CN114041765B - Self-calibration noninvasive continuous blood pressure measurement method and device - Google Patents
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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
- A61B5/022—Measuring pressure in heart or blood vessels by applying pressure to close blood vessels, e.g. against the skin; Ophthalmodynamometers
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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
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
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- A—HUMAN NECESSITIES
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- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2560/00—Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
- A61B2560/02—Operational features
- A61B2560/0223—Operational features of calibration, e.g. protocols for calibrating sensors
Abstract
The embodiment of the invention discloses a self-calibration noninvasive continuous blood pressure measurement method and a device, wherein the method comprises the following steps: collecting signals; carrying out feature extraction on data acquired in different directions based on a triaxial acceleration sensor, and determining whether motion exists in each direction according to the extracted features and a set threshold value; if motion exists, a motion prompt is given according to the motion state; analyzing pulse signal characteristics or electrocardiosignal characteristics, and determining whether to perform blood pressure calibration according to analysis results; if the blood pressure calibration is needed, carrying out blood pressure calculation through a pressurization calculation method, and inputting the obtained calibrated blood pressure value into a corresponding algorithm model of a continuous blood pressure measurement mode; the blood pressure value is calculated based on an algorithm model of a continuous blood pressure measurement mode. The embodiment of the invention can improve the continuous blood pressure measurement precision.
Description
Technical Field
The invention relates to the field of blood pressure measurement, in particular to a self-calibration noninvasive continuous blood pressure measurement method and device.
Background
Blood pressure is one of the most important physiological indexes of a human body, reflects the physical and blood supply function states of the heart and blood vessels of the human body, and has very important guiding significance in the aspects of disease diagnosis, treatment effect evaluation, drug effect evaluation and the like. However, most of the current blood pressure measurement methods in medical diagnosis adopt an intermittent blood pressure measurement method, and the intermittent blood pressure measurement method cannot accurately reflect the physiological condition of a human body because of the time continuity of the blood pressure, so that the continuous blood pressure measurement method for accurately judging the change situation of the blood pressure of the human body has great practical value.
The current methods for non-invasive continuous blood pressure measurement can be broadly divided into the following forms: (1) Pulse wave velocity method (PWV) or pulse wave transit time method (PWTT); (2) pulse characterization; (3) neural network-based or deep learning methods. The following outlines the above-described 3 non-invasive continuous blood pressure measurement methods.
Pulse wave propagation velocity method (PWV) or pulse wave propagation time method (PWTT), two signal sources are adopted, which can be electrocardiosignals and pulse signals, or two pulse signals, the distance between the two sensors is fixed, the relationship between the blood pressure value and PWV/PWTT is established by calculating the propagation velocity between the progressive signals, and finally the calculation of the blood pressure value is completed, and the method can be mainly divided into two modes when the model is built, (1) a linear model is built; (2) constructing a nonlinear model. The disadvantage is that the model is relatively fixed and the system cannot accurately measure the blood pressure when it changes significantly.
Pulse characterization, i.e. extracting corresponding feature points from each pulse wave cycle, such as the relative height (H/H) between the canyon low amplitude and the main peak amplitude of the pulse wave in the pulse wave drop, the relative height (g/H) between the peak amplitude of the dicrotic wave and the main peak amplitude of the pulse, or related frequency domain features. And finally establishing a regression equation by carrying out regression analysis on the obtained characteristics so as to realize continuous measurement of blood pressure. The disadvantage is that the model is relatively fixed and the system cannot calculate it accurately when there is a large change in blood pressure.
The neural network or the deep learning method is also applied to the field of noninvasive continuous blood pressure measurement along with the continuous improvement of the current calculation power and the better fitting mode of the neural network or the deep learning method to the nonlinear model, and the traditional calculation method is to complete model establishment through a large amount of data training and finally complete continuous measurement of blood pressure. Although the method can select a large amount of data during model training, including blood pressure states in various scenes, it is almost impossible to cover all blood pressure states, so that the accuracy of calculation is difficult to be ensured.
Currently, a strategy for calibrating a continuous blood pressure measurement mode exists, but model adjustment is basically performed through a strategy with a fixed time threshold, the mode is not intelligent enough, and the calibration mode may be too sensitive under certain conditions; this way of calibration may in turn be too lazy in certain situations. Therefore, the calibration mode cannot grasp the degree, and further, the method does not greatly help the improvement of the continuous blood pressure measurement accuracy.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides a self-calibration noninvasive continuous blood pressure measurement method which can improve the continuous blood pressure measurement precision.
The invention also provides a self-calibration noninvasive continuous blood pressure measuring device.
A self-calibrating non-invasive continuous blood pressure measurement method according to an embodiment of the first aspect of the present invention comprises the steps of: collecting signals, wherein the signals are electrocardiosignals, pulse signals, two paths of pulse signals or one path of pulse signals; determining whether the signal collecting part moves or not based on a triaxial acceleration sensor, wherein the method comprises the steps of extracting features of data acquired in different directions, and determining whether the movement exists in each direction or not according to the extracted features and a set threshold value; if motion exists, a motion prompt is given according to the motion state; after determining that the signal collecting part does not have motion, analyzing pulse signal characteristics or electrocardiosignal characteristics, and determining whether to perform blood pressure calibration according to an analysis result; if the blood pressure calibration is needed, carrying out blood pressure calculation through a pressurization calculation method, and inputting the obtained calibrated blood pressure value into a corresponding continuous blood pressure measurement algorithm model; the blood pressure value is calculated based on a continuous blood pressure measurement algorithm model.
The self-calibration noninvasive continuous blood pressure measurement method provided by the embodiment of the invention has at least the following beneficial effects: the self-calibration noninvasive continuous blood pressure measurement method of the embodiment of the invention can reset or adjust the model of the continuous blood pressure measurement mode under a certain specific scene (the blood pressure can be greatly changed) so as to ensure that the measurement accuracy of the blood pressure is higher. The method provided by the embodiment of the invention enables the noninvasive continuous blood pressure measurement to be more accurate, the previous application scene can be only in wearable equipment, and the noninvasive continuous blood pressure measurement can be carried out in medical or household scenes by adopting the method provided by the embodiment of the invention.
According to some embodiments of the invention, analyzing the pulse signal characteristics, determining whether to perform blood pressure calibration according to the analysis result comprises: analyzing pulse signal characteristics, wherein the pulse signal characteristics comprise a slope variation rate from valley to peak, a peak Gu Chazhi variation rate, a peak-to-peak difference variation rate, a Gu Gu difference variation rate and a blood pressure variation rate; and performing weight distribution on all the pulse signal characteristics, accumulating and summing, comparing a calculation result with a set threshold value, and determining to perform blood pressure calibration if the calculation result exceeds the set threshold value. The blood pressure calibration threshold setting in this embodiment is intelligent, and is not limited to calibration and reset of the model by using only the time threshold, but also can select related characteristics such as waveform characteristic variation degree, blood pressure variation degree and the like to perform intelligent setting of the blood pressure calibration mode.
According to some embodiments of the invention, analyzing the pulse signal characteristics includes calculating a variability from the corresponding variables; the variation rate of the slope from valley to peak is the slope from valley to peak, the variation rate of peak Gu Chazhi is peak Gu Chazhi, the variation rate of peak-peak difference is peak-peak difference, the variation rate of Gu Gu is Gu Gu, and the variation rate of blood pressure is blood pressure; setting a corresponding calibration switch for the pulse signal characteristics; the method for calculating the variation rate of a certain pulse signal characteristic specifically comprises the following steps: step A, calculating a variable average value in a fixed time T, calculating the ratio of the variable value at the moment T+1 to the variable average value, and if the calculation result exceeds a corresponding threshold value, opening a calibration switch of the pulse signal characteristic, wherein the coefficient is set to be 1; and B, the variable average value slides the data at the first moment backwards along with the window with the time window of T to pop up, the data at the moment of T+1 enters, and the step A is returned.
According to some embodiments of the invention, the calculating the blood pressure by the pressurization calculation method comprises the steps of: pressurizing the air bag by the pressurizing device, and stopping pressurizing when the air bag completely closes the artery; extracting pulse signals in the pressure curve through corresponding filters; extracting characteristics of the pulse signals, wherein the characteristics comprise peaks and positions thereof, and valleys and positions thereof; fitting the envelope of the pulse signal by using a linear interpolation mode or a nonlinear interpolation mode; and calculating the blood pressure by a qualitative method, a pressure envelope curve inflection point method or an amplitude coefficient method.
According to some embodiments of the invention, the continuous blood pressure measurement algorithm model comprises an algorithm model of PWV/PWTT architecture, an algorithm model based on pulse signal feature architecture, or an algorithm model based on neural network or deep learning architecture.
A self-calibrating, non-invasive continuous blood pressure measuring apparatus in accordance with an embodiment of the second aspect of the present invention, comprising: the signal acquisition system, including signal acquisition device, signal acquisition device includes: an electrocardiosignal acquisition device, a pulse signal acquisition device, two pulse signal acquisition devices or one pulse signal acquisition device; the motion detection system comprises a triaxial acceleration sensor and is used for extracting characteristics of data acquired in different directions and determining whether motion exists in each direction according to the extracted characteristics and a set threshold value; if motion exists, a motion prompt is given according to the motion state; the blood pressure calibration system is used for carrying out blood pressure calibration, carrying out blood pressure calculation through a pressurization calculation method, and inputting the obtained calibrated blood pressure value into a corresponding continuous blood pressure measurement algorithm model; the blood pressure calibration system further comprises a blood pressure calibration threshold calculation system, wherein the blood pressure calibration threshold calculation system is used for analyzing pulse signal characteristics or electrocardiosignal characteristics and determining whether to perform blood pressure calibration according to analysis results; the blood pressure measurement system is used for calculating a blood pressure value based on the continuous blood pressure measurement algorithm model.
The self-calibration noninvasive continuous blood pressure measuring device provided by the embodiment of the invention has at least the following beneficial effects: the self-calibration noninvasive continuous blood pressure measuring device provided by the embodiment of the invention can reset or adjust the model of the continuous blood pressure measuring mode under a certain specific scene (the blood pressure can be greatly changed) so as to ensure that the measuring precision of the blood pressure is higher. The non-invasive continuous blood pressure measuring device with the automatic correction mode can be used in medical or household situations.
According to some embodiments of the invention, the signal acquisition device is an optoelectronic signal acquisition device or a pressure signal acquisition device.
According to some embodiments of the invention, the blood pressure calibration system includes a micro pressurizing device, a micro depressurizing device, a balloon, and a pressure detecting device.
According to some embodiments of the invention, the algorithm model of the continuous blood pressure measurement mode comprises an algorithm model of a PWV/PWTT architecture, an algorithm model based on a pulse signal feature architecture or an algorithm model based on a neural network or a deep learning architecture.
According to some embodiments of the invention, the signal acquisition system further comprises a filter for removing interfering signals outside a preset frequency distribution range.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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The foregoing and/or additional aspects and advantages of the invention will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
FIG. 1 is a block schematic diagram of an apparatus according to an embodiment of the present invention;
FIG. 2 is a block schematic diagram of a blood pressure calibration system according to an embodiment of the present invention;
FIG. 3 is a flow chart of a method according to an embodiment of the invention;
FIG. 4 is a flow chart illustrating a pulse signal feature analysis according to an embodiment of the present invention;
fig. 5 is a flowchart illustrating a blood pressure calculation performed by the pressurization calculation method according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
In the description of the present invention, a plurality means one or more, and a plurality means two or more, and it is understood that greater than, less than, exceeding, etc. does not include the present number, and it is understood that greater than, less than, within, etc. include the present number. The description of the first and second is for the purpose of distinguishing between technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
Referring to fig. 1, the non-invasive continuous blood pressure measuring apparatus according to the embodiment of the present invention may be divided into the following four parts: the system comprises a signal acquisition system, a motion detection system, a blood pressure calibration system and a blood pressure measurement system.
The signal acquisition system comprises a plurality of signal acquisition devices, wherein the signal acquisition devices comprise but are not limited to an electrocardiosignal acquisition device, a pulse signal acquisition device, two paths of pulse signal acquisition devices or one path of pulse signal acquisition device, the signal acquisition devices can be photoelectric signal acquisition devices or pressure signal acquisition devices, and the signal acquisition devices comprise but are not limited to the two types of sensors. The sensor of the signal acquisition device of this embodiment may be integrated using a wrist or other non-invasive continuous blood pressure measurement.
The motion acquisition device comprises, but is not limited to, a triaxial acceleration sensor, and is mainly used for judging the motion state of the current tested person so as to assist the subsequent blood pressure calibration system in judging whether a calibration mode needs to be started or not. In the embodiment, the triaxial acceleration sensor performs feature extraction on data acquired in different directions, and determines whether motion exists in each direction according to the extracted features and a set threshold; if motion exists, the device can give motion prompts according to the motion state.
The blood pressure calibration system comprises a blood pressure calibration threshold calculation system, wherein the calculation mode is mainly used for carrying out feature analysis on pulse signals or electrocardiosignals in a mode of combining time domain, frequency domain or time domain and frequency domain, and comparing a calculated result with a set threshold by combining a corresponding time threshold to determine whether the correction of the blood pressure calculation coefficient is needed. With further reference to FIG. 2, the blood pressure calibration system includes, but is not limited to, a micro-pressurization device, a micro-depressurization device, a bladder and a pressure detection device, the primary purpose of which is to perform blood pressure calculations. If correction is required for the blood pressure calculation model, the micro pressurizing device is used for pressurizing the air bag, and the correction blood pressure value is calculated through addition, so that the correction is finally performed for the blood pressure model.
The blood pressure measurement system performs noninvasive continuous blood pressure calculation, and the model of the blood pressure measurement system can be PWV\PWTT architecture, pulse signal characteristic-based architecture or neural network-based or deep learning architecture.
When the device of the embodiment performs noninvasive continuous blood pressure measurement, firstly, the signal acquisition system acquires related signals through the electrocardiosignal and the pulse sensor, the two paths of pulse sensors or the single-channel pulse sensor, and as the frequency of the pulse or the electrocardiosignal is approximately distributed at 0.6Hz-4Hz, interference outside the frequency distribution range needs to be removed by adopting a filter in a corresponding frequency range, and then, the pulse signals or the electrocardiosignal are subjected to feature extraction by adopting a strategy of combining a time domain, a frequency domain or a time domain and a frequency domain, wherein the feature comprises but is not limited to P waves, Q waves, R waves, S waves, ST segments and the like of the electrocardiosignal and related frequency domain features thereof comprise but are not limited to frequency spectrums, power spectrums and the like of the pulse or the electrocardiosignal. Relevant features of the pulse signal are then extracted, including but not limited to, a main peak point, a valley point, a descending isthmus, a dicrotic wave, a relative height (H/H) between the descending isthmus amplitude and the main peak amplitude of the pulse wave, a relative height (g/H) between the dicrotic wave amplitude and the main peak amplitude of the pulse, and the like of the pulse signal, and relevant frequency domain features thereof include but are not limited to a frequency spectrum, a power spectrum, and the like thereof.
The triaxial acceleration sensor is analyzed, the data acquired by the sensors in different directions are subjected to feature extraction including but not limited to peak value, valley value and related frequency domain features, for example, when the peak value, the valley value and the frequency domain features exceed a certain set threshold value, the test is considered to have motion in the direction, the blood pressure correction mode is not started subsequently, and the system gives a motion prompt.
Blood pressure calibration assessment is performed to analyze pulse signal characteristics including, but not limited to, slope variability from valley to peak, peak Gu Chazhi variability, peak-to-peak difference variability, gu Gu difference variability, blood pressure variability. The following is a brief description of the method for calculating the slope variation rate from the valley to the peak, firstly, calculating the slope average value between the peaks and valleys in a fixed time T, then, calculating the ratio of the peak-valley slope value at the time T+1 to the average value at the time T before the peak-valley slope value exceeds a threshold m1, if the calculation result exceeds the threshold m1, considering that the feature is not tried to be used for the current blood pressure calculation model, at this time, setting the software coefficient of the feature to be turned on by a calibration switch, then, the slope average value is popped up by sliding the data at the first time backwards along with a window with the time window of T, the data at the time T+1 enters, and thus, the calculation mode of other features is calculated according to the strategy.
The pulse signal features of the embodiment of the present invention include a plurality of features, and the present embodiment performs weight distribution on the related features, and the slope variation rate (m 1) between the valley and the peak: 40%, peak Gu Chazhi variability (m 2), peak-to-peak variability (m 3), gu Gu variability (m 4) of 10% each, blood pressure variability (m 5): 30, they are then cumulatively summed, and the correction mode needs to be turned on at this time if the calculation result > threshold M, otherwise.
The blood pressure calibration calculation adopts a pressurization calculation mode, if the blood pressure calibration is needed, the micro pressurization device pressurizes the air bag, when the air bag completely closes the arterial pressure, the pressurization is stopped, the system can extract pulse signals in a pressure curve through a corresponding filter, and then the characteristics of the pulse signals including but not limited to peak value and position thereof, valley value and position thereof are extracted; then fitting the envelope of the pulse signal by using a linear interpolation mode or a nonlinear interpolation mode; and finally, carrying out blood pressure calculation by a qualitative method, a pressure envelope curve inflection point method or an amplitude coefficient method, and finally, bringing the obtained calibrated blood pressure value into an algorithm model of a related continuous blood pressure measurement mode to finally realize the resetting and calibration of the related model coefficient.
Referring to fig. 3, the embodiment of the invention further provides a self-calibration noninvasive continuous blood pressure measurement method, which specifically comprises the following steps:
s100, acquiring signals, including electrocardiosignals, pulse signals, two paths of pulse signals or one path of pulse signals;
s200, carrying out feature extraction on data acquired in different directions based on a triaxial acceleration sensor, and determining whether motion exists in each direction according to the extracted features and a set threshold;
s300, judging a motion state, and if motion exists, giving a motion prompt according to the motion state;
s400, analyzing pulse signal characteristics or electrocardiosignal characteristics, and determining whether to calibrate blood pressure according to analysis results;
s500, if blood pressure calibration is needed, blood pressure calculation is carried out through a pressurization calculation method, and the obtained calibrated blood pressure value is input into a corresponding algorithm model of a continuous blood pressure measurement mode; the algorithm model of the continuous blood pressure measurement mode comprises an algorithm model of a PWV/PWTT framework, an algorithm model based on a pulse signal characteristic framework or an algorithm model based on a neural network or a deep learning framework;
s600, calculating a blood pressure value based on an algorithm model of a continuous blood pressure measurement mode.
In the embodiment of the invention, the mathematical model for continuous blood pressure calculation is mainly divided into two algorithm models of 1.PWV/PWTT architecture; 2. an algorithm model based on a pulse signal feature architecture; 3. algorithm models based on neural networks or deep learning architectures. The mathematical model for calculating the blood pressure constructed by the three modes is a linear or nonlinear mathematical model, and parameters in the mathematical model in the traditional mode are fixed, but because the variability of the blood pressure of a human body is relatively large in certain specific scenes, a relatively large error can exist in fixing the parameters of the mathematical model to the calculated blood pressure value. Therefore, the main purpose of the invention is to correct the related parameters in the mathematical model by adopting a self-calibration mode so as to enable the measured blood pressure value to be more accurate. How do blood pressure be in good standing? Firstly, a point is to be determined, the blood pressure value obtained by the pressurization mode is basically accurate compared with the actual blood pressure value of the human body, so that if the fact that the tested person moves at the moment is detected or the fact that the variation of the blood pressure possibly exists at the moment is found through signal characteristics, pressurization measurement is carried out, the measured blood pressure value is substituted into a relevant parameter in a blood pressure calculation model, the relevant parameter is solved, and further the mathematical model for blood pressure measurement is updated.
In some embodiments, the relevant signals are acquired, and as the frequency of the pulse or the electrocardiosignal is approximately distributed at 0.6Hz-4Hz, the interference outside the frequency distribution range needs to be removed by adopting a filter in the corresponding frequency range, then the pulse signal or the electrocardiosignal is subjected to feature extraction by adopting a strategy of combining time domain, frequency domain or time domain and frequency domain, which is characterized by including but not limited to P-wave, Q-wave, R-wave, S-wave, ST-segment and the like of the electrocardiosignal and relevant frequency domain features thereof including but not limited to frequency spectrum, power spectrum and the like, and then the relevant features of the pulse signal including but not limited to main peak point, valley point, descending gorge, dicrotic wave, relative height (H/H) between the descending gorge amplitude and the main peak amplitude of the pulse wave, relative height (g/H) between the amplitude of the dicrotic wave and the main peak amplitude of the pulse and the relevant frequency domain features thereof include but not limited to frequency spectrum, power spectrum and the like.
In some embodiments, the triaxial acceleration sensor is analyzed, the data acquired by the sensors in different directions is extracted by features including, but not limited to, its peak, valley, and related frequency domain features, for example, when the peak, valley, and frequency domain features exceed a certain set threshold, then the test is considered to have motion in that direction, the blood pressure correction mode will not be started later, and the system will give motion cues.
With further reference to fig. 4, analyzing the pulse signal characteristics, determining whether to perform blood pressure calibration according to the analysis result includes:
s210, analyzing pulse signal characteristics, including slope variation rate from valley to peak, peak Gu Chazhi variation rate, peak-to-peak difference variation rate, gu Gu difference variation rate, blood pressure variation rate and the like;
and S220, carrying out weight distribution on all pulse signal characteristics, accumulating and summing, comparing a calculation result with a set threshold value, and if the calculation result exceeds the set threshold value, determining to calibrate the blood pressure.
In some embodiments, the pulse signal features are weighted and summed together in an accumulated manner: slope variation ratio (m 1) between valley and peak: 40%, peak Gu Chazhi variability (m 2), peak-to-peak variability (m 3), gu Gu variability (m 4) of 10% each, blood pressure variability (m 5): 30, they are then cumulatively summed, and the correction mode needs to be turned on at this time if the calculation result > threshold M, otherwise.
In some embodiments, the determination of the slope variability from valley to peak includes the steps of: firstly, calculating the average value of the slope between peaks and valleys in a fixed time T, then, calculating the ratio of the peak-valley slope value at the time T+1 to the average value at the time T before the peak-valley slope value at the time T, if the calculation result exceeds a threshold value m1, considering that the characteristic is not tried to be used for the current blood pressure calculation model, at the moment, setting the software coefficient to be 1 by opening a calibration switch of the characteristic, and then, sliding the data at the time T backwards along with a window with the time window T by the average value of the slope to pop up the data at the time T+1, and repeating the steps. Similarly, other features are calculated according to the strategy.
With further reference to fig. 5, in some embodiments, the blood pressure calculation by the pressurization calculation method includes the steps of:
s510, pressurizing the air bag through a pressurizing device, and stopping pressurizing when the air bag completely closes the artery;
s520, extracting pulse signals in the pressure curve through corresponding filters;
s530, extracting characteristics of pulse signals, including peak values and positions thereof, valley values and positions thereof, and the like;
s540, fitting the envelope of the pulse signal by using a linear interpolation mode or a nonlinear interpolation mode;
s550, calculating the blood pressure by a qualitative method, a pressure envelope curve inflection point method or an amplitude coefficient method.
Although specific embodiments are described herein, those of ordinary skill in the art will recognize that many other modifications or alternative embodiments are also within the scope of the present disclosure. For example, any of the functions and/or processing capabilities described in connection with a particular device or component may be performed by any other device or component. In addition, while various exemplary implementations and architectures have been described in terms of embodiments of the present disclosure, those of ordinary skill in the art will recognize that many other modifications to the exemplary implementations and architectures described herein are also within the scope of the present disclosure.
It should be appreciated that the method steps in embodiments of the present invention may be implemented or carried out by computer hardware, a combination of hardware and software, or by computer instructions stored in non-transitory computer-readable memory. The method may use standard programming techniques. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Furthermore, the operations of the processes described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes (or variations and/or combinations thereof) described herein may be performed under control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications), by hardware, or combinations thereof, collectively executing on one or more microprocessors. The computer program includes a plurality of instructions executable by one or more microprocessors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable computing platform, including, but not limited to, a personal computer, mini-computer, mainframe, workstation, network or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and so forth. Aspects of the invention may be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optical read and/or write storage medium, RAM, ROM, etc., such that it is readable by a programmable computer, which when read by a computer, is operable to configure and operate the computer to perform the processes described herein. Further, the machine readable code, or portions thereof, may be transmitted over a wired or wireless network. When such media includes instructions or programs that, in conjunction with a microprocessor or other data processor, implement the steps described above, the invention described herein includes these and other different types of non-transitory computer-readable storage media. The invention also includes the computer itself when programmed according to the methods and techniques of the present invention.
The computer program can be applied to the input data to perform the functions described herein, thereby converting the input data to generate output data that is stored to the non-volatile memory. The output information may also be applied to one or more output devices such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including specific visual depictions of physical and tangible objects produced on a display.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of one of ordinary skill in the art without departing from the spirit of the present invention.
Claims (5)
1. A self-calibrating, noninvasive continuous blood pressure measurement apparatus, comprising:
the signal acquisition system comprises a signal acquisition device, wherein the signal acquisition device comprises one of the following three devices: (1) an electrocardiosignal acquisition device, a pulse signal acquisition device, (2) two pulse signal acquisition devices and (3) one pulse signal acquisition device;
the motion detection system comprises a triaxial acceleration sensor and is used for extracting characteristics of data acquired in different directions and determining whether motion exists in each direction according to the extracted characteristics and a set threshold value; if motion exists, a motion prompt is given according to the motion state;
the blood pressure calibration system is used for carrying out blood pressure calibration, carrying out blood pressure calculation through a pressurization calculation method, and inputting the obtained calibrated blood pressure value into a corresponding continuous blood pressure measurement algorithm model; the blood pressure calibration system further comprises a blood pressure calibration threshold calculation system, wherein the blood pressure calibration threshold calculation system is used for analyzing pulse signal characteristics and determining whether to perform blood pressure calibration according to analysis results; analyzing the pulse signal characteristics, and determining whether to perform blood pressure calibration according to the analysis result comprises the following steps:
analyzing pulse signal characteristics, wherein the pulse signal characteristics comprise a slope variation rate from valley to peak, a peak Gu Chazhi variation rate, a peak-to-peak difference variation rate, a Gu Gu difference variation rate and a blood pressure variation rate;
weight distribution is carried out on all the pulse signal characteristics, summation is carried out, the calculation result is compared with a set threshold value, and if the calculation result exceeds the set threshold value, blood pressure calibration is determined;
analyzing the pulse signal features includes:
calculating the variation rate according to the corresponding variable; the variation rate of the slope from valley to peak is the slope from valley to peak, the variation rate of peak Gu Chazhi is peak Gu Chazhi, the variation rate of peak-peak difference is peak-peak difference, the variation rate of Gu Gu is Gu Gu, and the variation rate of blood pressure is blood pressure;
setting a corresponding calibration switch for the pulse signal characteristics;
for calculating the mutation rate, the method specifically comprises the following steps:
step A, calculating a variable average value in a fixed time T, calculating the ratio of the variable value at the moment T+1 to the variable average value, and if the calculation result exceeds a corresponding threshold value, opening a calibration switch of the pulse signal characteristic, wherein the coefficient is set to be 1;
step B, the variable average value slides the data at the first moment backwards along with the window with the time window of T to pop up, the data at the moment of T+1 enters, and the step A is returned;
the blood pressure measurement system is used for calculating a blood pressure value based on the continuous blood pressure measurement algorithm model.
2. The self-calibrating, non-invasive continuous blood pressure measuring device of claim 1, wherein the signal acquisition device is a photoelectric signal acquisition device or a pressure signal acquisition device.
3. The self-calibrating, non-invasive continuous blood pressure measuring device of claim 1, wherein the blood pressure calibrating system comprises a micro pressurizing device, a micro depressurizing device, a balloon, and a pressure detecting device.
4. The self-calibrating non-invasive continuous blood pressure measurement apparatus of claim 1, wherein the continuous blood pressure measurement algorithm model comprises an algorithm model of PWV/PWTT architecture or an algorithm model based on neural network architecture.
5. The self-calibrating non-invasive continuous blood pressure measuring apparatus of claim 1, wherein the signal acquisition system further comprises a filter for removing interfering signals outside a preset frequency distribution range.
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