CN109965861A - Continuous monitoring device when a kind of wearable non-invasive blood pressure of no cuff is long - Google Patents
Continuous monitoring device when a kind of wearable non-invasive blood pressure of no cuff is long Download PDFInfo
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- CN109965861A CN109965861A CN201910305355.4A CN201910305355A CN109965861A CN 109965861 A CN109965861 A CN 109965861A CN 201910305355 A CN201910305355 A CN 201910305355A CN 109965861 A CN109965861 A CN 109965861A
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- 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
- A61B5/021—Measuring pressure in heart or blood vessels
- A61B5/02108—Measuring pressure in heart or blood vessels from analysis of pulse wave characteristics
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- 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
Abstract
Continuous monitoring device when long the present invention relates to a kind of no wearable non-invasive blood pressure of cuff, belongs to medical measurement technical field.This method includes upper computer end and the next generator terminal；The next generator terminal includes: signal acquisition module, signal processing module, micro controller module, data memory module, data transmission module, LCD display module and power module；Upper computer end includes: the APP based on mobile terminal or the application software based on PC；Signal acquisition module includes: ECG signal sampling module, pulse wave signal detection module and 3-axis acceleration signal detection module；Signal processing module includes ECG's data compression module and pulse wave signal processing module.The present invention realizes the continuous non-invasive dynamic monitoring of blood pressure, understands the function situation of human body cardiovascular and cerebrovascular in time, provides abundant, effective clinical foundation for the prevention of cardiovascular and cerebrovascular diseases, diagnosis and treatment.
Description
Technical field
The invention belongs to medical measurement technical field, it is related to continuously monitoring dress when a kind of wearable non-invasive blood pressure of no cuff is long
It sets.
Background technique
Currently, the illness rate of cardiovascular disease is higher and higher, thus improve the awareness of cardiovascular disease, treatment rate and
Control rate reduces threat of the cardiovascular disease to national health, contains the ascendant trend of cardiovascular disease, be the weight faced at present
It wants and one of difficult task.
Compared with discontinuous measurement, it is all more and more prominent that non-invasive blood pressure continuously measures importance in medical research and clinically
Out.Either everyday home is nursed, or the monitoring to cardiovascular patient, even in the special occupations such as aerospace
Application, and using the slope of systolic pressure and diastolic pressure as ambulatory arterial hardenability value (ASSI) Lai Fanying artery sclerosis etc.
Aspect, non-invasive blood pressure continuously measure the waveform variation for all capableing of real-time monitoring arterial pressure because of it, survey to show interruption
Measure incomparable advantage.
In recent years, the body physiological state monitoring system based on wearable device becomes grinding for biomedical engineering field
Study carefully one of hot spot.The comfort that wearable device should meet Human Engineering Principle, meet wearing meets medically raw again
It manages the standard of signal detection, provide foundation for clinical diagnosis.
Non-invasive blood pressure continuous monitor system based on wearable device can be the cardiovascular diseases such as hypertension, coronary heart disease
Prevention, diagnosis and treatment provide strong help.Currently, most of blood pressure monitoring devices are surveyed using based on oscillographic method
Amount, this needs patient to wear inflation cuff for a long time, and the constraint of the long-time of cuff will cause the strong sense of discomfort of patient, together
When also can daily life to patient, action and sleep cause to seriously affect.And the non-invasive blood pressure based on wearable device connects
Continuous monitoring system can carry out the survey of continuous non-inflatable to human blood-pressure under the premise of not influencing patient's normal physiological activity
Amount, not will cause serious sense of discomfort to patient.
To sum up, need a kind of non-invasive blood pressure based on no cuff wearable device it is long when continuous monitoring device, pass through electrocardio
The consecutive variations of blood pressure are monitored with pulse wave sensor, understand the function situation of human body cardiovascular and cerebrovascular in time, are cardiovascular and cerebrovascular
Prevention, diagnosis and the treatment of disease provide abundant, effective useful clinically diagnosis basis.
Summary of the invention
In view of this, the purpose of the present invention is to provide continuously monitor dress when a kind of no wearable non-invasive blood pressure of cuff is long
It sets, while realizing continuous blood pressure noninvasive dynamic monitoring, the function situation of human body cardiovascular and cerebrovascular can be understood in time, be heart and brain blood
Prevention, diagnosis and the treatment of pipe disease provide abundant, effective useful clinically diagnosis basis.
In order to achieve the above objectives, the invention provides the following technical scheme:
Continuous monitoring device when a kind of wearable non-invasive blood pressure of no cuff is long, including upper computer end and the next generator terminal；It is described
The next generator terminal includes: signal acquisition module, signal processing module, micro controller module, data memory module, data transmission mould
Block, LCD display module and power module；The upper computer end include: APP based on mobile terminal or based on PC using soft
Part；The signal acquisition module includes: ECG signal sampling module, pulse wave signal detection module and 3-axis acceleration signal
Detection module；The signal processing module includes ECG's data compression module and pulse wave signal processing module；
The data memory module will be for that will pass through electrocardiosignal, pulse wave signal and the 3-axis acceleration of filtering processing
Signal is stored into SD card；
The electrocardiosignal and pulse wave signal that the LCD display module is used to that filtering processing will to be passed through are shown in real time
Show, and the pressure value of each heartbeat beat is obtained by the blood pressure prediction algorithm built in micro controller module；
The data transmission module will be for that will pass through electrocardiosignal, pulse wave signal and the 3-axis acceleration of filtering processing
Signal real-time Transmission to upper computer end, upper computer end is further handled and is analyzed to signal, obtains measured in addition to blood pressure more
More physiological characteristic parameters.
Further, the ECG signal sampling module is made of patch electrode and conducting wire；The pulse wave signal acquisition
Module is by the pulse wave based on piezoelectric polyvinylidene fluoride (Piezoelectric Polyvinylidene Fluoride, PVDF)
Sensor and its conducting wire composition, the 3-axis acceleration signal acquisition module are made of 3-axis acceleration sensor.
Further, the ECG's data compression module is by pre-amplification circuit, 0.5-100Hz bandwidth-limited circuit, 50Hz
Trap circuit, second amplifying circuit, optical coupling isolation circuit, level lifting circuit composition；The pulse wave processing circuit is by preposition
Amplifying circuit, 0.1-20Hz bandwidth-limited circuit, second amplifying circuit, level lifting circuit composition.
Carried out data transmission between the APP based on mobile terminal and bottom generator terminal using bluetooth approach；It is described to be based on
The application software of PC obtains the signal data of the next generator terminal acquisition by reading the SD card of the next generator terminal.
Further, the upper computer end carries out coherent signal processing using based on 3-axis acceleration sensor signal, upper
Position generator terminal filters out the motion artifacts of electrocardiosignal and pulse wave signal and baseline drift, and believes filtered electrocardio
Number and pulse wave signal carry out Feature point recognition, utilize the built-in company based on pulse wave translation time and pulse wave characteristic parameters
Continuous blood pressure prediction model, calculates often fight diastolic pressure and systolic pressure of often fighting in real time, and diastolic pressure and often fights to often fighting for being calculated
Systolic pressure is shown and is analyzed in the application software at the end APP or PC of mobile terminal.The APP energy based on mobile terminal
Enough electrocardiosignals and pulse wave signal by by filtering processing, often fighting of being calculated and are often fought in systolic pressure data at diastolic pressure
Cloud platform is reached, so that doctor checks and diagnoses.
Further, the motion artifacts based on 3-axis acceleration sensor signal filter out, comprising the following steps:
(1) the processing pause based on 3-axis acceleration sensor signal: the acceleration obtained according to 3-axis acceleration sensor
Degree evidence utilizes the threshold value a being previously setthresholdJudged, to show whether user is in strenuous exercise's shape
State, and then judge whether to suspend data acquisition；
(2) motion artifacts based on 3-axis acceleration sensor signal filter out: firstly, being surveyed using 3-axis acceleration sensor
Electrocardiosignal/pulse wave signal is measured, the electrocardiosignal/pulse wave signal and movement bring interference letter of human body are contained
Number；While acquiring electrocardiosignal/pulse wave signal, by 3-axis acceleration sensor acquire human body motor message and with
This reference-input signal as sef-adapting filter, then using sef-adapting filter to electrocardiosignal/pulse wave signal into
Row filtering processing, obtains electrocardiosignal/pulse wave signal of removal motion artifacts.
Further, the pause based on 3-axis acceleration sensor signal handles judgment rule are as follows: works as 3-axis acceleration
The total acceleration of sensorThen suspend processing；Otherwise continue to pass based on 3-axis acceleration
The processing of sensor signal；Wherein, ax、ay、azThe respectively component of acceleration of the 3-axis acceleration sensor signal on x, y, z axis.
Further, the filtering method of the pulse wave signal is the pulse wave based on dual-tree complex wavelet and cubic spline interpolation
Signal denoising algorithm, specifically includes the following steps:
(1) dual-tree complex wavelet decomposition is carried out to original noisy pulse wave signal, Bayes is used most to each layer wavelet coefficient
Big Posterior estimator threshold denoising；
(2) dual-tree complex wavelet inverse transformation is carried out, obtains filtering out the pulse wave signal after high-frequency noise；
(3) the obtained pulse wave signal for having filtered out high-frequency noise is detected into the trough point in signal using sliding window method；
(4) approximate baseline drift curve is fitted using cubic spline interpolation；
(5) the baseline drift curve fitted is subtracted with the pulse wave signal for having filtered out high-frequency noise, to realize high frequency
Noise and baseline drift filter out.
Further, the filtering method of the electrocardiosignal is that the electrocardiosignal based on dual-tree complex wavelet and morphologic filtering is gone
It makes an uproar algorithm, specifically includes the following steps:
(1) dual-tree complex wavelet decomposition is carried out to original noisy electrocardiosignal, it is maximum using Bayes to each layer wavelet coefficient
Posterior estimator threshold denoising；
(2) dual-tree complex wavelet inverse transformation is carried out, obtains filtering out the electrocardiosignal after high-frequency noise；
(3) filtering of morphology opening operation, filter are carried out to the electrocardiosignal for filtering out high-frequency noise using platypelloid type structural element
Except the positive pulse in electrocardiosignal；
(4) closing operation of mathematical morphology filtering is carried out to the electrocardiosignal for having filtered out positive pulse, eliminates the negative arteries and veins in electrocardiosignal
Punching, to filtered out the signal sequence of positive pulse and negative pulse, as baseline drift amount；
(5) electrocardiosignal that high-frequency noise is eliminated obtained in step (2) subtracts baseline drift in step (4) formula
Amount, to obtain the electrocardiosignal without high-frequency noise and baseline drift.
Further, the specific steps of Feature point recognition are carried out to filtered pulse wave signal are as follows:
(1) pulse wave signal trough point position, the position of as b point are detected using sliding window method；
(2) maximizing between two neighboring b point, the i.e. position for main wave wave crest c point；
(3) the first-order difference signal for seeking pulse wave signal, finds extreme point in specified range, in this range if having
The minimum value of the interior maximum value for finding maximum and minimum, respectively corresponds characteristic point g and f；If electrodeless in specified range
Be worth point, then seek the second differnce signal of pulse wave signal and judge whether there is inflection point within the scope of this, have, ask curvature minimum and
Maximum two points, respectively correspond characteristic point g and f；
(4) it asks the first-order difference signal of pulse wave signal to find minimum point between c point and f point, pole is found if having
The minimum value of small value is as characteristic point d；If nothing, seeks the second differnce signal of pulse wave signal and find turning for this range
Point, as characteristic point d；
(5) it carries out 5 layers of dual-tree complex wavelet to pulse wave signal to decompose, in d5 layer signal between characteristic point d and f corresponding position
There are the characteristic point e in the corresponding former pulse wave signal of maximum of points.
Further, the specific steps of Feature point recognition are carried out to filtered electrocardiosignal are as follows:
(1) 4 layers of dual-tree complex wavelet are carried out to filtered electrocardiosignal to decompose；
(2) the modulus maximum point of d4 layers of electrocardiosignal is identified using sliding window method；
(3) it corresponds to back in filtered electrocardiosignal, to realize the identification of R wave of electrocardiosignal.
The beneficial effects of the present invention are: the present invention realize non-invasive blood pressure it is long when the continuous continuous monitoring of dynamic, so as to
Enough function situations for understanding human body cardiovascular and cerebrovascular in time provide abundant, effective for the prevention of cardiovascular and cerebrovascular diseases, diagnosis and treatment
Useful clinically diagnosis basis.
Other advantages, target and feature of the invention will be illustrated in the following description to a certain extent, and
And to a certain extent, based on will be apparent to those skilled in the art to investigating hereafter, Huo Zheke
To be instructed from the practice of the present invention.Target of the invention and other advantages can be realized by following specification and
It obtains.
Detailed description of the invention
To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention is made below in conjunction with attached drawing excellent
The detailed description of choosing, in which:
Fig. 1 is continuous monitoring device structure chart of the present invention；
Fig. 2 is the flow chart of the signal processing pause based on 3-axis acceleration sensor；
Fig. 3 is the flow diagram that the motion artifacts based on 3-axis acceleration sensor filter out；
Fig. 4 is that electrocardiosignal denoises flow chart；
Fig. 5 is R wave of electrocardiosignal identification process figure.
Specific embodiment
Illustrate embodiments of the present invention below by way of specific specific example, those skilled in the art can be by this specification
Other advantages and efficacy of the present invention can be easily understood for revealed content.The present invention can also pass through in addition different specific realities
The mode of applying is embodied or practiced, the various details in this specification can also based on different viewpoints and application, without departing from
Various modifications or alterations are carried out under spirit of the invention.It should be noted that diagram provided in following embodiment is only to show
Meaning mode illustrates basic conception of the invention, and in the absence of conflict, the feature in following embodiment and embodiment can phase
Mutually combination.Wherein, the drawings are for illustrative purposes only and are merely schematic diagrams, rather than pictorial diagram, should not be understood as to this hair
Bright limitation.
As shown in Figure 1, continuous monitoring device when a kind of no wearable non-invasive blood pressure of cuff provided by the invention is long, including it is upper
Position generator terminal and the next generator terminal；The next generator terminal includes: signal acquisition module, signal processing module, micro controller module, data storage
Module, data transmission module, LCD display module and power module；Upper computer end includes: APP or base based on mobile terminal
In the application software of PC.Signal acquisition module includes: that ECG signal sampling module, pulse wave signal detection module and three axis add
Speed signal detection module.
Signal processing module includes ECG's data compression module and pulse wave signal processing module；ECG signal sampling mould
Block is made of patch electrode and conducting wire；The pulse wave signal acquisition module is by the pulse wave sensor based on PVDF and its leads
On line composition, the 3-axis acceleration signal acquisition module are made of 3-axis acceleration sensor.ECG's data compression module master
It will be by pre-amplification circuit, 0.5-100Hz bandwidth-limited circuit, 50Hz trap circuit, second amplifying circuit, light-coupled isolation electricity
Road, level lifting circuit composition；The pulse wave processing circuit is mainly by pre-amplification circuit, 0.1-20Hz bandpass filtering electricity
Road, second amplifying circuit, level lifting circuit composition.
Data memory module will be stored by the electrocardiosignal, pulse wave signal and 3-axis acceleration signal of filtering processing
Into SD card；LCD display module passes through the electrocardiosignal and pulse wave signal progress real-time display Jing Guo Lv Bochuli
Blood pressure prediction algorithm built in micro controller module obtains the pressure value of each heartbeat beat；Data transmission module will be by filtering
Electrocardiosignal, pulse wave signal and the 3-axis acceleration signal real-time Transmission of processing can be mobile terminal to upper computer end
Or PC, the APP by mobile terminal or the application software based on PC are further handled and are analyzed to signal, are obtained measured and are removed
More physiological characteristic parameters outside blood pressure.
Carried out data transmission between APP and the next generator terminal based on mobile terminal using bluetooth approach；It is described based on PC's
Application software obtains the signal data of the next generator terminal acquisition by reading the SD card of the next generator terminal.
Upper computer end carries out coherent signal processing using based on 3-axis acceleration sensor signal, in upper computer end to electrocardio
The noises such as the motion artifacts and baseline drift of signal and pulse wave signal are filtered out, and to filtered electrocardiosignal and arteries and veins
Wave signal of fighting carries out Feature point recognition, utilizes the built-in continuous blood pressure based on pulse wave translation time and pulse wave characteristic parameters
Prediction model calculates often fight diastolic pressure and systolic pressure of often fighting in real time, and diastolic pressure and often fights systolic pressure to often fighting for being calculated
It is shown and is analyzed in the application software at the end APP or PC of mobile terminal.Wherein, the APP based on mobile terminal can be incited somebody to action
Electrocardiosignal and pulse wave signal by filtering processing, be calculated often fight diastolic pressure and systolic pressure data of often fighting are uploaded to
Cloud platform, so that doctor checks and diagnoses.
The continuous blood pressure prediction model can be realized no noninvasive dynamic of cuff type blood pressure it is long when monitoring process in model
The adaptive dynamic of connection weight adjusts between structure and different neurons, guarantees the blood pressure precision of prediction of entire monitoring process,
It realizes and is monitored when the blood pressure of the really continuous beat of dynamic is long, invasive measurement bring wound and Tail cuff blood pressure is avoided to monitor charge and discharge
The constraint of gas.The blood pressure prediction model at a certain moment is the electrocardiosignal and photoelectricity volume obtained by software subsystem according to measurement
The characteristic parameter adaptive of pulse wave signal matches what classification determined from the noninvasive Dynamic monitoring pattern cluster of blood pressure, in blood pressure dynamic
The self-correcting for realizing blood pressure prediction model when long in measurement process, the artificial correction without carrying out blood pressure prediction model.
Motion artifacts based on 3-axis acceleration sensor signal filter out, comprising the following steps:
1) the processing pause based on 3-axis acceleration sensor signal: the acceleration obtained according to 3-axis acceleration sensor
Data utilize the threshold value a being previously setthresholdJudged, thus show whether user is in strenuous exercise's state,
And then judge whether to suspend data acquisition.
As shown in Fig. 2, judgment rule are as follows: when the total acceleration of 3-axis acceleration sensorThen suspend processing；Otherwise continue the processing based on 3-axis acceleration sensor signal；Its
In, ax、ay、azThe respectively component of acceleration of the 3-axis acceleration sensor signal on x, y, z axis.
2) motion artifacts based on 3-axis acceleration sensor signal filter out, as shown in Figure 3: firstly, being accelerated using three axis
Degree sensor measurement obtains electrocardiosignal/pulse wave signal, contains the electrocardiosignal/pulse wave signal and movement band of human body
The interference signal come；While acquiring electrocardiosignal/pulse wave signal, the fortune of human body is acquired by 3-axis acceleration sensor
Dynamic signal (i.e. acceleration signal) and the reference-input signal in this, as sef-adapting filter, then use sef-adapting filter
Electrocardiosignal/pulse wave signal is filtered, electrocardiosignal/pulse wave signal of removal motion artifacts is obtained.
The filtering method of pulse wave signal is to be calculated based on dual-tree complex wavelet and the Pulse Wave Signal Denoising of cubic spline interpolation
Method, specifically includes the following steps:
(1) dual-tree complex wavelet decomposition is carried out to original noisy pulse wave signal, Bayes is used most to each layer wavelet coefficient
Big Posterior estimator threshold denoising；
(2) dual-tree complex wavelet inverse transformation is carried out, obtains filtering out the pulse wave signal after high-frequency noise；
(3) the obtained pulse wave signal for having filtered out high-frequency noise is detected into the trough point in signal using sliding window method；
(4) approximate baseline drift curve is fitted using cubic spline interpolation；
(5) the baseline drift curve fitted is subtracted with the pulse wave signal for having filtered out high-frequency noise, to realize high frequency
Noise and baseline drift filter out.
As shown in figure 4, the filtering method of electrocardiosignal is that the electrocardiosignal based on dual-tree complex wavelet and morphologic filtering is gone
It makes an uproar algorithm, specifically includes the following steps:
(1) dual-tree complex wavelet decomposition is carried out to original noisy electrocardiosignal, it is maximum using Bayes to each layer wavelet coefficient
Posterior estimator threshold denoising；
(2) dual-tree complex wavelet inverse transformation is carried out, obtains filtering out the electrocardiosignal after high-frequency noise；
(3) filtering of morphology opening operation, filter are carried out to the electrocardiosignal for filtering out high-frequency noise using platypelloid type structural element
Except the positive pulse in electrocardiosignal；
(4) closing operation of mathematical morphology filtering is carried out to the electrocardiosignal for having filtered out positive pulse, eliminates the negative arteries and veins in electrocardiosignal
Punching, to filtered out the signal sequence of positive pulse and negative pulse, as baseline drift amount；
(5) electrocardiosignal that high-frequency noise is eliminated obtained in step (2) subtracts baseline drift in step (4) formula
Amount, to obtain the electrocardiosignal without high-frequency noise and baseline drift.
The specific steps of Feature point recognition are carried out to filtered pulse wave signal are as follows:
(1) pulse wave signal trough point position, the position of as b point are detected using sliding window method；
(2) maximizing between two neighboring b point, the i.e. position for main wave wave crest c point；
(3) the first-order difference signal for seeking pulse wave signal, finds extreme point in specified range, in this range if having
The minimum value of the interior maximum value for finding maximum and minimum, respectively corresponds characteristic point g and f；If electrodeless in specified range
Be worth point, then seek the second differnce signal of pulse wave signal and judge whether there is inflection point within the scope of this, have, ask curvature minimum and
Maximum two points, respectively correspond characteristic point g and f；
(4) it asks the first-order difference signal of pulse wave signal to find minimum point between c point and f point, pole is found if having
The minimum value of small value is as characteristic point d；If nothing, seeks the second differnce signal of pulse wave signal and find turning for this range
Point, as characteristic point d；
(5) it carries out 5 layers of dual-tree complex wavelet to pulse wave signal to decompose, in d5 layer signal between characteristic point d and f corresponding position
There are the characteristic point e in the corresponding former pulse wave signal of maximum of points.
As shown in figure 5, to the specific steps of filtered R wave of electrocardiosignal identification are as follows:
(1) 4 layers of dual-tree complex wavelet are carried out to filtered electrocardiosignal to decompose；
(2) the modulus maximum point of d4 layers of electrocardiosignal is identified using sliding window method；
(3) it corresponds to back in filtered electrocardiosignal, to realize the identification of R wave of electrocardiosignal.
The operating process of continuous monitoring device when the wearable non-invasive blood pressure of no cuff of the present invention is long are as follows:
(1) electrocardioelectrode is attached to body appointed part, and cardiac diagnosis lead-line and electrocardioelectrode is connected；
(2) piezoelectric polyvinylidene fluoride pulse wave sensor is placed in wrist appointed part, and pulse wave conducting wire is pacified
It installs；
(3) it will test device and be worn on body appointed part, opening detecting device, start recording data；
(4) Android phone bluetooth is opened, is matched with the bluetooth of detection device, opens simultaneously Android phone
APP, the collected electrocardiosignal of detection device and pulse wave signal will on APP real-time display, meanwhile, it is systolic pressure of often fighting, every
It fights diastolic pressure and blood viscosity value will also be shown on APP.
(5) after having recorded 24 hour datas, detection device is closed, takes out SD card, is inserted into the end PC, opens answering for the end PC
With software, the data in SD card are read, and show electrocardiosignal, pulse wave signal and continuous blood pressure on the interface of application software
Waveform.
Finally, it is stated that the above examples are only used to illustrate the technical scheme of the present invention and are not limiting, although referring to compared with
Good embodiment describes the invention in detail, those skilled in the art should understand that, it can be to skill of the invention
Art scheme is modified or replaced equivalently, and without departing from the objective and range of the technical program, should all be covered in the present invention
Scope of the claims in.
Claims (10)
- Continuous monitoring device when 1. a kind of wearable non-invasive blood pressure of no cuff is long, which is characterized in that including upper computer end and bottom Generator terminal；The bottom generator terminal includes: signal acquisition module, signal processing module, micro controller module, data memory module, number According to transmission module, LCD display module and power module；The upper computer end includes: APP based on mobile terminal or based on PC Application software；The signal acquisition module includes: that ECG signal sampling module, pulse wave signal detection module and three axis add Speed signal detection module；The signal processing module includes ECG's data compression module and pulse wave signal processing module；The data memory module will be for that will pass through electrocardiosignal, pulse wave signal and the 3-axis acceleration signal of filtering processing It stores into SD card；The LCD display module is used to the electrocardiosignal Jing Guo Lv Bochuli and pulse wave signal carrying out real-time display, and The pressure value of each heartbeat beat is obtained by the blood pressure prediction algorithm built in micro controller module；The data transmission module will be for that will pass through electrocardiosignal, pulse wave signal and the 3-axis acceleration signal of filtering processing Real-time Transmission to upper computer end, upper computer end is further handled and is analyzed to signal, and it is more in addition to blood pressure to obtain measured Physiological characteristic parameter.
- Continuous monitoring device when 2. a kind of no wearable non-invasive blood pressure of cuff according to claim 1 is long, which is characterized in that The ECG signal sampling module is made of patch electrode and conducting wire；The pulse wave signal acquisition module based on piezoelectricity by being gathered The pulse wave sensor and its conducting wire of vinylidene (Piezoelectric Polyvinylidene Fluoride, PVDF) Composition, the 3-axis acceleration signal acquisition module are made of 3-axis acceleration sensor.
- Continuous monitoring device when 3. a kind of no wearable non-invasive blood pressure of cuff according to claim 1 is long, which is characterized in that The ECG's data compression module is put by pre-amplification circuit, 0.5-100Hz bandwidth-limited circuit, 50Hz trap circuit, second level Big circuit, optical coupling isolation circuit, level lifting circuit composition；The pulse wave processing circuit is by pre-amplification circuit, 0.1- 20Hz bandwidth-limited circuit, second amplifying circuit, level lifting circuit composition.
- Continuous monitoring device when 4. a kind of no wearable non-invasive blood pressure of cuff according to claim 1 is long, which is characterized in that The upper computer end carries out coherent signal processing using based on 3-axis acceleration sensor signal, in upper computer end to electrocardiosignal And pulse wave signal motion artifacts and baseline drift filtered out, and to filtered electrocardiosignal and pulse wave signal into Row Feature point recognition, using the built-in continuous blood pressure prediction model based on pulse wave translation time and pulse wave characteristic parameters, It calculates and often fights diastolic pressure and systolic pressure of often fighting in real time, and diastolic pressure and often fight systolic pressure in mobile terminal to often fighting for being calculated The end APP or PC application software on shown and analyzed.
- Continuous monitoring device when 5. a kind of no wearable non-invasive blood pressure of cuff according to claim 4 is long, which is characterized in that Motion artifacts based on 3-axis acceleration sensor signal filter out, comprising the following steps:(1) the processing pause based on 3-axis acceleration sensor signal: the acceleration degree obtained according to 3-axis acceleration sensor According to utilizing the threshold value a being previously setthresholdJudged, thus show whether user is in strenuous exercise's state, into And judge whether to suspend data acquisition；(2) motion artifacts based on 3-axis acceleration sensor signal filter out: firstly, being measured using 3-axis acceleration sensor To electrocardiosignal/pulse wave signal, the electrocardiosignal/pulse wave signal and movement bring interference signal of human body are contained； While acquiring electrocardiosignal/pulse wave signal, the motor message of human body is acquired by 3-axis acceleration sensor and with this As the reference-input signal of sef-adapting filter, then electrocardiosignal/pulse wave signal is carried out using sef-adapting filter Filtering processing obtains electrocardiosignal/pulse wave signal of removal motion artifacts.
- Continuous monitoring device when 6. a kind of no wearable non-invasive blood pressure of cuff according to claim 5 is long, which is characterized in that The pause based on 3-axis acceleration sensor signal handles judgment rule are as follows: when the total acceleration of 3-axis acceleration sensorThen suspend processing；Otherwise continue the processing based on 3-axis acceleration sensor signal；Its In, ax、ay、azThe respectively component of acceleration of the 3-axis acceleration sensor signal on x, y, z axis.
- Continuous monitoring device when 7. a kind of no wearable non-invasive blood pressure of cuff according to claim 5 is long, which is characterized in that The filtering method of the pulse wave signal is the Pulse Wave Signal Denoising algorithm based on dual-tree complex wavelet and cubic spline interpolation, tool Body the following steps are included:(1) dual-tree complex wavelet decomposition is carried out to original noisy pulse wave signal, after using Bayes maximum each layer wavelet coefficient Test estimation threshold denoising；(2) dual-tree complex wavelet inverse transformation is carried out, obtains filtering out the pulse wave signal after high-frequency noise；(3) the obtained pulse wave signal for having filtered out high-frequency noise is detected into the trough point in signal using sliding window method；(4) approximate baseline drift curve is fitted using cubic spline interpolation；(5) the baseline drift curve fitted is subtracted with the pulse wave signal for having filtered out high-frequency noise, to realize high-frequency noise And baseline drift filters out.
- Continuous monitoring device when 8. a kind of no wearable non-invasive blood pressure of cuff according to claim 5 is long, which is characterized in that The filtering method of the electrocardiosignal is the Denoising Algorithm of ECG Signals based on dual-tree complex wavelet and morphologic filtering, is specifically included Following steps:(1) dual-tree complex wavelet decomposition is carried out to original noisy electrocardiosignal, Bayesian MAP is used to each layer wavelet coefficient Estimate threshold denoising；(2) dual-tree complex wavelet inverse transformation is carried out, obtains filtering out the electrocardiosignal after high-frequency noise；(3) filtering of morphology opening operation is carried out to the electrocardiosignal for filtering out high-frequency noise using platypelloid type structural element, filters out the heart Positive pulse in electric signal；(4) closing operation of mathematical morphology filtering is carried out to the electrocardiosignal for having filtered out positive pulse, eliminates the negative pulse in electrocardiosignal, from And the signal sequence of positive pulse and negative pulse has been filtered out, as baseline drift amount；(5) electrocardiosignal that high-frequency noise is eliminated obtained in step (2) subtracts baseline drift amount in step (4), from And obtain the electrocardiosignal without high-frequency noise and baseline drift.
- Continuous monitoring device when 9. a kind of no wearable non-invasive blood pressure of cuff according to claim 4 is long, which is characterized in that The specific steps of Feature point recognition are carried out to filtered pulse wave signal are as follows:(1) pulse wave signal trough point position, the position of as b point are detected using sliding window method；(2) maximizing between two neighboring b point, the i.e. position for main wave wave crest c point；(3) the first-order difference signal for seeking pulse wave signal, extreme point is found in specified range, is sought within the scope of this if having The maximum value of maximum and the minimum value of minimum are looked for, characteristic point g and f are respectively corresponded；If without extreme point in specified range, It then seeks the second differnce signal of pulse wave signal and judges whether there is inflection point within the scope of this, have, seek curvature minimum and maximum Two points respectively correspond characteristic point g and f；(4) it asks the first-order difference signal of pulse wave signal to find minimum point between c point and f point, minimum is found if having Minimum value as characteristic point d；If nothing, seeks the second differnce signal of pulse wave signal and find the inflection point of this range, i.e., It is characterized point d；(5) 5 layers of dual-tree complex wavelet are carried out to pulse wave signal to decompose, is existed between characteristic point d and f corresponding position in d5 layer signal Characteristic point e in the corresponding former pulse wave signal of maximum of points.
- 10. continuous monitoring device when a kind of no wearable non-invasive blood pressure of cuff according to claim 4 is long, feature exist In to the specific steps of filtered electrocardiosignal progress Feature point recognition are as follows:(1) 4 layers of dual-tree complex wavelet are carried out to filtered electrocardiosignal to decompose；(2) the modulus maximum point of d4 layers of electrocardiosignal is identified using sliding window method；(3) it corresponds to back in filtered electrocardiosignal, to realize the identification of R wave of electrocardiosignal.
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