CN114224304A - A dynamic cuffless continuous blood pressure measurement method, device and storage medium - Google Patents
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Abstract
本发明公开了一种动态无袖带连续血压测量方法、装置及可存储介质，具体应用领域为血压检测，其中方法包括：获取被测者的个人特征信息、心电信号以及光电容积脉搏波信号；对所述心电信号及所述光电容积脉搏波信号进行预处理，得到脉搏波传导时间信息；对经过预处理的所述光电容积脉搏波信号进行提取，得到脉搏波波形信息；利用神经网络建立所述脉搏波传导时间信息、所述脉搏波波形信息及所述个人特征信息与血压的关系模型；本发明检测血压时全面考虑了与血压相关的心血管方面的参数，同时考虑了个人的体征参数。
The invention discloses a dynamic cuffless continuous blood pressure measurement method, a device and a storable medium, and the specific application field is blood pressure detection. ; Preprocess the ECG signal and the photoplethysmographic signal to obtain pulse wave transit time information; extract the preprocessed photoplethysmographic signal to obtain pulse wave waveform information; use a neural network The relationship model between the pulse wave transit time information, the pulse wave waveform information and the personal characteristic information and blood pressure is established; the present invention comprehensively considers the cardiovascular parameters related to blood pressure when detecting the blood pressure, and also considers the individual's blood pressure. Physical parameters.
Description
Technical Field
The invention relates to the technical field of blood pressure detection, in particular to a dynamic cuff-free continuous blood pressure measuring method and device and a storage medium.
Background
At present, blood pressure is one of important physiological parameters reflecting the functions of human heart and blood vessels, and is also an important basis for clinical disease diagnosis and effect evaluation methods. With the improvement of living standard of people, the pace of life is accelerated, the incidence of diseases such as cerebral apoplexy and myocardial infarction caused by hypertension is higher and higher due to reasons such as irregular living habits, and the like, so that the effective detection of the blood pressure of the human body has great significance for preventing and controlling the hypertension and chronic diseases caused by the hypertension.
However, a series of experiments show that PTT has a certain relation with blood pressure, so that the blood pressure can be indirectly measured through PTT, and cuff-free continuous detection of the blood pressure is realized. The photoplethysmography is affected by the heart, the viscosity of the blood vessel, the hardness of the blood vessel, the resistance of the blood vessel and the like in the process of spreading along the artery blood vessel. Therefore, the pulse wave waveform contains much information about the blood vessel, and many researchers have studied the correlation between the pulse wave waveform and the blood pressure, and have realized the detection of the cuff-free blood pressure by the pulse wave waveform. However, when the pulse wave waveform is used for blood pressure prediction, a researcher only selects partial parameters of the photoplethysmography waveform to participate in prediction, and does not consider the whole pulse wave waveform, so that partial pulse wave waveform information related to blood pressure is lost. Further studies by researchers have found that blood pressure is not only related to PTT and pulse wave waveform, but also has a certain relationship with PCPs, such as age, height, weight and sex. The invention comprehensively considers PTT, PWW and PCPs and establishes a relation model between the PTT, the PWW and the PCPs and the blood pressure.
Therefore, how to provide a cuff-free continuous blood pressure measurement method capable of solving the above problems is a problem that needs to be solved urgently by those skilled in the art.
Disclosure of Invention
In view of the above, the present invention provides a dynamic cuff-less continuous blood pressure measuring method, device and storage medium, which fully considers the cardiovascular parameters related to blood pressure and the physical parameters of an individual when detecting blood pressure.
In order to achieve the purpose, the invention adopts the following technical scheme:
a dynamic cuff-less continuous blood pressure measurement method, comprising:
acquiring personal characteristic information, electrocardiosignals and photoplethysmographic signals of a tested person;
preprocessing the electrocardiosignals and the photoplethysmography signals to obtain pulse wave conduction time information;
extracting the preprocessed photoplethysmography signals to obtain pulse wave waveform information;
and establishing a relation model of the pulse wave conduction time information, the pulse wave waveform information and the personal characteristic information with the blood pressure by utilizing a neural network to realize the measurement of the blood pressure.
Preferably, the specific process of obtaining the pulse wave transit time information includes:
acquiring a time difference between an R wave peak value of the adjacent electrocardiosignals and a peak value of the photoplethysmography signals, wherein the time difference is pulse wave conduction time information and is specifically represented by the following formula:
in the formula (I), the compound is shown in the specification,
the mean value of the pulse wave conduction time obtained by the electrocardiosignal and the photoplethysmography pulse wave signal with a certain data length is represented; ECG (ECG)R[i]The abscissa representing the peak value of the R wave of the ith electrocardiosignal, PPGmax[i]The abscissa represents the peak value of the ith photoplethysmographic signal; m represents ECGR[i]N denotes the PPGmax[i]The array length of (2).
Preferably, the specific process of preprocessing the photoplethysmographic signal includes:
extracting the peak value and the valley value of the photoplethysmography pulse wave signal by a self-adaptive window function method, and fitting the baseline of the photoplethysmography pulse wave signal by an interpolation method;
according to Lambert beer's law, the photoelectric volume pulse wave waveform and photoelectric volume pulse wave baseline are respectively logarithmized and then subjected to difference processing.
Preferably, the specific process of extracting the preprocessed photoplethysmographic pulse wave signals to obtain pulse wave waveform information includes:
and resampling the preprocessed photoplethysmography pulse waves, and correspondingly accumulating the multiple single-period photoplethysmography pulse waves according to the resampling sequence of sampling points in a period to obtain a pulse wave waveform of one period.
Preferably, the specific process of preprocessing the electrocardiographic signal includes:
and performing filtering processing of digital high-pass, digital low-pass and wavelet transform.
Preferably, the neural network comprises an input layer, a hidden layer and an output layer which are connected in sequence, wherein the number of neurons of the hidden layer is set according to the following formula:
in the formula, l represents the neuron number of a hidden layer, m represents the neuron number of an input layer, n represents the neuron number of an output layer, and alpha represents a positive integer of 1-10.
Further, the present invention also provides a dynamic cuff-less continuous blood pressure measuring device, comprising:
the data acquisition module is used for acquiring personal characteristic information, electrocardiosignals and photoplethysmography signals of a tested person;
the data preprocessing module is connected with the data acquisition module and is used for preprocessing the electrocardiosignals and the photoplethysmography signals;
the data extraction module is connected with the data preprocessing module and used for extracting the preprocessed photoplethysmography signals to obtain pulse wave waveform information;
and the data prediction module is connected with the data extraction module and is used for utilizing a neural network to perform relationship model between the pulse wave conduction time information, the pulse wave waveform information and the personal characteristic information and the blood pressure.
Further, the present invention also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the dynamic cuff-less continuous blood pressure measurement method of any one of the above.
Compared with the prior art, the invention discloses a dynamic cuff-free continuous blood pressure measuring method, a dynamic cuff-free continuous blood pressure measuring device and a storable medium, and the method has the following beneficial effects:
1. the method for preprocessing the photoplethysmography signals removes the interference of static information such as skin, tissues and the like in the photoplethysmography signals by using the Lambert beer law, and improves the signal-to-noise ratio of useful blood pressure information in the photoplethysmography signals.
2. The method takes the PWW as an input parameter, integrally inputs the waveform into a neural network, and automatically fits the relation between the PWW and the blood pressure by using the neural network; in order to improve the accuracy of the blood pressure model, PTT and PWPs are added to input parameters, and the optimal blood pressure model is finally obtained through multiple times of training; compared with the blood pressure measured by the traditional method, the blood pressure measured by the blood pressure model obtained by using the system designed by the invention has higher correlation and smaller RMSE;
3. after the blood pressure system model is established, the invention can realize the real-time cuff-free continuous blood pressure detection of the tested person. In addition, the system designed by the invention has high precision, is convenient to operate and easy to carry, and can realize portable cuff-free continuous blood pressure detection.
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 embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flow chart of a dynamic cuff-less continuous blood pressure measurement method according to the present invention;
FIG. 2 is a schematic block diagram of a dynamic cuff-less continuous blood pressure measuring device according to the present invention;
fig. 3 is a flowchart of a pulse waveform acquisition algorithm according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to the attached figure 1, the embodiment of the invention discloses a dynamic cuff-free continuous blood pressure measuring method, which comprises the following steps:
acquiring personal characteristic information, electrocardiosignals and photoplethysmographic signals of a tested person;
preprocessing the electrocardiosignal and the photoplethysmography signal to obtain pulse wave conduction time information;
extracting the preprocessed photoplethysmography signals to obtain pulse wave waveform information;
and establishing a relation model of pulse wave conduction time information, pulse wave waveform information and personal characteristic information and blood pressure by using a neural network to realize the measurement of the blood pressure.
In a specific embodiment, the specific process of obtaining the pulse wave transit time information includes:
acquiring the time difference between the R wave peak value of the adjacent electrocardiosignals and the peak value of the photoplethysmography signals, wherein the time difference is the pulse wave conduction time information, and the specific formula is as follows:
in the formula (I), the compound is shown in the specification,
the mean value of the pulse wave conduction time obtained by the electrocardiosignal and the photoplethysmography pulse wave signal with a certain data length is represented; ECG (ECG)R[i]The abscissa representing the peak value of the R wave of the ith electrocardiosignal, PPGmax[i]The abscissa represents the peak value of the ith photoplethysmographic signal; m represents ECGR[i]N denotes the PPGmax[i]The array length of (2).
In a specific embodiment, the specific process of preprocessing the photoplethysmographic signal comprises:
extracting the peak value and the valley value of the photoplethysmography pulse wave signal by a self-adaptive window function method, and fitting the baseline of the photoplethysmography pulse wave signal by an interpolation method;
according to Lambert beer's law, the photoelectric volume pulse wave waveform and photoelectric volume pulse wave baseline are respectively logarithmized and then subjected to difference processing.
Specifically, for the photoplethysmography signal, an adaptive window function method (the initial width of a window function is 800, the width of the window function can be self-adjusted according to the frequency of the electrocardiograph signal, the width of the window function is updated every 10 seconds, the width of the window function after adjustment is 0.8 single-cycle pulse wave length) is used to find the valley value of the photoplethysmography signal, and then a cubic spline interpolation method is used to fit the baseline of the photoplethysmography pulse wave. Then, taking the logarithm with the base of 10 for the baseline of the photoelectric volume pulse wave and the baseline of the photoelectric volume pulse wave respectively, and then, taking the difference between the logarithm and the baseline, so that the interference of skin, tissue, light intensity and the like in the photoelectric volume pulse wave signal can be removed, wherein the specific expression is as follows:
wherein p' i represents photoplethysmographic signals after removing interference such as skin, tissue and light intensity; p [ i ] represents the original photoplethysmographic signal, l [ i ] represents the baseline of the photoplethysmographic signal.
Referring to fig. 3, in a specific embodiment, the specific process of extracting the preprocessed photoplethysmographic signal to obtain the pulse waveform information includes:
resampling is carried out on the preprocessed photoplethysmography pulse waves, a plurality of single-period photoplethysmography pulse waves are correspondingly accumulated according to the resampling sequence of sampling points in a period, and the single-period photoplethysmography pulse waves are used as pulse wave waveforms of one period, and the specific process is as follows:
s1: acquiring k valley points of the photoplethysmography;
s2: removing data before the first valley point and data after the last valley point;
s3: dividing the pulse wave into k-1 single-period pulse waves according to valley points;
s4: finding out the length of each single-period photoplethysmography as N, setting a sampling point in each single-period photoplethysmography after resampling as N, and making Q equal to N/M, R equal to M/N, and sampling interval number val equal to (N-R)/Q;
s5: down-sampling according to val, and repeating S3-S5 for multiple times;
s6: and accumulating the points corresponding to each single-period photoplethysmogram pulse wave into one point as a final waveform input into the neural network.
In a specific embodiment, the specific process of preprocessing the cardiac electrical signal comprises:
and performing filtering processing of digital high-pass, digital low-pass and wavelet transform.
In a specific embodiment, the neural network comprises an input layer, a hidden layer and an output layer which are connected in sequence, wherein the neuron number of the hidden layer is set according to the following formula:
in the formula, l represents the number of neurons in a hidden layer, m represents the number of neurons in an input layer, n represents the number of neurons in an output layer, alpha represents a positive integer of 1-10, and the number of neurons in the hidden layer is selected to be 20 after multiple times of training.
Specifically, the pulse transit time PTT, the pulse wave waveform PWW and the personal characteristic parameters PCPs are used as input layers of an artificial neural network, SBP (systolic pressure) and DBP (diastolic pressure) are used as output layers of the neural network, and a blood pressure relation model system is established after multiple training.
Referring to fig. 2, an embodiment of the present invention further provides a dynamic cuff-less continuous blood pressure measuring device, including:
the data acquisition module is used for acquiring personal characteristic information, electrocardiosignals and photoplethysmography signals of a tested person;
the data preprocessing module is connected with the data acquisition module and is used for preprocessing the electrocardiosignal and the photoplethysmography signal;
the data extraction module is connected with the data preprocessing module and used for extracting the preprocessed photoplethysmography signals to obtain pulse wave waveform information;
and the data prediction module is connected with the data extraction module and is used for utilizing the relation model of the neural network pulse wave conduction time information, the pulse wave waveform information and the personal characteristic information with the blood pressure.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, and when being executed by a processor, the computer program implements the dynamic cuff-free continuous blood pressure measurement method according to any one of the above embodiments.
In order to verify the effectiveness of the system provided by the invention, the obtained data are divided into a control group and an experimental group to establish a blood pressure system model.
Wherein, the parameters of the contrast group participating in modeling are PTT, PWPs (pulse wave waveform characteristic parameters) and PCPs; parameters of the experimental group participating in modeling are PTT, PWW and PCPs; the control group and the experimental group are the same in other cases, and specific comparison results are shown in table 1.
TABLE 1 comparison of RMSE for the modeled and predicted results of the control and experimental groups
From Table 1, it can be seen that the Root Mean Square Error (RMSE) of SBP was reduced from 5.2mmHg to 3.5mmHg, the RMSE of SBP (systolic blood pressure) was reduced by 32.69%, the RMSE of DBP (diastolic blood pressure) was also reduced from 2.4mmHg to 1.8mmHg, and the RMSE of DBP was reduced by 25% compared to the control modeling results.
Compared with a control group, the experimental group has higher model correlation, more concentrated distribution range of a Bland-Altman graph and lower RMSE. The dynamic cuff-free continuous blood pressure monitoring method provided by the invention is characterized in that a conduction time parameter-PTT, a photoplethysmography waveform-PWW and a physical sign parameter-PCPs are comprehensively considered during system modeling. Compared with the method only considering the pulse wave waveform characteristic parameter-PWPs, the method provided by the invention has the advantages that the factors considered when the blood pressure prediction model is established are more comprehensive, the photoplethysmography pulse wave waveform is resampled, the whole waveform is used as input to participate in the blood pressure modeling, the accuracy of the established system model is higher, and the blood pressure prediction value is more accurate.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A dynamic cuff-less continuous blood pressure measurement method is characterized by comprising the following steps:
acquiring personal characteristic information, electrocardiosignals and photoplethysmographic signals of a tested person;
preprocessing the electrocardiosignals and the photoplethysmography signals to obtain pulse wave conduction time information;
extracting the preprocessed photoplethysmography signals to obtain pulse wave waveform information;
and establishing a relation model of the pulse wave conduction time information, the pulse wave waveform information and the personal characteristic information with the blood pressure by utilizing a neural network to realize the measurement of the blood pressure.
2. The method as claimed in claim 1, wherein the specific process of obtaining the pulse transit time information comprises:
acquiring a time difference between an R wave peak value of the adjacent electrocardiosignals and a peak value of the photoplethysmography signals, wherein the time difference is pulse wave conduction time information and is specifically represented by the following formula:
in the formula (I), the compound is shown in the specification,
the mean value of the pulse wave conduction time obtained by the electrocardiosignal and the photoplethysmography pulse wave signal with a certain data length is represented; ECG (ECG)R[i]The abscissa representing the peak value of the R wave of the ith electrocardiosignal, PPGmax[i]The abscissa represents the peak value of the ith photoplethysmographic signal; m represents ECGR[i]N denotes the PPGmax[i]The array length of (2).
3. The dynamic cuff-free continuous blood pressure measuring method as claimed in claim 1, wherein the specific process of preprocessing the photoplethysmographic signal comprises:
extracting the peak value and the valley value of the photoplethysmography pulse wave signal by a self-adaptive window function method, and fitting the baseline of the photoplethysmography pulse wave signal by an interpolation method;
according to Lambert beer's law, the photoelectric volume pulse wave waveform and photoelectric volume pulse wave baseline are respectively logarithmized and then subjected to difference processing.
4. The method as claimed in claim 2, wherein the step of extracting the preprocessed photoplethysmographic signal to obtain the pulse waveform information comprises:
and resampling the preprocessed photoplethysmography pulse waves, and correspondingly accumulating the multiple single-period photoplethysmography pulse waves according to the resampling sequence of sampling points in a period to obtain a pulse wave waveform of one period.
5. The method as claimed in claim 2, wherein the step of preprocessing the electrocardiographic signals comprises:
and performing filtering processing of digital high-pass, digital low-pass and wavelet transform.
6. The method of claim 1, wherein the neural network comprises an input layer, a hidden layer and an output layer connected in sequence, wherein the number of neurons in the hidden layer is set according to the following formula:
in the formula, l represents the number of hidden layer neurons, m is a table
The number of neurons in an input layer, n represents the number of neurons in an output layer, and alpha represents a positive integer of 1-10.
7. A dynamic cuff-less continuous blood pressure measuring device, comprising:
the data acquisition module is used for acquiring personal characteristic information, electrocardiosignals and photoplethysmography signals of a tested person;
the data preprocessing module is connected with the data acquisition module and is used for preprocessing the electrocardiosignals and the photoplethysmography signals;
the data extraction module is connected with the data preprocessing module and used for extracting the preprocessed photoplethysmography signals to obtain pulse wave waveform information;
and the data prediction module is connected with the data extraction module and is used for utilizing a neural network to perform relationship model between the pulse wave conduction time information, the pulse wave waveform information and the personal characteristic information and the blood pressure.
8. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the dynamic cuff-less continuous blood pressure measurement method according to any one of claims 1 to 6.
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