CN114587307A - Non-contact blood pressure detector and method based on capacitive coupling electrode - Google Patents
Non-contact blood pressure detector and method based on capacitive coupling electrode Download PDFInfo
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Abstract
The invention discloses a non-contact blood pressure detector and a method based on a capacitive coupling electrode, wherein the capacitive coupling exciting electrode and the capacitive coupling measuring electrode are attached to the outer side of clothes on the arm of a human body to carry out impedance volume characteristic detection; the capacitive coupling measuring electrode and the capacitive coupling reference electrode are attached to the outer side of the clothes at the chest part of the human body to detect electrocardiogram signals; the electrocardio and impedance volume signal measuring module is used for synchronously acquiring a capacitive coupling impedance volume rate (CCIPG) signal and a Capacitive Coupling Electrocardiogram (CCECG) signal; the data acquisition module is used for converting the received chest lead CCECG and arm CCIPG signals into digital signals and transmitting the digital signals to the controller, so that the controller obtains a capacitive coupling electrocardiogram waveform and an impedance volume ratio oscillogram of a measurer; the feature extraction module is used for acquiring Pulse Wave Transit Time (PWTT); the impedance volume characteristic parameter calculation module is used for acquiring human body hemodynamic parameters; the PTT blood pressure calculation module is used for calculating a blood pressure value 1; the PTT fusion impedance volume parameter machine learning module is used for performing learning training by taking human body hemodynamic parameters and a blood pressure value 1 as sample data, and establishing a final blood pressure model; the blood pressure prediction module is used for predicting the blood pressure value of a human body, so that the real-time monitoring of the blood pressure and the analysis of the cardiovascular health state can be realized, the electrode does not need to be directly attached to the skin, the discomfort of a measurer is avoided, and the blood pressure prediction module can be widely used in the fields of human body health monitoring and the like.
Description
Technical Field
The invention relates to a non-contact blood pressure detector and a non-contact blood pressure detection method based on a capacitive coupling electrode, which can be used for real-time monitoring and measurement of human blood pressure and belong to the technical field of medical instruments.
Background
Blood pressure is a common and important indicator in biomedical measurements. Factors such as the pumping function of the heart, the blood supply condition of coronary arteries, the resistance and elasticity of peripheral blood vessels, the systemic blood volume and the physical state of blood are reflected in the parameter indexes of blood pressure, and the blood pressure can be used as an indicator of the state of the cardiovascular system. Therefore, the measurement of blood pressure parameters provides an extremely important basis in clinical examination, patient care, or in physiological research work.
The existing blood pressure measuring technology is mainly divided into two modes of invasive blood pressure measurement and non-invasive blood pressure measurement, wherein the invasive blood pressure measurement is to insert a catheter into a blood vessel or a heart of a part to be measured and place a pressure sensor at the tail end of the catheter, and the pressure generated by the blood flowing and impacting of the part to be measured is transmitted to the end part of the catheter and sensed by the pressure sensor, so that the blood pressure of a patient is directly measured; the non-invasive blood pressure measuring method is to utilize the relationship between the pressure in the blood vessel and the blood flow change at the opening moment of blood blocking to measure the corresponding blood pressure value from the body surface, the currently used widely Korotkoff sound method and oscillometric method are to use the cuff to inflate and pressurize and block the artery, then slowly deflate, detect the pulse change or the blood flow change under the cuff or at the far end of the artery, calculate the systolic pressure and the diastolic pressure by the blood pressure algorithm, although the defect of infection to the patient caused by the invasion can be avoided, it can only measure the systolic pressure and the diastolic pressure of the patient in the period of time, and can not continuously record the blood pressure waveform for a long time.
In order to overcome the defects that invasive measurement is inconvenient, infection risks exist and blood pressure cannot be continuously monitored for a long time in a non-invasive mode, in recent years, a plurality of foreign research institutes use a photoplethysmography (PPG) to detect pulse wave waveform characteristic changes and an AgCl wet electrode Electrocardiosignal (ECG) mode to obtain the conduction time (PTT) of pulse waves, so that continuous monitoring of the blood pressure is achieved. However, the PPG technique has a high demand on a measurer, and meanwhile, an AgCl wet electrode generally needs to be used together with a conductive adhesive in order to reduce contact impedance in the use process, so that the PPG electrode has the disadvantages of long preparation time, stimulation of human skin, easy falling off in the long-time monitoring process, and the like.
In order to solve the problems, the invention discloses a non-contact blood pressure detector based on a capacitive coupling electrode and a method thereof. The method is characterized in that a coupling electrode is designed on the basis of a capacitive coupling principle, a bioelectric signal on the surface of a human body is completely coupled to a detection end on the premise of not directly contacting the skin, a Coupled Chest Electrocardiosignal (CCECG) and a coupled arm impedance volume rate signal (CCIPG) are further detected, pulse wave conduction time is calculated, and a non-contact continuous blood pressure prediction model is constructed by combining characteristic parameters of an impedance blood flow graph, so that the accuracy of a blood pressure measurement value is improved.
Disclosure of Invention
The invention aims to provide a non-contact blood pressure detector and a method based on a capacitive coupling electrode, which mainly take the detection of non-contact electrocardiosignals and impedance volume ratio signals as a core, expand the research of non-invasive blood pressure detection, avoid the direct contact of electrode plates with skin, improve the accuracy of blood pressure measurement and establish and optimize a model for non-contact continuous blood pressure prediction.
In order to realize the problems, the invention provides a non-contact blood pressure detector based on a capacitive coupling electrode and a method thereof, and the method comprises a capacitive coupling excitation electrode 1, a capacitive coupling measuring electrode 2, a capacitive coupling reference electrode 3, an electrocardio and impedance volume signal measuring module 4, a data acquisition module 5, a feature extraction module 6, an impedance volume feature parameter calculating module 7, a PTT blood pressure calculating module 8, a machine learning module 9 of PTT fusion impedance volume parameters and a blood pressure predicting module 10.
The capacitive coupling excitation electrode is used for being matched with the capacitive coupling measuring electrode and attached to the outer side of clothes of a human arm to detect the impedance volume characteristic.
The other capacitive coupling measuring electrode is used for matching with the capacitive coupling reference electrode and attaching to the outer side of clothes of the chest and heart of a human body to detect electrocardiogram signals.
The electrocardio and impedance volume signal measuring module is used for synchronously acquiring a capacitance coupling impedance volume ratio (CCIPG) signal and a Capacitance Coupling Electrocardiogram (CCECG) signal.
The data acquisition module is used for converting the received chest lead CCECG and arm CCIPG signals into digital signals and transmitting the digital signals to the controller, so that the controller obtains a capacitive coupling electrocardiogram waveform and an impedance volume ratio waveform chart of a measurer.
The feature extraction module is used for obtaining Pulse Wave Transit Time (PWTT).
And the impedance volume characteristic parameter calculation module is used for acquiring human body hemodynamic parameters.
And the PTT blood pressure calculation module is used for calculating a blood pressure value 1.
The machine learning module of the PTT fusion impedance volume parameter is used for performing learning training by taking the morphological characteristic parameter of the physiological characteristics of the cardiovascular system reflected on the impedance volume rate oscillogram and the blood pressure value 1 as sample data, and establishing a final blood pressure model.
The blood pressure prediction module is used for predicting the blood pressure value of the human body.
Furthermore, the coupling electrode is a four-layer rectangular PCB, and two layers in the middle of the coupling electrode are both set as shielding layers, so that the function of isolating space noise is realized.
Furthermore, the electrocardio and impedance volume signal measuring module comprises a coupled electrocardio measuring unit and a coupled impedance volume ratio measuring unit.
The coupling electrocardio measuring unit comprises a differential amplifying circuit, a 50Hz trap circuit and a band-pass filter circuit, wherein the input end of the differential amplifying circuit is connected with an electrocardio coupling measuring electrode and is used for carrying out unit gain difference on a capacitive coupling electrocardiosignal acquired by the coupling measuring electrode and an electrocardiosignal of a reference electrode and simultaneously leading out common mode interference of the electrocardio coupling electrode, and the 50Hz trap circuit and the band-pass filter circuit are used for filtering the differentially amplified signals to obtain chest lead electrocardiosignals.
The coupled impedance volume ratio measuring unit comprises a main control circuit, a constant current source circuit, a differential amplifying circuit, a filter circuit, a full-wave rectifying circuit and a differentiating circuit, wherein the main control circuit controls the constant current source circuit to output 50KHz and 0.3mA currents, the impedance of the arm of a human body is measured by adopting a four-electrode method, the input end of the differential amplifying circuit is connected with a coupled measuring electrode and is used for carrying out differential amplification on a capacitance coupled impedance volume signal acquired by the coupled measuring electrode, and the processed signal enters the full-wave rectifying and differentiating circuit through the filter circuit to obtain an arm impedance volume ratio signal.
The data acquisition module mainly comprises an A/D circuit and is used for converting the received chest lead electrocardiosignals and arm impedance volume rate signals into digital signals and transmitting the digital signals to the controller, so that the controller obtains a capacitive coupling electrocardiogram waveform and an impedance volume rate oscillogram of a measurer.
Wherein, the morphological characteristic parameters of the physiological characteristics of the cardiovascular system reflected on the human body CCIPG signal oscillogram comprise: ratio of BC segment time to single cycle time (T)RBC) Ratio of CX period time to single period time (T)RCX) The ratio of the ejection time to the monocycle (T)R) Slow and slowTime of slow ejection period (T)CX) Ejection time (T)BX) And stroke cardiac output (S)BX)。
The non-contact blood pressure detection method comprises the following specific steps:
step one, positioning an effective peak point of an R wave of an acquired electrocardiogram waveform to obtain an effective peak position of the R wave of the electrocardiogram waveform; and positioning the rising branch end point of the impedance volume rate signal oscillogram to obtain the rising branch end point position of the impedance volume rate signal oscillogram.
And after the jth R-wave effective peak point appears on the electrocardiogram waveform, determining the jth R-wave effective peak point position on the electrocardiogram waveform, and determining the rising branch end point position of the impedance volume ratio signal waveform diagram corresponding to the jth R-wave effective peak point on the electrocardiogram waveform. Calculating the heart rate HR of the jth measurerj，HRj＝1/TjWherein T isjCalculating the time difference between the j-th R wave effective peak point on the electrocardiogram waveform and the j + 1-th R wave effective peak point on the electrocardiogram waveform to obtain the j-th pulse wave propagation time PWTT of the measurerj。
Step two, according to the jth heart rate and the jth pulse wave conduction time PWTTjCalculating the jth diastolic pressure DBPjAnd the jth systolic pressure SBPj。
And step three, introducing morphological characteristic parameters of the impedance volume rate signal oscillogram into an index data set formed by the PTT blood pressure calculation module.
And step four, establishing a nonlinear model of the blood pressure by adopting a machine learning mode based on an extreme random forest model.
Further, the jth diastolic pressure DBPjThe calculation expression of (a) is:
DBPj＝a*ln PWTTj+b*HRj+c*TRCX+d*TR+e*TCX+f*SBX+g
further, the jth systolic pressure SBPjThe calculation expression of (a) is:
SBPj＝α*ln PWTTj+β*TRBC+δ*TRCX+ε*TR+γ
a. and matching and taking values of the parameter values b, c, d, e, f, g, alpha, beta, delta, epsilon, gamma and the like through the measurer to obtain electrocardio-impedance volume rate data sets of m groups of the measurer, wherein m is more than or equal to 2, and the systolic pressure and the diastolic pressure are measured by synchronously using a sphygmomanometer. The ECG impedance-volume rate data set includes the heart rate, the pulse wave transit time and the morphological parameters of the impedance-volume rate oscillogram. And substituting the m groups of electrocardio-impedance volume rate data sets and the corresponding systolic pressure and diastolic pressure into training to obtain the matched model parameter values.
Further, the jth pulse wave transit time is PWTTjThe time difference between the effective peak point of the jth wave on the electrocardiogram waveform and the end point of the rising branch of the corresponding impedance volume ratio waveform diagram. The method for dividing the effective peak point of the R wave on the electrocardiogram waveform and the end point of the ascending branch of the impedance volume rate waveform diagram is as follows: if the time of the last R-wave effective peak point of the electrocardiographic waveform is earlier than the time of one rising end point of the impedance volume ratio waveform diagram, and there is no other R-wave effective peak point of the electrocardiographic waveform and no other rising end point of the impedance volume ratio waveform diagram between the two, the two are divided into a corresponding group.
Further, the method for locating the characteristic peak point of the electrocardiogram waveform or the impedance volume rate waveform specifically comprises the following steps:
(1) filtering and denoising the electrocardiogram waveform or the impedance volume rate waveform by a wavelet threshold filtering method.
(2) And (3) carrying out derivation on the signal curve processed in the step (1) to obtain a derivative function curve. And calculating a zero point of the derivative function curve, thereby determining the positions of the maximum value point and the minimum value point of the obtained curve.
(3) Screening the extreme points determined in (2) for values greater than an amplitude threshold XSAs a predetermined peak value, XS＝3/4*(XSMAX-XSMIN)+XSMINWherein X isSMAXRepresents the value of maximum amplitude of data in (1), XSMINRepresents the value at which the amplitude of the data in (1) is the smallest.
(4) The n predetermined peaks are divided into i groups of determined peaks. And the time position difference between any two adjacent preset peak values among the i determined peak value groups is more than 0.3-0.5 s.
(5) And selecting a position point with the maximum amplitude from the i determined peak value groups to obtain i effective peak value points.
Further, in the third step, the method for establishing the nonlinear blood pressure model based on the machine learning mode of the extreme random forest model is detailed as follows:
firstly, introducing morphological characteristic parameters of an impedance volume rate signal oscillogram into an index data set consisting of a PTT blood pressure calculation module;
selecting 6 groups of sample data from the original training set to generate 6 training sets;
thirdly, respectively training 6 decision tree models for the 6 training sets;
fourthly, selecting the best characteristic for splitting aiming at a single decision tree model, and repeating the operation for multiple times;
fifthly, forming a random forest by the generated 6 decision trees;
and sixthly, obtaining a predicted value of the blood pressure, and obtaining a result of a verification set according to the cross validation of the test set, thereby evaluating the correctness of the result of the regression model.
The invention has the beneficial effects that:
1. the invention is based on the non-contact type detection of human blood pressure of the capacitive coupling electrode, can avoid the electrode slice from directly contacting the skin, and solves the problems of unstable long-term monitoring and skin irritation caused by the electrode.
2. On the basis of the PTT blood pressure calculation model, the morphological characteristic parameters of the impedance volume oscillogram are introduced, so that the non-contact continuous blood pressure prediction model is further optimized.
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, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic structural diagram of a non-contact blood pressure monitor and method based on capacitive coupling electrodes;
FIG. 2 is a block diagram of a hardware circuit system of a non-contact blood pressure monitor and method based on capacitive coupling electrodes;
FIG. 3 is a flow chart of the present invention for predicting blood pressure values using an extreme random forest approach.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments.
As shown in fig. 1, a non-contact blood pressure monitor and method based on capacitive coupling electrode includes a capacitive coupling excitation electrode 1, a capacitive coupling measurement electrode 2, a capacitive coupling reference electrode 3, an electrocardiogram and impedance volume signal measurement module 4, a data acquisition module 5, a feature extraction module 6, an impedance volume feature parameter calculation module 7, a PTT blood pressure calculation module 8, a PTT fusion impedance volume parameter machine learning module 9, and a blood pressure prediction module 10.
The capacitive coupling excitation electrode 1, the capacitive coupling measurement electrode 2 and the capacitive coupling reference electrode 3 are used for completing synchronous acquisition of capacitive coupling electrocardiosignals and capacitive coupling impedance volume signals when a human body wears thin clothes and cannot directly attach electrode plates to skin; the method comprises the following steps: according to the electrode arrangement mode of the four-electrode method, the capacitive coupling excitation electrode 1 and the capacitive coupling measurement electrode 2 are fixed on the outer side of the human arm clothes, and meanwhile, the other capacitive coupling measurement electrodes 2 and the capacitive coupling reference electrodes 3 are fixed on the periphery of the human chest clothes in the chest electrocardio three-lead mode.
The electrocardio and impedance volume signal measuring module is used for synchronously acquiring a capacitance coupling impedance volume ratio (CCIPG) signal and a Capacitance Coupling Electrocardiogram (CCECG) signal.
The data acquisition module is used for converting the received chest lead CCECG and arm CCIPG signals into digital signals and transmitting the digital signals to the controller, so that the controller obtains a capacitive coupling electrocardiogram waveform and an impedance volume ratio waveform chart of a measurer.
The feature extraction module is used for obtaining Pulse Wave Transit Time (PWTT).
The impedance volume characteristic parameter calculation module is used for acquiring morphological characteristic parameters of physiological characteristics of the cardiovascular system reflected on the impedance volume rate oscillogram.
And the PTT blood pressure calculation module is used for calculating a blood pressure value 1.
The machine learning module of the PTT fusion impedance volume parameter is used for performing learning training by taking the morphological characteristic parameter of the physiological characteristics of the cardiovascular system reflected on the impedance volume rate oscillogram and the blood pressure value 1 as sample data, and establishing a final blood pressure model.
The blood pressure prediction module is used for predicting the blood pressure value of the human body.
As shown in fig. 2, the hardware system circuit of the present invention includes a coupled electrocardiographic measurement unit, a coupled impedance volume ratio measurement unit, and a data acquisition module circuit; the coupled electrocardio measuring unit comprises a differential amplifying circuit, a 50Hz trap circuit and a band-pass filter circuit, wherein the input end of the differential amplifying circuit is connected with an electrocardio coupling measuring electrode and is used for carrying out unit gain difference on a capacitive coupling electrocardiosignal acquired by the coupling measuring electrode and an electrocardiosignal of a reference electrode and simultaneously leading out common mode interference of the electrocardio coupling electrode, and the 50Hz trap circuit and the band-pass filter circuit are used for filtering the differentially amplified signals to obtain chest lead electrocardiosignals; the coupled impedance volume ratio measuring unit comprises a main control circuit, a constant current source circuit, a differential amplifying circuit, a filter circuit, a full-wave rectifying circuit and a differentiating circuit, wherein the main control circuit controls the constant current source circuit to output 50KHz and 0.3mA currents, the impedance of the arm of a human body is measured by adopting a four-electrode method, the input end of the differential amplifying circuit is connected with a coupled measuring electrode and is used for carrying out differential amplification on a capacitance coupled impedance volume signal acquired by the coupled measuring electrode, and the processed signal enters the full-wave rectifying and differentiating circuit through the filter circuit to obtain an arm impedance volume ratio signal; and the data acquisition module mainly comprises an A/D circuit and is used for converting the received chest lead electrocardiosignals and arm impedance volume rate signals into digital signals and transmitting the digital signals to the controller, so that the controller obtains the capacitive coupling electrocardiogram waveform and impedance volume rate oscillogram of a measurer.
The non-contact blood pressure detection method specifically comprises the following steps:
step one, positioning an effective peak point of an R wave of an acquired electrocardiogram waveform to obtain an effective peak position of the R wave of the electrocardiogram waveform; and positioning the rising branch end point of the impedance volume rate signal oscillogram to obtain the rising branch end point position of the impedance volume rate signal oscillogram.
And after the jth R-wave effective peak point appears on the electrocardiogram waveform, determining the jth R-wave effective peak point position on the electrocardiogram waveform, and determining the rising branch end point position of the impedance volume ratio signal waveform diagram corresponding to the jth R-wave effective peak point on the electrocardiogram waveform. Calculating the heart rate HR of the jth measurerj，HRj＝1/TjWherein T isjCalculating the time difference between the j-th R wave effective peak point on the electrocardiogram waveform and the j + 1-th R wave effective peak point on the electrocardiogram waveform to obtain the j-th pulse wave propagation time PWTT of the measurerj。
Step two, according to the jth heart rate and the jth pulse wave conduction time PWTTjCalculating the jth diastolic pressure DBPjAnd the jth systolic pressure SBPj(ii) a Obtaining the jth diastolic pressure DBPjThe calculation expression of (a) is: DBPj＝a*ln PWTTj+b*HRj+c*TRCX+d*TR+e*TCX+f*SBX+ g, jth systolic pressure SBPjThe calculation expression of (a) is: SBPj＝α*ln PWTTj+β*TRBC+δ*TRCX+ε*TR+ gamma. Wherein, the parameter values of a, b, c, d, e, f, g, alpha, beta, delta, epsilon, gamma and the like are matched and valued by the measurer, an electrocardio impedance volume rate data set of m groups of measurers is obtained, m is more than or equal to 2, and the systolic pressure and the diastolic pressure are measured by using the sphygmomanometer synchronously. The ECG impedance-volume rate data set includes the heart rate, the pulse wave transit time and the morphological parameters of the impedance-volume rate oscillogram. M groups of electrocardio impedance volume rate data sets and pairsThe corresponding systolic pressure and diastolic pressure are substituted into the training to obtain the matched model parameter value.
Step three, the ratio (T) of the time of the morphological characteristic parameter BC section of the impedance volume ratio signal oscillogram to the time of a single cycleRBC) Ratio of CX period time to single period time (T)RCX) The ratio of the ejection time to the monocycle (T)R) Slow ejection period time (T)CX) Ejection time (T)BX) And stroke cardiac output (S)BX) And the like are introduced into an index data set formed by the PTT blood pressure calculation module.
Establishing a nonlinear model of the blood pressure by adopting a machine learning mode based on an extreme random forest model; the random forest is a supervised learning algorithm, is an integrated learning algorithm taking a decision tree as a base learning device, and has the construction process that: firstly, introducing morphological characteristic parameters of an impedance volume rate signal oscillogram into an index data set consisting of a PTT blood pressure calculation module; selecting 6 groups of sample data from the original training set to generate 6 training sets; thirdly, respectively training 6 decision tree models for the 6 training sets; fourthly, selecting the best characteristic for splitting aiming at a single decision tree model, and repeating the operation for multiple times; fifthly, forming a random forest by the generated 6 decision trees; and sixthly, obtaining a predicted value of the blood pressure, and obtaining a result of a verification set according to the cross validation of the test set, thereby evaluating the correctness of the result of the regression model.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (4)
1. A non-contact blood pressure detector based on a capacitive coupling electrode and a method thereof,
the system comprises a capacitive coupling excitation electrode, a capacitive coupling measuring electrode, a capacitive coupling reference electrode, an electrocardio and impedance volume signal measuring module, a data acquisition module, a feature extraction module, an impedance volume feature parameter calculating module, a PTT blood pressure calculating module, a PTT fusion impedance volume parameter machine learning module and a blood pressure prediction module, wherein the capacitive coupling excitation electrode, the capacitive coupling measuring electrode, the capacitive coupling reference electrode, the electrocardio and impedance volume signal measuring module, the data acquisition module, the feature extraction module, the impedance volume feature parameter calculating module, the PTT blood pressure calculating module, the PTT fusion impedance volume parameter machine learning module and the blood pressure prediction module are sequentially connected;
the capacitive coupling excitation electrode is used for being matched with the capacitive coupling measuring electrode and attached to the outer side of clothes of a human arm to carry out impedance volume characteristic detection;
the other capacitive coupling measuring electrode is used for matching with the capacitive coupling reference electrode and attaching the capacitive coupling reference electrode to the outer side of clothes on the chest part of the human body to detect electrocardiogram signals;
the electrocardio and impedance volume signal measuring module is used for synchronously acquiring a capacitance coupling impedance volume ratio (CCIPG) signal and a Capacitance Coupling Electrocardiogram (CCECG) signal;
the data acquisition module is used for converting the received chest lead CCECG and arm CCIPG signals into digital signals and transmitting the digital signals to the controller, so that the controller obtains a capacitive coupling electrocardiogram waveform and an impedance volume ratio oscillogram of a measurer;
the feature extraction module is used for acquiring Pulse Wave Transit Time (PWTT);
the impedance volume characteristic parameter calculation module is used for acquiring morphological characteristic parameters of physiological characteristics of a cardiovascular system reflected on the impedance volume rate oscillogram;
the PTT blood pressure calculation module is used for calculating a blood pressure value 1;
the machine learning module of the PTT fusion impedance volume parameter is used for performing learning training by taking morphological characteristic parameters of physiological characteristics of a cardiovascular system reflected on the impedance volume rate oscillogram and a blood pressure value 1 as sample data, and establishing a final blood pressure model;
the blood pressure prediction module is used for predicting the blood pressure value of the human body.
2. The impedance volume characteristic calculation module of claim 1. The characteristic is that the morphological characteristic parameters reflecting the physiological characteristics of the human cardiovascular system on the impedance volume ratio oscillogram comprise: ratio of BC period time to single period time (T)RBC) Ratio of CX period time to single period time (T)RCX) The ratio of the ejection time to the monocycle time (T)R) Slow ejection period time (T)CX) Ejection time (T)BX) And stroke cardiac output (S)BX)。
3. The non-contact blood pressure monitor and method based on capacitive coupling electrode according to claim 1. The method is characterized in that:
the calculated expression for diastolic pressure DBP is:
DBP＝a*lnPWTT+b*HRj+c*TRCX+d*TR+e*TCX+f*SBX+g
the calculated expression for the systolic blood pressure SBP is:
SBP＝α*lnPWTT+β*TRBC+δ*TRCX+ε*TR+γ
wherein, the parameter values of a, b, c, d, e, f, g, alpha, beta, delta, epsilon, gamma and the like are matched and valued by the measurer, an electrocardio impedance volume rate data set of m groups of measurers is obtained, m is more than or equal to 2, and the systolic pressure and the diastolic pressure are measured by using the sphygmomanometer synchronously. The ECG and RC data sets include the heart rate, the pulse transit time, and the morphology parameters of the RC oscillogram. And substituting the m groups of electrocardio-impedance volume rate data sets and the corresponding systolic pressure and diastolic pressure into training to obtain the matched model parameter values.
4. The PTT fusion impedance volume parameter machine learning module of claim 1, wherein: the method for establishing the nonlinear blood pressure model based on the machine learning mode of the extreme random forest model is detailed as follows, firstly, morphological characteristic parameters of an impedance volume rate signal oscillogram are introduced into an index data set formed by a PTT blood pressure calculation module, secondly, 6 groups of sample data are selected from an original training set, and 6 training sets are generated; thirdly, respectively training 6 decision tree models for the 6 training sets; fourthly, selecting the best characteristic for splitting aiming at a single decision tree model, and repeating the operation for multiple times; fifthly, forming a random forest by the generated 6 decision trees; and sixthly, obtaining a predicted value of the blood pressure, and obtaining a result of a verification set according to the cross validation of the test set, thereby evaluating the correctness of the result of the regression model.
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