CN112274120A - Noninvasive arteriosclerosis detection method and device based on one-way pulse wave - Google Patents
Noninvasive arteriosclerosis detection method and device based on one-way pulse wave Download PDFInfo
- Publication number
- CN112274120A CN112274120A CN202011170907.4A CN202011170907A CN112274120A CN 112274120 A CN112274120 A CN 112274120A CN 202011170907 A CN202011170907 A CN 202011170907A CN 112274120 A CN112274120 A CN 112274120A
- Authority
- CN
- China
- Prior art keywords
- pulse wave
- arteriosclerosis
- points
- characteristic
- artery
- 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.)
- Granted
Links
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
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7246—Details of waveform analysis using correlation, e.g. template matching or determination of similarity
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Heart & Thoracic Surgery (AREA)
- Public Health (AREA)
- Physiology (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Veterinary Medicine (AREA)
- Biomedical Technology (AREA)
- Physics & Mathematics (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Vascular Medicine (AREA)
- Cardiology (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Psychiatry (AREA)
- Signal Processing (AREA)
- Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
Abstract
The invention provides a non-invasive arteriosclerosis detection method and a non-invasive arteriosclerosis detection device based on one-way pulse waves, which relate to the field of arteriosclerosis detection, and the method comprises the following steps: acquiring pulse wave signals of main artery points and other positions which are convenient to test of the whole body of a target object, preprocessing the signals, selecting feature points, calculating to obtain feature parameters, combining physiological information of the target object to construct an arteriosclerosis model feature set, analyzing and screening through correlation to obtain a feature subset, taking the feature subset as input, taking the arteriosclerosis degree as output, training by applying a machine learning algorithm to obtain an arteriosclerosis model based on the feature subset, and further combining the physiological information of a specific target object to calibrate the model and finish the detection of arteriosclerosis. The method for non-invasive arteriosclerosis detection is more convenient, more accurate and feasible.
Description
Technical Field
The invention relates to the field of arteriosclerosis detection, and particularly provides a non-invasive arteriosclerosis detection method and device based on a single-path pulse wave.
Background
Cardiovascular disease is a serious disease that seriously endangers human health, and most cardiovascular diseases have clinical manifestations in later stages, while there are few opportunities for examination in the early and middle stages. Arteriosclerotic lesions are a common pathophysiological basis for most cardiovascular diseases, and it is based on this that accurate detection of the degree of arteriosclerosis is critical to the level of prevention and treatment of cardiovascular diseases.
For arteriosclerosis, methods for detecting the arteriosclerosis degree at the present stage are mainly classified into invasive and non-invasive methods, and the invasive detection method is an arteriography method, has the defects of being invasive and potentially injuring a human body, is only suitable for screening in middle and late stages, and is not beneficial to early stage arteriosclerosis detection. The non-invasive arteriosclerosis detection method mainly comprises a biochemical method and an Ankle Brachial Index (ABI), and the two methods are not visual and comprehensive in arteriosclerosis detection; the method mainly uses imaging means such as carotid artery ultrasound, CT scanning, nuclear magnetic resonance and the like to detect the artery structural lesion clinically, and has the defects of high price, complex operation, requirement of professional operation, inconvenience for portable monitoring and the like. A pulse wave-based noninvasive arteriosclerosis detection method gradually becomes a hot spot in the arteriosclerosis detection field. The traditional non-invasive arteriosclerosis detection method based on pulse wave can be distinguished according to detection parameters. The cervical femoral pulse wave velocity (cfPWV) is the golden standard for non-invasive evaluation of arteriosclerosis, the ankle pulse wave velocity (baPWV) can also detect arteriosclerosis within a certain range, and pulse wave parameter detection methods such as an Augmentation Index (AI) and a Diastolic Augmentation Index (DAI) are also provided, but the detection accuracy needs to be improved.
Aiming at the problem that the existing noninvasive arteriosclerosis detection methods based on pulse waves are not high in accuracy, no effective solution is provided at present.
Disclosure of Invention
In view of the above, the present invention provides a method and an apparatus for non-invasive arteriosclerosis detection based on one-way pulse wave, so as to improve the problem that the accuracy of the non-invasive arteriosclerosis detection is not ideal in the existing pulse wave based devices.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
in a first aspect, the present invention provides a non-invasive arteriosclerosis detecting method based on one-way pulse wave, comprising:
taking main artery points of the whole body and other position points which are convenient for collecting pulse waves as alternative collecting points, recording pulse wave signals obtained by each collecting point as original pulse wave signals, preprocessing the original pulse wave signals to obtain preprocessed original pulse wave signals A, and then obtaining first-order difference pulse wave signals B and second-order difference pulse wave signals C of the preprocessed original pulse wave signals; preprocessing here refers to filtering for noise reduction,
normalizing the preprocessed original pulse wave signal A, the first-order difference pulse wave signal B and the second-order difference pulse wave signal C (normalizing A, B and C respectively to obtain A1, B1 and C1),
then, feature point extraction is carried out in a time domain, main feature points (the main feature points refer to the starting points, the horizontal and vertical coordinates of the wave crests and the wave troughs of A1, B1 and C1 and the horizontal and vertical coordinates of the wave crests and the wave troughs of the dicrotic wave in A) of each signal after normalization processing are obtained, and then a pulse wave time domain feature parameter set is obtained through calculation;
meanwhile, feature point extraction is carried out in a frequency domain based on the normalized original pulse wave signals, frame division and window processing are carried out on the normalized original pulse wave signals, then discrete Fourier transform (FFT) is carried out to obtain a power spectrum of the signals, a digital filter bank is introduced to carry out filtering processing on the power spectrum in a pulse wave related frequency band, then logarithm operation is carried out, and finally pulse wave cepstrum coefficients are obtained through Discrete Cosine Transform (DCT); bringing the pulse wave cepstrum coefficients into a pulse wave frequency domain characteristic parameter set so as to obtain a pulse wave frequency domain characteristic parameter set;
extracting characteristic points of the normalized second-order difference pulse wave signals, comparing the characteristic points with main characteristic points of the normalized original pulse wave signals, and calculating to obtain pulse wave conduction time PWTT;
introducing main physiological information and health information of a target object, and combining pulse wave time-domain characteristic parameter sets, pulse wave frequency-domain characteristic sets and PWTT (pulse wave conduction time PWTT) of all sampling points to establish an arteriosclerosis model characteristic set;
screening feature data in the feature set of the arteriosclerosis model through correlation analysis to obtain a feature subset; training by using a machine learning algorithm to obtain an arteriosclerosis model based on the characteristic subset;
the physiological information and the health information of the object to be detected are input, any pulse wave signal is collected and processed, the processed signal is input into a trained arteriosclerosis model based on the feature subset, the noninvasive arteriosclerosis detection can be completed, and the detection result is output.
Further, the above-mentioned main artery point of the whole body and other position points convenient for collecting pulse wave are used as alternative collecting points, and the step of collecting one pulse wave signal includes:
1) collecting pulse wave signals by adopting various sensors; adopting a photoelectric volume type pulse wave sensor to obtain an arterial pulse wave signal of a tested person by measuring an optical signal of blood in an artery of the tested person; the pressure sensors are adopted, so that the pressure applied to each part of the target object to be tested is uniform and moderate, the accuracy of a test result is ensured, and the use experience of a user is improved;
2) one of the pulse wave signals of multiple parts of the body of the target object is acquired, including main artery points and other positions convenient to test, such as: carotid artery, brachial artery, radial artery, earlobe, distal finger, femoral artery, peroneal artery, popliteal artery, posterior tibial artery, dorsal foot artery, distal toe, etc.;
3) the various sensors are integrated in a fixing device, at least one photoelectric volume type pulse wave sensor and one pressure sensor are arranged in the fixing device, and the fixing device is realized in the form that: the fixing device comprises a headband, a wrist strap, a cuff, a finger clip, an ear clip, gloves, a sleeve and a watch or a bracelet, and two sensors are embedded in the fixing device and are in close contact with the skin of a tested person, so that the accuracy of a measuring result is ensured. Preferably, the radial artery of the wrist is collected to form a bracelet type, or the brachial artery is collected to form a cuff type
The method comprises the following steps of preprocessing signals by utilizing the acquired pulse wave signals, wherein the method comprises the following steps:
filtering the pulse wave signal by using a Butterworth band-pass filter to filter high-frequency noise;
wavelet decomposition and reconstruction are carried out on the pulse wave signals by utilizing wavelet transformation, low-frequency noise is filtered, and baseline drift is removed.
The step of extracting the feature points in the time domain includes:
and (3) combining a first-order difference threshold algorithm, a second-order difference threshold algorithm, a third-order difference threshold algorithm and wavelet transformation, and detecting feature points of the preprocessed and normalized original pulse wave signal, the first-order difference pulse wave signal and the second-order difference pulse wave signal to further obtain a time domain feature point position (the position refers to the horizontal and vertical coordinates of the feature points) to obtain a pulse wave time domain feature parameter set.
The pulse wave time domain feature point position refers to the starting point, the wave crest and the wave trough of three pulse wave signals which are preprocessed and normalized, and the horizontal and vertical coordinates of the wave crest and the wave trough of a dicrotic wave in the original pulse wave signal, namely the main feature point.
The pulse wave time domain feature parameter set comprises:
1) time parameters: pulse cycle time T, main wave rising time T1, time from the starting point to the trough of the dicrotic wave T2, time from the trough of the dicrotic wave to the ending point T3, time from the main wave peak at the same height of the trough of the dicrotic wave T4, blood vessel hardness index T4/T, heart beat rate coefficient T2/T3, myocardial contraction coefficient T1/T, heart beat output coefficient (T2-T1)/T, time difference between the first order difference signal and the second order difference signal starting point, time interval from the trough of the first order difference signal to the ending point, and time interval from the trough of the second order difference signal to the ending point;
2) slope parameter: ascending branch slope AS and descending branch slope DS;
3) amplitude parameter: the amplitude difference AID of the ascending branch, the amplitude difference DID of the descending branch, the amplitude H1 from the main wave crest to the starting point, the amplitude H2 from the repeating wave to the starting point, the peripheral resistance coefficient H2/H1, the wave valley amplitude of the repeating wave, the ratio of the amplitude difference between the wave crest and the repeating wave to the amplitude difference between the wave crest and the wave valley, the ratio of the amplitude of the wave crest to the amplitude of the starting point and the ratio of the amplitude of the wave crest to the amplitude of the wave valley;
4) area parameters: ascending branch area AA, descending branch area DA, and ascending branch to descending branch area ratio;
5) other parameters: heart rate, blood oxygen, growth index AI, diastolic growth index DAI.
The step of obtaining the pulse wave frequency domain characteristic parameter set comprises the following steps:
1) normalizing the preprocessed original pulse wave signal A to obtain a normalized A1 signal, framing, pre-emphasizing and windowing to obtain a time domain pulse wave signal segment x (n), wherein an FFT conversion formula is as follows:
wherein k is 0, …, and N-1 represents the frequency point corresponding to FFT operation; where N is 0, 1, …, N-1, which refers to the time domain signal, k represents the frequency point; n is the number of divided frames; j is an imaginary unit, j2＝-1
2) In the relevant frequency range of pulse wave (such as 0.5Hz-5Hz), M groups of digital filters (M is generally 16-32) are introduced, and the frequency response of each filter is Hm(k) Filtering the power spectrum, and then carrying out logarithm operation to obtain a logarithm spectrum transfer function SmFinally, obtaining a cepstrum coefficient C (l) of the static pulse wave through Discrete Cosine Transform (DCT);
wherein L is 1, …, L, L represents the order of pulse wave cepstrum coefficient, the value of L is related to the pulse wave main frequency domain width, generally 8-16,
3) acquiring dynamic cepstrum coefficients on the basis of the static pulse wave cepstrum coefficients, wherein the dynamic cepstrum coefficients are first-order differential pulse wave cepstrum coefficients delta C (l) and second-order differential pulse wave cepstrum coefficients delta C (l);
4) to this end, 3 × L cepstral coefficients are obtained from c (L), Δ Δ c (L), and constitute the pulse wave frequency domain feature parameter set.
The above-mentioned main physiological information and health information of the introduction target object include: gender, age, height, weight, BMI, smoking or not, chronic medical history (heart disease, hypertension, diabetes, dyslipidemia, etc.) of the target subject.
The step of obtaining the reference arteriosclerosis degree comprises:
1) clinical procedure derived ankle index (ABI)
2) Cervical femoral pulse wave velocity (cfPWV) obtained by clinical method
3) Ankle pulse wave velocity (baPWV) obtained by clinical method
4) With reference to the degree of arteriosclerosis AS, then:
AS＝a*ABI+b*cfPWV+c*baPWV
wherein a, b and c are weight coefficients, which can be obtained by experimental fitting or set by experience, and corresponding reference arteriosclerosis degrees can be calculated under the conditions of known ankle-brachial index, neck-femoral pulse wave velocity and ankle-brachial pulse wave velocity.
The method comprises the following steps of carrying out correlation analysis on the characteristic set of the arteriosclerosis model and the reference arteriosclerosis degree, and screening to obtain a characteristic subset corresponding to the current target object, wherein the steps comprise:
1) and (3) calculating mutual information correlation degree of each characteristic parameter in the arteriosclerosis model characteristic set and the reference arteriosclerosis degree by using a mutual information theory, and sequencing, wherein a mutual correlation formula is as follows:
wherein X and Y respectively represent two random variables, X, Y is an arteriosclerosis model characteristic set, I (X; Y) is more than or equal to 0 represents the cross-correlation degree of the two variables, and the larger the cross-correlation degree is, the higher the cross-correlation degree is; p (x) p (y) represents the probability distribution of x, y, p (x, y) represents the joint probability distribution of x, y;
2) and according to a minimum redundancy maximum correlation (mRMR) criterion, maximizing the correlation between the characteristic parameters and the reference arteriosclerosis degree, minimizing the correlation between the characteristic parameters, and screening to obtain a characteristic subset corresponding to the current target object.
An arteriosclerosis model frame composed of a neural network model and a machine learning algorithm is established, a characteristic subset is used as input, reference is made to arteriosclerosis degree as output, the arteriosclerosis model based on the characteristic subset is obtained by training the characteristic subsets corresponding to different target objects, and the quantity of the different target objects is infinite (in the embodiment, a plurality of target objects with different heights, fatness, sex, age BMI, smoking and different chronic histories (heart disease, hypertension, diabetes and dyslipidemia) are selected for training (generally, the quantity of users is more than 1000, and the data quantity can be from a public database such as MIMIC or a mechanism provided by other medical data), the method comprises the following steps:
1) training to obtain an arteriosclerosis model AS based on a machine learning algorithm1；
2) Training to obtain an arteriosclerosis model AS based on a neural network model2；
3) The arteriosclerosis model is:
AS＝d*AS1+e*AS2
wherein d and e are weight coefficients.
In a second aspect, the present invention provides a non-invasive arteriosclerosis detecting device based on one-way pulse wave, comprising:
the signal acquisition module acquires single pulse wave signals of main artery points of the whole body or other positions where pulse waves are convenient to test;
the preprocessing module is used for filtering high-frequency noise and low-frequency noise of the pulse wave signals;
the time domain feature extraction module is used for calculating to obtain a time domain feature parameter set;
the frequency domain characteristic extraction module is used for calculating to obtain a frequency domain characteristic parameter set;
the PWTT extracting module is used for calculating and obtaining a PWTT (pulse wave conduction time PWTT);
the information input module is used for inputting physiological information and health information of the target object;
the characteristic screening module is used for carrying out correlation analysis on the characteristic set of the arteriosclerosis model and screening to obtain a characteristic subset;
and the arteriosclerosis detection module is used for outputting an arteriosclerosis detection result by taking the characteristic subset corresponding to the target object as input based on a pre-trained arteriosclerosis model based on the characteristic subset.
The signal acquisition module comprises at least one fixing device, and at least one photoelectric volume type pulse wave sensor and a pressure sensor are integrated in each fixing device; the fixing device is a head band, a wrist band, a sleeve band, a finger clip, an ear clip, a glove, a sleeve and a watch or a hand ring. The detection device can be provided with a plurality of fixing devices with different forms, and a convenient one can be selected by a user for signal acquisition according to the use habit of the user.
Compared with the prior art, the invention has the beneficial effects that:
the invention mainly evaluates arteriosclerosis based on pulse wave signals, and the acquisition mode is that data acquired by a plurality of pulse wave acquisition points are input when a model is manufactured, and the pulse wave acquisition points are not limited to a certain fixed point when the model is used at a later stage, so that the signal acquisition module has various specific implementation modes, such as a wrist strap type, an ear clip type, a cuff type, a finger clip type and the like, and performs data transmission with the arteriosclerosis detection module in a wireless or wired mode, and the information input module and the arteriosclerosis detection module can be integrated into portable equipment such as a smart phone and the like, so that the acquisition and implementation processes are more convenient and portable.
The method can be applied to arteriosclerosis detection after the training of the arteriosclerosis model based on the characteristic subset is completed, so that a user can automatically measure the arteriosclerosis only by inputting physiological information and health information and wearing the system.
The accuracy of the method is higher than that of the traditional arteriosclerosis assessment method based on the pulse wave, and the traditional arteriosclerosis assessment method extracts fewer pulse wave parameters and is lower in accuracy.
The method is innovative in that on the basis of evaluating arteriosclerosis by predecessors, arteriosclerosis is measured based on pulse wave measurement and integrated for measuring arteriosclerosis, data of any artery part of a human body is collected to form a feature set with more feature parameters by combining with physiological and health information of the human body, and the formed feature set is utilized to screen an important related feature subset meeting the current user, so that the arteriosclerosis degree is comprehensively evaluated, the consideration factors are comprehensive and strict, and the arteriosclerosis can be more accurately described.
The invention can detect according to any artery sampling point, has a plurality of alternative acquisition points, for example, the invention selects one of the alternative acquisition points from carotid artery, brachial artery, radial artery, earlobe, finger tip, femoral artery, peroneal artery, popliteal artery, posterior tibial artery, instep artery, toe tip and the like for acquisition, and has convenient use and high precision.
The method utilizes M groups of digital filters to filter the power spectrum, and obtains 3 × L cepstrum characteristics as a frequency domain characteristic parameter set, so that the method is more complete in obtaining frequency domain information;
the patent innovatively applies AS a ABI + b cfPWV + c baPWV AS the degree of arteriosclerosis, AS a standard output, for training of the model.
The fixing device is convenient to wear, does not need to be operated by professional personnel, can be worn correctly only according to the instruction of the instruction and inputs the related physiological information and health information, can evaluate the arteriosclerosis degrees of the main parts of the body, and has the advantages of higher evaluation precision, wider application range and less use restriction.
Additional features and advantages of the disclosure will be set forth in the description which follows, or in part may be learned by the practice of the above-described techniques of the disclosure, or may be learned by practice of the disclosure.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flowchart illustrating a non-invasive arteriosclerosis detecting method based on one-way pulse wave according to an embodiment of the present invention;
fig. 2 is a schematic diagram of signal acquisition according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a program module of a non-invasive arteriosclerosis detecting device based on one-way pulse wave according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. 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.
The traditional non-invasive arteriosclerosis detection method based on pulse wave can be distinguished according to detection parameters. The cervical femoral pulse wave velocity (cfPWV) is the golden standard for non-invasive evaluation of arteriosclerosis, the ankle pulse wave velocity (baPWV) can also detect arteriosclerosis within a certain range, and pulse wave parameter detection methods such as an Augmentation Index (AI) and a Diastolic Augmentation Index (DAI) are also provided, but the detection accuracy needs to be improved.
In order to solve the above problems, embodiments of the present invention provide a method and an apparatus for non-invasive arteriosclerosis detection based on one-way pulse waves, where the technique is applicable to wearable devices and terminal devices connected to a signal collector for detecting the degree of arteriosclerosis. The technique can be implemented by using corresponding hardware and software, and the embodiment of the invention is described in detail below.
The first embodiment is as follows:
the embodiment provides a noninvasive arteriosclerosis detection method, which can be executed by an arteriosclerosis detection device, wherein the arteriosclerosis detection device can be a portable device, in one embodiment, the arteriosclerosis detection device is a wearable device, in another embodiment, the arteriosclerosis detection device is a portable terminal comprising a signal collector and a processor, the signal collector and the processor can be integrally arranged or can be physically separated, and in practical application, the processor can also be a mobile phone, a tablet computer and the like, and both portable detection can be realized.
Referring to fig. 1, a flowchart of a method for detecting arteriosclerosis according to the present invention specifically includes the following steps:
s1: acquiring a pulse wave signal at one position in pulse wave signals of multiple parts of the body of a target object;
s2: preprocessing the acquired pulse wave signals;
s3-1: deriving the preprocessed original pulse wave signals to obtain first-order differential pulse wave signals and second-order differential pulse wave signals, then respectively carrying out normalization processing on the three signals, and carrying out feature point extraction on a time domain to further calculate to obtain a pulse wave time domain feature parameter set;
s3-2: after normalization processing is carried out on the basis of the preprocessed original pulse wave signals, feature point extraction is carried out in a frequency domain, and then a pulse wave frequency domain feature parameter set is obtained through calculation;
s3-3: comparing the normalized second-order differential pulse wave signal with the original pulse wave signal to calculate the pulse wave conduction time PWTT;
s3-4: introducing main physiological information and health information of a target object;
s4: establishing an arteriosclerosis model characteristic set;
s5: obtaining a characteristic subset through correlation analysis and screening;
s6: training by using a machine learning algorithm to obtain an arteriosclerosis model based on the characteristic subset;
s7: the feature subset based arteriosclerosis model is calibrated in combination with the physiological and health information of the target subject and arteriosclerosis detection values are output.
In step S1, the step of collecting pulse wave signals of main artery points of the whole body and other positions convenient for testing includes: collecting pulse wave signals by adopting various sensors; adopting a photoelectric volume type pulse wave sensor to obtain an arterial pulse wave signal of a tested person by measuring an optical signal of blood in an artery of the tested person; the pressure sensors are adopted, so that the pressure applied to each part of the target object to be tested is uniform and moderate, the accuracy of a test result is ensured, and the use experience of a user is improved; acquiring one of pulse wave signals of multiple parts of the body of a target subject, including main artery points and other positions convenient for testing, referring to a signal acquisition diagram shown in fig. 2, such as: carotid artery, brachial artery, radial artery, earlobe, distal finger, femoral artery, peroneal artery, popliteal artery, posterior tibial artery, dorsal foot artery, distal toe, etc.; the method for fixing the sensors comprises the following steps: the fixed mode of bandeau, wrist strap, sleeve area, finger clip, ear clip, gloves, oversleeve and wrist-watch or bracelet, embedded multiple sensor of fixing device, with surveyed person's skin in close contact with, guarantee measuring result's accuracy. The signal sampling rate of the signal collector can be not less than 1000 Hz.
In step S2, the step of preprocessing the acquired pulse wave signal includes: filtering the pulse wave signal by using a Butterworth band-pass filter to filter high-frequency noise; wavelet decomposition and reconstruction are carried out on the pulse wave signals by utilizing wavelet transformation, low-frequency noise is filtered, and baseline drift is removed.
In step S3-1, the step of performing feature point recognition and extraction on the pulse wave signal in the time domain includes: and performing singular point detection on the preprocessed and normalized original pulse wave signal, the first-order differential pulse wave signal and the second-order differential pulse wave signal by combining a first-order differential threshold algorithm, a second-order differential threshold algorithm and wavelet transformation so as to obtain the position of a time domain feature point. The characteristic points of the pulse wave time domain signals comprise the starting point, the wave crest and the wave trough of the three pulse wave signals after normalization and the wave crest and the wave trough of the dicrotic wave in the original pulse wave signals.
In step S3-1, the step of obtaining the set of pulse wave time domain feature parameters by calculating from the pulse wave time domain feature points includes:
1) time parameters: pulse cycle time T, main wave rising time T1, time from the starting point to the trough of the dicrotic wave T2, time from the trough of the dicrotic wave to the ending point T3, time from the main wave peak at the same height of the trough of the dicrotic wave T4, blood vessel hardness index T4/T, heart beat rate coefficient T2/T3, myocardial contraction coefficient T1/T, heart beat output coefficient (T2-T1)/T, time difference between the first order difference signal and the second order difference signal starting point, time interval from the trough of the first order difference signal to the ending point, and time interval from the trough of the second order difference signal to the ending point;
2) slope parameter: ascending branch slope AS and descending branch slope DS;
3) amplitude parameter: the amplitude difference AID of the ascending branch, the amplitude difference DID of the descending branch, the amplitude H1 from the main wave crest to the starting point, the amplitude H2 from the repeating wave to the starting point, the peripheral resistance coefficient H2/H1, the wave valley amplitude of the repeating wave, the ratio of the amplitude difference between the wave crest and the repeating wave to the amplitude difference between the wave crest and the wave valley, the ratio of the amplitude of the wave crest to the amplitude of the starting point and the ratio of the amplitude of the wave crest to the amplitude of the wave valley;
4) area parameters: ascending branch area AA, descending branch area DA, and ascending branch to descending branch area ratio;
5) other parameters: heart rate, blood oxygen, growth index AI, diastolic growth index DAI.
In step S3-2, the step of obtaining the pulse wave frequency domain feature parameter set includes:
1) normalizing the preprocessed original pulse wave signals, framing, pre-emphasizing and windowing to obtain time domain pulse wave signal segments x, and performing FFT (fast Fourier transform) on the time domain pulse wave signal segments x;
2) in the relevant frequency range of pulse wave (such as 0.5Hz-5Hz), M groups of digital filters are introduced, and the frequency response of each filter is Hm(k) Filtering the power spectrum, and then carrying out logarithm operation to obtain a logarithm spectrum transfer function SmFinally by discrete cosine transformObtaining pulse wave cepstrum coefficient C (l) by conversion (DCT);
3) acquiring dynamic cepstrum coefficients on the basis of the static pulse wave cepstrum coefficients, wherein the dynamic cepstrum coefficients are first-order differential pulse wave cepstrum coefficients delta C (l) and second-order differential pulse wave cepstrum coefficients delta C (l);
4) to this end, 3 × L cepstral coefficients are obtained from c (L), Δ Δ c (L), and constitute the pulse wave frequency domain feature parameter set.
In step S3-4, the step of introducing the main physiological information and the health information of the target object includes: gender, age, height, weight, BMI, smoking status, chronic history (heart disease, hypertension, diabetes, dyslipidemia, etc.).
In step S5, the step of obtaining the reference arteriosclerosis degree includes:
1) clinical procedure derived ankle index (ABI)
2) Cervical femoral pulse wave velocity (cfPWV) obtained by clinical method
3) Ankle pulse wave velocity (baPWV) obtained by clinical method
4) With reference to the degree of arteriosclerosis AS, then:
AS＝a*ABI+b*cfPWV+c*baPWV
wherein a, b and c are weight coefficients.
In step S5, the step of performing correlation analysis on the feature set of the arteriosclerosis model and the reference arteriosclerosis degree, and screening to obtain the feature subset includes: and (3) calculating mutual information correlation degree of each characteristic parameter in the arteriosclerosis model characteristic set and the reference arteriosclerosis degree by using a mutual information theory, and sequencing, wherein a mutual correlation formula is as follows:
according to a minimum redundancy maximum correlation (mRMR) criterion, maximizing the correlation of the characteristic parameters and the reference arteriosclerosis degree, and minimizing the correlation between the characteristic parameters; and screening to obtain a feature subset.
In step S6, the step of obtaining the arteriosclerosis model based on the feature subset by training using the machine learning algorithm further includes:
training to obtain an arteriosclerosis model AS based on a machine learning algorithm1(ii) a Training to obtain an arteriosclerosis model AS based on a neural network model2(ii) a The arteriosclerosis model is: AS ═ d × AS1+e*AS2Wherein d and e are weight coefficients.
Example two:
for the non-invasive arteriosclerosis detecting method provided by the first embodiment, the embodiment of the present invention further provides a non-invasive arteriosclerosis detecting apparatus, referring to a schematic structural diagram of an arteriosclerosis detecting apparatus shown in fig. 3, including the following modules: the signal acquisition module acquires single pulse wave signals of main artery points of the whole body or other positions where pulse waves are convenient to test; the preprocessing module is used for filtering high-frequency noise and low-frequency noise of the pulse wave signals; the time domain feature extraction module is used for calculating to obtain a time domain feature parameter set; the frequency domain characteristic extraction module is used for calculating to obtain a frequency domain characteristic parameter set; the PWTT extracting module is used for calculating to obtain PWTT; the information input module is used for inputting physiological information and health information of the target object; the characteristic screening module is used for carrying out correlation analysis on the characteristic set of the arteriosclerosis model and screening to obtain a characteristic subset; and the arteriosclerosis detection module is loaded with an arteriosclerosis model which is trained in advance and based on the characteristic subset, and outputs an arteriosclerosis detection result by taking the arteriosclerosis model characteristic subset as input.
The signal acquisition module comprises at least one fixing device, and each fixing device is integrated with at least one photoelectric volume type pulse wave sensor (such as MAX30102) and one pressure sensor (such as SBT674 miniature pressure sensor); the fixing device is a head band, a wrist band, a sleeve band, a finger clip, an ear clip, a glove, a sleeve and a watch or a hand ring. One fixture may be a signal collector.
The device provided by the embodiment has the same implementation principle and technical effect as the foregoing embodiment, and for the sake of brief description, reference may be made to the corresponding contents in the foregoing method embodiment for the portion of the embodiment of the device that is not mentioned.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Nothing in this specification is said to apply to the prior art.
Claims (7)
1. A non-invasive arteriosclerosis detection method based on one-way pulse waves is characterized by comprising the following steps:
taking main artery points of the whole body and other position points which are convenient for collecting pulse waves as alternative collecting points, recording pulse wave signals obtained by each collecting point as original pulse wave signals, preprocessing the original pulse wave signals to obtain preprocessed original pulse wave signals A, and then obtaining first-order difference pulse wave signals B and second-order difference pulse wave signals C of the preprocessed original pulse wave signals;
performing normalization processing based on the preprocessed original pulse wave signal A, the first-order difference pulse wave signal B and the second-order difference pulse wave signal C;
then, respectively extracting feature points of the three signals subjected to normalization processing in a time domain to obtain main feature points, and further calculating to obtain a pulse wave time domain feature parameter set;
extracting feature points in a frequency domain based on the normalized original pulse wave signals to obtain a pulse wave frequency domain feature parameter set;
extracting characteristic points of the normalized second-order difference pulse wave signals, comparing the characteristic points with main characteristic points of the normalized original pulse wave signals, and calculating to obtain pulse wave conduction time PWTT;
introducing main physiological information and health information of a target object, and combining pulse wave time domain characteristic parameter sets, pulse wave frequency domain characteristic sets and PWTT of all sampling points to establish an arteriosclerosis model characteristic set;
performing correlation analysis on the feature set of the arteriosclerosis model and the reference arteriosclerosis degree, and screening to obtain a feature subset corresponding to the current target object;
taking the characteristic subset as input, taking the arteriosclerosis degree as output, and training by applying a machine learning algorithm to obtain an arteriosclerosis model based on the characteristic subset;
inputting physiological information and health information of a to-be-detected object, collecting pulse wave signals at any position, processing the signals, inputting the signals into a trained arteriosclerosis model based on a feature subset, namely completing noninvasive arteriosclerosis detection, and outputting a detection result.
2. The detection method according to claim 1, wherein the step of collecting pulse wave signals using main artery points of the whole body and other points at which pulse waves are conveniently collected as alternative collection points comprises
1) Collecting pulse wave signals by adopting various sensors; adopting a photoelectric volume type pulse wave sensor to obtain an arterial pulse wave signal of a tested person by measuring an optical signal of blood in an artery of the tested person; the pressure sensors are adopted to ensure that the pressure applied to each part of the target object to be detected is uniform and moderate;
2) collecting pulse wave signals of multiple parts of the body of a target object, wherein the pulse wave signals comprise main artery points and other positions which are convenient for collecting pulse waves, namely the positions of carotid artery, brachial artery, radial artery, earlobe, finger tail end, femoral artery, peroneal artery, popliteal artery, posterior tibial artery, instep artery and toe tail end;
3) the various sensors are integrated in a fixing device, at least one photoelectric volume type pulse wave sensor and one pressure sensor are arranged, and the fixing device is realized in the form of a headband, a wrist band, a cuff, a finger clip, an ear clip, a glove, a sleeve and a watch or a wrist band.
3. The detecting method according to claim 1, wherein the step of obtaining the pulse wave frequency domain feature parameter set comprises:
1) normalizing and framing, pre-emphasizing and windowing the preprocessed pulse wave signals to obtain time-domain pulse wave signal segments x (N), wherein N is 0, … and N-1, and the FFT conversion formula is as follows:
wherein k is 0, …, N-1 represents the frequency point corresponding to FFT operation, and N refers to time domain signal;
2) in the relevant frequency band of pulse wave, M groups of digital filters are introduced, and the frequency response of each filter is Hm(k) Filtering the power spectrum, and then carrying out logarithm operation to obtain a logarithm spectrum transfer function SmFinally, obtaining pulse wave cepstrum coefficient C (l) through Discrete Cosine Transform (DCT);
wherein L is 1, …, L represents the order of the pulse wave cepstrum coefficient, and the highest order of the L pulse wave cepstrum coefficient;
3) acquiring dynamic cepstrum coefficients on the basis of the static pulse wave cepstrum coefficients, wherein the dynamic cepstrum coefficients are first-order differential pulse wave cepstrum coefficients delta C (l) and second-order differential pulse wave cepstrum coefficients delta C (l);
4) to this end, 3 × L cepstral coefficients are obtained from c (L), Δ Δ c (L), and constitute the pulse wave frequency domain feature parameter set.
4. The method according to claim 1, wherein the step of obtaining the reference arteriosclerosis degree comprises:
1) clinical procedure derived ankle index (ABI)
2) Cervical femoral pulse wave velocity (cfPWV) obtained by clinical method
3) Ankle pulse wave velocity (baPWV) obtained by clinical method
4) With reference to the degree of arteriosclerosis AS, then:
AS＝a*ABI+b*cfPWV+c*baPWV
wherein a, b and c are weight coefficients.
5. The method of claim 1, wherein the step of training the neural network model using a machine learning algorithm to obtain the feature subset based arteriosclerosis model comprises:
1) training to obtain an arteriosclerosis model AS based on a machine learning algorithm1；
2) Training to obtain an arteriosclerosis model AS based on a neural network model2；
3) The arteriosclerosis model is:
AS＝d*AS1+e*AS2
wherein d and e are weight coefficients.
6. A non-invasive arteriosclerosis detecting device based on one-way pulse wave, comprising:
the signal acquisition module acquires single pulse wave signals of main artery points of the whole body or other positions where pulse waves are convenient to test;
the preprocessing module is used for filtering high-frequency noise and low-frequency noise of the pulse wave signals;
the time domain feature extraction module is used for calculating to obtain a time domain feature parameter set;
the frequency domain characteristic extraction module is used for calculating to obtain a frequency domain characteristic parameter set;
the PWTT extracting module is used for calculating to obtain PWTT;
the information input module is used for inputting physiological information and health information of the target object;
the characteristic screening module is used for carrying out correlation analysis on the characteristic set of the arteriosclerosis model and screening to obtain a characteristic subset;
and the arteriosclerosis detection module is used for outputting an arteriosclerosis detection result by taking the arteriosclerosis model characteristic subset as input based on the arteriosclerosis model based on the characteristic subset trained in advance.
7. The device according to claim 6, wherein the signal acquisition module comprises at least one fixture, each fixture having at least one photoplethysmographic pulse wave sensor and one pressure sensor integrated therein; the fixing device is a head band, a wrist band, a sleeve band, a finger clip, an ear clip, a glove, a sleeve and a watch or a hand ring.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011170907.4A CN112274120B (en) | 2020-10-28 | 2020-10-28 | Noninvasive arteriosclerosis detection method and device based on one-way pulse wave |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011170907.4A CN112274120B (en) | 2020-10-28 | 2020-10-28 | Noninvasive arteriosclerosis detection method and device based on one-way pulse wave |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112274120A true CN112274120A (en) | 2021-01-29 |
| CN112274120B CN112274120B (en) | 2023-07-25 |
Family
ID=74374004
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011170907.4A Active CN112274120B (en) | 2020-10-28 | 2020-10-28 | Noninvasive arteriosclerosis detection method and device based on one-way pulse wave |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112274120B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112869717A (en) * | 2021-02-25 | 2021-06-01 | 佛山科学技术学院 | Pulse feature recognition and classification system and method based on BL-CNN |
| CN114027810A (en) * | 2021-03-31 | 2022-02-11 | 北京超思电子技术有限责任公司 | Method for generating blood pressure calculation model containing arteriosclerosis classification and blood pressure measuring system |
| CN114052688A (en) * | 2021-12-07 | 2022-02-18 | 山东大学 | Blood pressure monitoring device based on one-way pulse wave, storage medium and electronic equipment |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009066688A1 (en) * | 2007-11-20 | 2009-05-28 | The Doshisha | Arteriosclerosis diagnostic device |
| KR100954817B1 (en) * | 2009-03-13 | 2010-04-28 | (주)바이오센스크리에티브 | System and method for testing blood vessel's health and stress through signal analysis of pluse wave |
| JP2015080545A (en) * | 2013-10-22 | 2015-04-27 | 国立大学法人東北大学 | Evaluation of arteriosclerotic level by aortic bloodstream waveform analysis |
| CN104873186A (en) * | 2015-04-17 | 2015-09-02 | 中国科学院苏州生物医学工程技术研究所 | Wearable artery detection device and data processing method thereof |
| CN106691406A (en) * | 2017-01-05 | 2017-05-24 | 大连理工大学 | Detection method of vascular elasticity and blood pressure based on single probe photoplethysmography pulse wave |
| KR101798495B1 (en) * | 2017-03-31 | 2017-11-16 | 주식회사 엠프로스 | Wearable wristwatch-type blood pressure monitor |
| CN107582037A (en) * | 2017-09-30 | 2018-01-16 | 深圳前海全民健康科技有限公司 | Method based on pulse wave design medical product |
| CN107595249A (en) * | 2017-09-30 | 2018-01-19 | 深圳前海全民健康科技有限公司 | pregnant female screening method based on pulse wave |
| 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 |
| WO2018104970A1 (en) * | 2016-12-09 | 2018-06-14 | Indian Institute Of Technology Bombay | Pulse detection, measurement and analysis based health management system, method and apparatus |
| CN108175387A (en) * | 2017-12-19 | 2018-06-19 | 桂林电子科技大学 | A kind of peripheral vascular resistance detection device and detection method based on electrocardio and pulse wave Morphologic Parameters |
| CN109793507A (en) * | 2019-03-06 | 2019-05-24 | 桂林电子科技大学 | It is a kind of based on finger pressure oscillographic method without oversleeve blood pressure measuring device and measurement method |
| CN110141196A (en) * | 2019-06-13 | 2019-08-20 | 东北大学 | Peripheral arterial blood vessel elasticity evaluation method and system based on double triangle flow models |
| CN111065323A (en) * | 2017-07-21 | 2020-04-24 | 皇家飞利浦有限公司 | Device and method for determining the blood pressure of a subject |
-
2020
- 2020-10-28 CN CN202011170907.4A patent/CN112274120B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009066688A1 (en) * | 2007-11-20 | 2009-05-28 | The Doshisha | Arteriosclerosis diagnostic device |
| KR100954817B1 (en) * | 2009-03-13 | 2010-04-28 | (주)바이오센스크리에티브 | System and method for testing blood vessel's health and stress through signal analysis of pluse wave |
| JP2015080545A (en) * | 2013-10-22 | 2015-04-27 | 国立大学法人東北大学 | Evaluation of arteriosclerotic level by aortic bloodstream waveform analysis |
| CN104873186A (en) * | 2015-04-17 | 2015-09-02 | 中国科学院苏州生物医学工程技术研究所 | Wearable artery detection device and data processing method thereof |
| WO2018104970A1 (en) * | 2016-12-09 | 2018-06-14 | Indian Institute Of Technology Bombay | Pulse detection, measurement and analysis based health management system, method and apparatus |
| CN106691406A (en) * | 2017-01-05 | 2017-05-24 | 大连理工大学 | Detection method of vascular elasticity and blood pressure based on single probe photoplethysmography pulse wave |
| KR101798495B1 (en) * | 2017-03-31 | 2017-11-16 | 주식회사 엠프로스 | Wearable wristwatch-type blood pressure monitor |
| CN111065323A (en) * | 2017-07-21 | 2020-04-24 | 皇家飞利浦有限公司 | Device and method for determining the blood pressure of a subject |
| CN107582037A (en) * | 2017-09-30 | 2018-01-16 | 深圳前海全民健康科技有限公司 | Method based on pulse wave design medical product |
| CN107595249A (en) * | 2017-09-30 | 2018-01-19 | 深圳前海全民健康科技有限公司 | pregnant female screening method based on pulse wave |
| CN108175387A (en) * | 2017-12-19 | 2018-06-19 | 桂林电子科技大学 | A kind of peripheral vascular resistance detection device and detection method based on electrocardio and pulse wave Morphologic Parameters |
| 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 |
| CN109793507A (en) * | 2019-03-06 | 2019-05-24 | 桂林电子科技大学 | It is a kind of based on finger pressure oscillographic method without oversleeve blood pressure measuring device and measurement method |
| CN110141196A (en) * | 2019-06-13 | 2019-08-20 | 东北大学 | Peripheral arterial blood vessel elasticity evaluation method and system based on double triangle flow models |
Non-Patent Citations (5)
| Title |
|---|
| MASASHI SAITO; MAMI MATSUKAWA; TAKAAKI ASADA; YOSHIAKI WATANABE: "Noninvasive assessment of arterial stiffness by pulse wave analysis", 《 IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL》 * |
| MASASHI SAITO; MAMI MATSUKAWA; TAKAAKI ASADA; YOSHIAKI WATANABE: "Noninvasive assessment of arterial stiffness by pulse wave analysis", 《 IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL》, vol. 59, no. 11, 10 November 2012 (2012-11-10), XP011471075, DOI: 10.1109/TUFFC.2012.2473 * |
| 张丽娜; 周润景; 武佩; 刘美玲; 张珏: "基于心电、脉搏波信号的动脉硬化无创检测", 《生物医学工程学杂志》 * |
| 郑莹莹: "基于桡动脉波形分析的动脉硬化评估指标研究", 《中国博士学位论文全文数据库医药卫生科技辑》 * |
| 郑莹莹: "基于桡动脉波形分析的动脉硬化评估指标研究", 《中国博士学位论文全文数据库医药卫生科技辑》, 15 November 2011 (2011-11-15) * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112869717A (en) * | 2021-02-25 | 2021-06-01 | 佛山科学技术学院 | Pulse feature recognition and classification system and method based on BL-CNN |
| CN112869717B (en) * | 2021-02-25 | 2023-02-24 | 佛山科学技术学院 | Pulse feature recognition and classification system and method based on BL-CNN |
| CN114027810A (en) * | 2021-03-31 | 2022-02-11 | 北京超思电子技术有限责任公司 | Method for generating blood pressure calculation model containing arteriosclerosis classification and blood pressure measuring system |
| CN114027810B (en) * | 2021-03-31 | 2024-03-26 | 北京超思电子技术有限责任公司 | Blood pressure calculation model generation method containing arteriosclerosis classification and blood pressure measurement system |
| CN114052688A (en) * | 2021-12-07 | 2022-02-18 | 山东大学 | Blood pressure monitoring device based on one-way pulse wave, storage medium and electronic equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112274120B (en) | 2023-07-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112274126B (en) | Noninvasive continuous blood pressure detection method and device based on multiple pulse waves | |
| Ibrahim et al. | Cuffless blood pressure monitoring from an array of wrist bio-impedance sensors using subject-specific regression models: Proof of concept | |
| CN112274127B (en) | Noninvasive continuous blood pressure detection method and device based on one-way pulse wave | |
| US11844593B2 (en) | System and method for non-invasive determination of blood pressure dip based on trained prediction models | |
| Miao et al. | Multi-sensor fusion approach for cuff-less blood pressure measurement | |
| Rastegar et al. | Non-invasive continuous blood pressure monitoring systems: current and proposed technology issues and challenges | |
| Forouzanfar et al. | Oscillometric blood pressure estimation: past, present, and future | |
| CN108185996B (en) | Arterial blood vessel age estimation model construction method and device | |
| CN112274121B (en) | Noninvasive arteriosclerosis detection method and device based on multipath pulse waves | |
| CN112274120B (en) | Noninvasive arteriosclerosis detection method and device based on one-way pulse wave | |
| CN112040846A (en) | Method for estimating blood pressure and degree of arteriosclerosis based on photoplethysmography (PPG) signal | |
| Natarajan et al. | Photoplethysmography fast upstroke time intervals can be useful features for cuff-less measurement of blood pressure changes in humans | |
| CN109069031A (en) | For determining the device and method of object blood pressure | |
| WO2018201395A1 (en) | Apparatus and method for determining a blood pressure of a subject | |
| Zhou et al. | The noninvasive blood pressure measurement based on facial images processing | |
| Landry et al. | Accurate blood pressure estimation during activities of daily living: A wearable cuffless solution | |
| Jang et al. | A knowledge-based approach to arterial stiffness estimation using the digital volume pulse | |
| CN112089405A (en) | Pulse wave characteristic parameter measuring and displaying device | |
| CN115251866A (en) | Continuous blood pressure detection method and system adopting millimeter wave radar and wearable device | |
| Yan et al. | A non-invasive blood pressure prediction method based on pulse wave feature fusion | |
| Hsiao et al. | Design and implementation of auscultation blood pressure measurement using vascular transit time and physiological parameters | |
| He et al. | Continuous tracking of changes in systolic blood pressure using BCG and ECG | |
| CN113171061B (en) | Noninvasive vascular function assessment method, noninvasive vascular function assessment device, noninvasive vascular function assessment equipment and noninvasive vascular function assessment medium | |
| CN114343596A (en) | Continuous blood pressure measuring system and method based on reverse blood flow propagation time | |
| Kadarmandalgi et al. | Pulse diagnosis System for Nadi Pariksha using parametric and statistical analysis |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |