascular aging by the vascular age;
s23: and (3) evaluating the effectiveness of the blood vessel ages according to the diagnosis result of the clinical patient, monitoring the blood vessel ages of the sick people and the healthy people in the same age period, and indicating the effectiveness of the blood vessel ages if the blood vessel ages of the sick people are larger than the blood vessel ages of the healthy people.
The invention has the beneficial effects that:
the idea of obtaining the age of the blood vessel by using the IMT as an independent variable to regress is similar to the idea of obtaining the age of the blood vessel by using the shape characteristic regression of the pulse signal, but the IMT is inconvenient to obtain, the detection equipment is extremely expensive and generally about 100W human life currency, the detection equipment of the pulse signal is very cheap, and few hundred yuan human currency can effectively reduce the detection cost; the blood vessel age obtained by the technology has effectiveness, and the accuracy of the blood vessel age is evaluated in multiple aspects.
In the invention, the pulse signal is objective physiological information, so that the subjectivity of the scale can be effectively avoided.
The invention extracts the function type characteristic of the pulse signal from the aspect of mathematical modeling, the evaluation angle of the technology is more comprehensive in the module for evaluating the effectiveness of the blood vessel age, the invention estimates the blood vessel age by analyzing the shape characteristic of the pulse signal, and the scientificity and the effectiveness of the blood vessel age are evaluated.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic view of the flow structure of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the blood vessel age estimation based on the pulse signal shape feature includes the following steps:
s11: acquiring pulse signals, collecting original pulse signals, and establishing a data set;
s12: data preprocessing, namely performing primary processing on an original pulse signal to reduce the influence of interference factors on the pulse signal;
s13: extracting the shape characteristics of the pulse signals by utilizing a functional data analysis method; the existing research shows that pulse signal shapes of people in different age groups have certain difference. Meanwhile, the pulse signals of people of the same age group have individual differences. The traditional time domain or frequency domain features have difficulty in expressing the overall shape features of the pulse signals.
S14: establishing a blood vessel age estimation model, and taking the shape characteristics of the pulse signals as independent variables; and establishing a regression model by taking the time age as a dependent variable.
S15: and calculating the blood vessel age, calculating the predicted age of any sample by using the established regression model, and taking the predicted age as the estimated value of the blood vessel age.
The positions of pulse signal acquisition in the S11 are the wrist radial artery, the carotid artery and the brachial artery, and common pulse signal acquisition devices are mainly divided into three types: the pressure sensor, the ultrasonic sensor and the photoelectric sensor are very cheap, and few hundreds of yuan RMB can effectively reduce the detection cost.
In S13, the pulse signal is regarded as a continuously changing function model, and the shape feature of the pulse signal is extracted by a function data analysis method. The function model comprises a mixed Gaussian function model, a Fourier basis function model or a wavelet basis function model.
The regression model in S14 includes multiple linear regression, support vector regression, neural network, or deep regression.
The effectiveness evaluation method for estimating the blood vessel age based on the pulse signal shape characteristics is used for scientifically and effectively evaluating the blood vessel age obtained by a model from multiple angles, and specifically comprises the following steps:
s21: the blood vessel age effectiveness evaluation is carried out according to the indirect evaluation parameters of the blood vessel aging degree, and the common indirect evaluation parameters for evaluating the blood vessel aging degree include: AIx, PTT, PWV, ABI, CAVI, FMD, IMT, etc., calculating the correlation between the age of the blood vessel and the time age and indirect parameters, wherein the correlation between the age of the blood vessel and the indirect evaluation parameters is higher than that of the time age, and the age of the blood vessel is more representative of the aging degree of the blood vessel;
s22: the method comprises the following steps of carrying out blood vessel age validity evaluation according to the risk of cardiovascular diseases, and evaluating the risk of cardiovascular diseases according to a risk evaluation scale established by risk factors by utilizing the principle that the greater the blood vessel age, the higher the risk of cardiovascular diseases, wherein most of the existing tools for evaluating the risk of cardiovascular diseases are the risk evaluation scale established by the risk factors: Framingham-CHD, EURO-SCORE, Q-RISK2, PCRE, ICVD, China-PAR and the like, for the population in the same time age bracket, the evaluation result of the RISK evaluation scale is consistent with the age of the blood vessel, which shows that the age of the blood vessel is evaluated to be effective;
s23: and (3) evaluating the effectiveness of the blood vessel ages according to the diagnosis result of the clinical patient, monitoring the blood vessel ages of the sick people and the healthy people in the same age period, and indicating the effectiveness of the blood vessel ages if the blood vessel ages of the sick people are larger than the blood vessel ages of the healthy people.
The present invention, when used, has the following differences compared to prior art evaluation methods:
(1) the IMT is used as an independent variable to obtain the blood vessel age, and the concept of obtaining the blood vessel age by utilizing the shape characteristic regression of the pulse signal is similar to that of the invention; the acquisition of IMT is inconvenient, the detection equipment is extremely expensive, generally about 100W RMB, but the detection equipment of pulse signals is very cheap, and less, hundreds of RMB; this technique does not evaluate the effectiveness of the obtained vascular age in many ways.
(2) The SCORE scale is for 12 countries in europe, and people in different regions have different lifestyles; therefore, any scale has difficulty in incorporating all risk factors, and in the technology, 12 countries in europe are divided into 2 groups of high risk regions and low risk regions for modeling; in the invention, the pulse signal is objective physiological information, so that the subjectivity of the scale can be effectively avoided. The effectiveness of the obtained vascular age is not available in the prior art, and various evaluations are carried out; the invention evaluates the effectiveness of the vascular age from multiple angles.
(3) The method adopts a function point-tracing expression idea from the aspect of feature expression, and extracts the functional features of the pulse signals from the aspect of mathematical modeling. In the module for evaluating the effectiveness of the blood vessel age, the evaluation angle of the technology is single; according to the invention, the effectiveness evaluation of the vascular age is evaluated from multiple angles, so that the accuracy of evaluation is ensured.
In the technical scheme of the invention, compared with the prior art, the corresponding model establishment is shown in the following table:
the preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (6)
1. The method for estimating the age of the blood vessel and evaluating the effectiveness based on the shape characteristics of the pulse signal is characterized by comprising the following steps of:
s11: acquiring pulse signals, collecting original pulse signals, and establishing a data set;
s12: data preprocessing, namely performing primary processing on an original pulse signal to reduce the influence of interference factors on the pulse signal;
s13: extracting the shape characteristics of the pulse signals by utilizing a functional data analysis method;
s14: establishing a blood vessel age estimation model, and taking the shape characteristics of the pulse signals as independent variables; establishing a regression model by taking the time age as a dependent variable;
s15: and calculating the blood vessel age, calculating the predicted age of any sample by using the established regression model, and taking the predicted age as the estimated value of the blood vessel age.
2. The pulse signal shape feature based blood vessel age estimation of claim 1, wherein the locations of pulse signal acquisition in S11 are wrist radial artery, carotid artery and brachial artery.
3. The method of claim 1, wherein the pulse signal is transformed into a continuously varying function model in step S13, and the shape feature of the pulse signal is extracted by a functional data analysis method.
4. The pulse signal shape feature based blood vessel age estimation according to claim 3, wherein the function model comprises a mixture Gaussian function model, a Fourier basis function model, or a wavelet basis function model.
5. The pulse signal shape feature based blood vessel age estimation of claim 1, wherein the regression model in S14 comprises multiple linear regression, support vector regression, neural network or depth regression.
6. The method for evaluating the effectiveness of blood vessel age estimation based on the pulse signal shape feature of claim 1, wherein the method for evaluating the effectiveness specifically comprises the following steps:
s21: carrying out blood vessel age effectiveness evaluation according to the indirect evaluation parameter of the blood vessel aging degree, and calculating the correlation between the blood vessel age and the time age and the indirect evaluation parameter, wherein the correlation between the blood vessel age and the indirect evaluation parameter is higher than the correlation between the time age and the indirect evaluation parameter, which indicates that the blood vessel age can represent the aging degree of the blood vessel;
s22: evaluating the effectiveness of the vascular age according to the risk of cardiovascular diseases, evaluating the risk of cardiovascular diseases according to a risk evaluation scale formulated by risk factors by utilizing the principle that the greater the vascular age, the higher the risk of cardiovascular diseases, and for the people in the same age period, the consistency of the evaluation result of the risk evaluation scale and the vascular age indicates the effectiveness of evaluating the vascular aging by the vascular age;
s23: and (3) evaluating the effectiveness of the blood vessel ages according to the diagnosis result of the clinical patient, monitoring the blood vessel ages of the sick people and the healthy people in the same age period, and indicating the effectiveness of the blood vessel ages if the blood vessel ages of the sick people are larger than the blood vessel ages of the healthy people.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110303670.0A CN113229787A (en) | 2021-03-22 | 2021-03-22 | Blood vessel age estimation and effectiveness evaluation method based on pulse signal shape characteristics |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110303670.0A CN113229787A (en) | 2021-03-22 | 2021-03-22 | Blood vessel age estimation and effectiveness evaluation method based on pulse signal shape characteristics |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113229787A true CN113229787A (en) | 2021-08-10 |
Family
ID=77130576
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110303670.0A Pending CN113229787A (en) | 2021-03-22 | 2021-03-22 | Blood vessel age estimation and effectiveness evaluation method based on pulse signal shape characteristics |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113229787A (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104127173A (en) * | 2014-07-14 | 2014-11-05 | 昆明理工大学 | Pathology information extraction method based on dynamic pulse wave feature parameters |
| CN105283120A (en) * | 2013-03-13 | 2016-01-27 | 艾沃锐斯特基因有限公司 | Flow-mediated dilation to determine vascular age |
| CN107822608A (en) * | 2017-10-26 | 2018-03-23 | 中国民航大学 | Pulse wave feature extracting method based on gauss hybrid models |
| CN107961001A (en) * | 2017-12-20 | 2018-04-27 | 中国科学院深圳先进技术研究院 | Appraisal procedure, device and the atherosclerosis detector of Degree of arteriosclerosis |
| CN108185996A (en) * | 2017-12-27 | 2018-06-22 | 中国科学院深圳先进技术研究院 | Arteries age appraising model construction method and device |
| CN110598625A (en) * | 2019-09-10 | 2019-12-20 | 北京望问信息科技有限公司 | Identity recognition technology based on pulse wave non-reference characteristics |
| CN112136048A (en) * | 2018-02-21 | 2020-12-25 | 艾迪菲斯健康有限公司 | Method for measuring systemic chronic inflammatory aging |
-
2021
- 2021-03-22 CN CN202110303670.0A patent/CN113229787A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105283120A (en) * | 2013-03-13 | 2016-01-27 | 艾沃锐斯特基因有限公司 | Flow-mediated dilation to determine vascular age |
| CN104127173A (en) * | 2014-07-14 | 2014-11-05 | 昆明理工大学 | Pathology information extraction method based on dynamic pulse wave feature parameters |
| CN107822608A (en) * | 2017-10-26 | 2018-03-23 | 中国民航大学 | Pulse wave feature extracting method based on gauss hybrid models |
| CN107961001A (en) * | 2017-12-20 | 2018-04-27 | 中国科学院深圳先进技术研究院 | Appraisal procedure, device and the atherosclerosis detector of Degree of arteriosclerosis |
| CN108185996A (en) * | 2017-12-27 | 2018-06-22 | 中国科学院深圳先进技术研究院 | Arteries age appraising model construction method and device |
| CN112136048A (en) * | 2018-02-21 | 2020-12-25 | 艾迪菲斯健康有限公司 | Method for measuring systemic chronic inflammatory aging |
| CN110598625A (en) * | 2019-09-10 | 2019-12-20 | 北京望问信息科技有限公司 | Identity recognition technology based on pulse wave non-reference characteristics |
Non-Patent Citations (1)
| Title |
|---|
| 王琳琳,王磊,张晓阳: "基于脉搏波波形特征参量的成年男性血管年龄预测模型", 《医疗卫生装备》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Charlton et al. | Wearable photoplethysmography for cardiovascular monitoring | |
| CN108185996B (en) | Arterial blood vessel age estimation model construction method and device | |
| KR100954817B1 (en) | System and method for testing blood vessel's health and stress through signal analysis of pluse wave | |
| CN109276241B (en) | Pressure identification method and equipment | |
| Zuo et al. | Comparison of three different types of wrist pulse signals by their physical meanings and diagnosis performance | |
| CN107788969A (en) | The automatic testing method of QRS complex in a kind of electrocardiosignal | |
| CN112274121B (en) | Noninvasive arteriosclerosis detection method and device based on multipath pulse waves | |
| CN113556971A (en) | System for monitoring physiological parameters | |
| Jang et al. | A knowledge-based approach to arterial stiffness estimation using the digital volume pulse | |
| Kim et al. | Novel diagnostic model for the deficient and excess pulse qualities | |
| CN112274120B (en) | Noninvasive arteriosclerosis detection method and device based on one-way pulse wave | |
| CN109222941A (en) | A kind of measurement method and measuring device of pulse wave propagation time | |
| Martsenyuk et al. | Information system of arterial oscillography for primary diagnostics of cardiovascular diseases | |
| Li et al. | The correlation study of Cun, Guan and Chi position based on wrist pulse characteristics | |
| Argha et al. | Blood pressure estimation from korotkoff sound signals using an end-to-end deep-learning-based algorithm | |
| CN114145725B (en) | PPG sampling rate estimation method based on noninvasive continuous blood pressure measurement | |
| CN116451129A (en) | Pulse classification and identification method and system | |
| CN113229787A (en) | Blood vessel age estimation and effectiveness evaluation method based on pulse signal shape characteristics | |
| Lin et al. | Research on feature mining algorithm and disease diagnosis of pulse signal based on piezoelectric sensor | |
| CN113712524A (en) | Data processing device, system and kit for auxiliary evaluation of cardiovascular risk | |
| Vakulenkoa et al. | New application of blood pressure monitor with software environment Oranta-AO based on arterial oscillography methods | |
| Savostin et al. | DEVISING A METHOD FOR PREDICTING A BLOOD PRESSURE LEVEL BASED ON ELECTROCARDIOGRAM AND PHOTOPLETHYSMOGRAM SIGNALS. | |
| Kadarmandalgi et al. | Pulse diagnosis System for Nadi Pariksha using parametric and statistical analysis | |
| Cano et al. | Novel photoplethysmographic and electrocardiographic features for enhanced detection of hypertensive individuals | |
| Wang et al. | A comparison of three types of pulse signals: Physical meaning and diagnosis performance |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210810 |
|
| RJ01 | Rejection of invention patent application after publication |