CN117045216A - Blood index non-invasive medical analysis method based on fuzzy control - Google Patents
Blood index non-invasive medical analysis method based on fuzzy control Download PDFInfo
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Abstract
The invention discloses a blood index non-invasive medical analysis method based on fuzzy control, which relates to the technical field of non-invasive medical analysis, and comprises the following steps: s1, acquiring physical information of a medical user, and monitoring in real time through a non-invasive monitoring sensor worn by the medical user; s2, processing the pulse signals and the electrocardiosignals, generating pulse waves and electrocardiograms according to the reserved time length, and calculating blood pressure evaluation data of the medical user in a cooperative calculation mode; s3, carrying out fuzzy reasoning by utilizing a classification recognition model based on a fuzzy control theory according to blood pressure evaluation data and body information; and S4, visually displaying the pulse wave, the electrocardiogram and the blood pressure analysis result obtained by real-time monitoring. According to the invention, through cooperative calculation and fuzzy control theory, more comprehensive and accurate blood pressure evaluation data are provided, so that the understanding of the health condition of a patient is improved, and the blood pressure analysis result of the patient is accurately identified and output.
Description
Technical Field
The invention relates to the technical field of non-invasive medical analysis, in particular to a blood index non-invasive medical analysis method based on fuzzy control.
Background
The blood index includes a series of parameters for assessing a person's health and physical function. Among these, the blood pressure index is one of the measures the force exerted on the wall of a blood vessel by the heart pump blood as it pushes blood into various parts of the body. Blood pressure is generally expressed in two values: systolic (Systolic Blood Pressure, SBP) and diastolic (Diastolic Blood Pressure, DBP). Blood pressure is an important physiological index and is critical to the normal functioning of the body. Normal blood pressure levels help to maintain adequate blood flow to the various organs and tissues, ensuring the body's oxygen and nutrient supply.
The importance of blood pressure detection and medical analysis cannot be overshadowed, as blood pressure is a critical physiological indicator, playing a vital role in physical health and cardiovascular function. Blood pressure is one of the important indicators for assessing heart and blood vessel health. By measuring blood pressure periodically, it is possible to find out early whether the patient has problems such as hypertension (higher than normal level) or hypotension (lower than normal level). Hypertension is one of the major risk factors for heart disease, stroke and other cardiovascular diseases. Monitoring blood pressure helps assess the risk of a patient for heart disease. Many patients require medications to manage hypertension, and regular detection of blood pressure can ensure therapeutic effects and help doctors adjust medication doses. By monitoring blood pressure and utilizing the blood pressure value for analysis and diagnosis, other potential health problems such as kidney diseases, diabetes and the like can be found early.
Conventional blood pressure detection methods typically use a cuff and a sphygmomanometer. The doctor or nurse ties the cuff to the patient's upper arm and then inflates and deflates the cuff through the sphygmomanometer while listening to the pulsatile sounds to measure SBP and DBP, requiring a professional healthcare professional to perform the procedure, and typically a single point measurement, may not capture the patient's daily blood pressure fluctuations nor be suitable for long-term continuous monitoring, such as night monitoring or 24 hour monitoring.
In addition, medical professionals use the patient's blood pressure value to assess his cardiovascular risk and help determine whether treatment is needed, which may ignore other important physiological parameters such as height, body mass index, etc., and may result in an insufficiently comprehensive risk assessment, for hypertensive patients, blood pressure values are typically used to monitor the effect of treatment, however, the limitations of single point measurement are not fully understood.
Disclosure of Invention
In view of the above, it is necessary to provide a blood index non-invasive medical analysis method based on fuzzy control.
The invention provides a blood index non-invasive medical analysis method based on fuzzy control, which comprises the following steps:
s1, acquiring physical information of a medical user, monitoring in real time through a non-invasive monitoring sensor worn by the medical user, and acquiring pulse signals and electrocardiosignals;
s2, processing the pulse signals and the electrocardiosignals, generating pulse waves and electrocardiograms according to the reserved time length, and calculating blood pressure evaluation data of the medical user in a cooperative calculation mode;
s3, carrying out fuzzy reasoning by utilizing a classification recognition model based on a fuzzy control theory according to blood pressure evaluation data and body information, and outputting a blood pressure analysis result of a medical user;
and S4, visually displaying the pulse wave, electrocardiogram and blood pressure analysis results obtained by real-time monitoring, and carrying out early warning and reminding in time according to a preset early warning threshold.
Further, acquiring physical information of a medical user, performing real-time monitoring through a non-invasive monitoring sensor worn by the medical user, and acquiring pulse signals and electrocardiosignals comprises the following steps:
s11, acquiring physical information comprising the identity and physiological characteristics of a medical user, wherein the physical information comprises the identity, age, sex, height, weight, medical history and blood vessel blood information of the user;
s12, respectively wearing a piezoelectric pulse sensor and an electrocardio sensor at the wrist and chest positions of the medical user for acquiring pulse signals and electrocardio signals of the medical user;
s13, measuring normal blood pressure data of a medical user in a conventional state by using a blood pressure meter;
s14, establishing a pulse wave blood pressure formula according to the normal blood pressure data and the pulse wave conduction time;
s15, the piezoelectric pulse sensor and the electrocardio sensor transmit the acquired pulse signals and electrocardio signals to a data processing receiving end through a signal adjustment interface circuit.
Further, establishing a pulse wave blood pressure formula according to the normal blood pressure data and the pulse wave conduction time comprises the following steps:
s141, measuring pulse wave transmission distance between a piezoelectric pulse sensor worn by a medical user and an electrocardio sensor;
s142, establishing a pulse wave blood pressure formula by utilizing a mathematical relationship between the blood pressure data and the pulse wave conduction time, calculating the normal pulse wave conduction time of the medical user in a normal state by utilizing the normal blood pressure data, and verifying the pulse wave blood pressure formula by measuring the actual pulse wave conduction time.
Further, the expression of the pulse wave blood pressure formula is:
in the method, in the process of the invention,MTrepresenting pulse transit time;Lrepresenting the pulse wave transmission distance;ρrepresents blood density;drepresents the inside diameter of the blood vessel;arepresents the vessel wall thickness;grepresenting gravitational acceleration;BPrepresenting blood pressure data;Eelastic modulus at zero blood pressure;γmodulus representing the relationship between vascular pressure and elasticity.
Further, processing the pulse signal and the electrocardiosignal, generating a pulse wave and an electrocardiogram according to the reserved time length, and calculating blood pressure evaluation data of the medical user in a cooperative calculation mode comprises the following steps:
s21, filtering and noise reduction preprocessing is carried out on pulse signals and electrocardiosignals, and the pulse signals and the electrocardiosignals are respectively converted into pulse waves and electrocardiograms with uniform phase delay;
s22, detecting the peak of the pulse wave;
s23, detecting the peak of an electrocardiogram;
s24, measuring pulse wave conduction time between pulse waves and the peak of an electrocardiogram, and calculating the systolic pressure of the medical user by using a pulse wave blood pressure formula;
s25, detecting the pulse wave and the valley point in the electrocardiogram respectively, measuring the pulse wave conduction time between the pulse wave and the valley point of the electrocardiogram, and calculating the diastolic pressure of the medical user by using a pulse wave blood pressure formula;
s26, taking the calculated systolic pressure and diastolic pressure as blood pressure evaluation data of the medical user in the current state.
Further, the filtering noise reduction pretreatment is performed on the pulse signal and the electrocardiosignal, and the pulse signal and the electrocardiosignal are respectively converted into pulse waves and electrocardiograms with uniform phase delay, and the method comprises the following steps:
s211, filtering electrocardiosignals by using a 0.05Hz high-pass filter, and filtering pulse signals by using a 0.03Hz high-pass filter, so that the two waveforms are ensured to have the same phase delay;
s212, eliminating distortion signals at the 1S position in the pulse signals and the electrocardiosignals at the same time;
s213, converting the pulse signals and the electrocardiosignals after filtering and elimination into the pulse waves and the electrocardiograms which are visually displayed according to the reserved time length of the displayable window.
Further, the detecting the peak of the pulse wave includes the following steps:
s221, acquiring the amplitude range and amplitude of pulse waves;
s222, setting a range of a pulse wave peak and a signal minimum point;
s223, searching the peak of the pulse wave in the peak range of the pulse wave through frequency detection.
Further, detecting the vertex of the electrocardiogram includes the following steps:
s231, reducing signal amplitude of negative wave, positive wave, wavelet and descending wave by using double square function, increasing signal amplitude of peak wave, and extracting peak wave from electrocardiogram;
s232, searching the area where the peak point of the peak wave is located by using a preset waveform threshold value, and determining the position of the peak point of the peak wave by using a slope detection method.
Further, according to blood pressure evaluation data and body information, fuzzy reasoning is performed by using a classification recognition model based on a fuzzy control theory, and a blood pressure analysis result of a medical user is output, which comprises the following steps:
s31, taking systolic pressure and diastolic pressure, electrocardiogram, pulse wave and body information in blood pressure evaluation data as a classification factor set;
s32, establishing a classification level set for describing the health of the user, and fuzzifying by using a fuzzy language;
s33, setting membership of the classification factors to the classification level, and establishing a fuzzy relation matrix;
s34, calculating the weight of each classification factor in the classification factor set, and establishing a weight set;
s35, multiplying the classification level set by the fuzzy relation matrix to obtain a fuzzy comprehensive evaluation result vector;
s36, analyzing the fuzzy comprehensive evaluation result vector according to the maximum membership rule to obtain a blood pressure classification grade which is used as a blood pressure analysis result of the medical user.
Further, calculating the weight of each classification factor in the classification factor set, and establishing a weight set includes the following steps:
s341, decomposing the medical analysis problem into a target layer, a criterion layer, an index layer and other base layers by using an analytic hierarchy process, and respectively determining factors corresponding to the layers;
s342, assigning scores to each factor in the subordinate hierarchy according to the relative importance degree of the pairwise comparison between each factor in the subordinate hierarchy of each hierarchy, and obtaining a judgment matrix of the hierarchy;
s343, obtaining a feature vector corresponding to the maximum feature according to the judgment matrix, and taking the feature vector as the respective corresponding weight of the elements in each layer after passing the consistency test;
s344, the relative importance of each layer of factors to the previous stage is obtained, the comprehensive importance of each layer of factors about the medical analysis target is obtained from top to bottom from the uppermost layer, and all the classification factors are optimized and ordered.
The beneficial effects of the invention are as follows:
1. by fusing pulse signals, electrocardiosignals and patient body information, real-time monitoring is realized, the change of blood indexes of a patient is allowed to be continuously tracked, more comprehensive and accurate blood pressure evaluation data is provided by a collaborative calculation and fuzzy control theory, and the understanding of the health condition of the patient is facilitated; by means of fuzzy reasoning, the blood pressure analysis result of the patient can be accurately identified and output, doctors are helped to make more accurate diagnosis and treatment decisions, and through visual display and early warning functions, the patient and the doctors can more easily understand and track blood indexes and timely take preventive and intervention measures, so that the efficiency and individuation degree of medical management are improved.
2. By acquiring detailed physical information of a medical user, including identity, age, gender, height, weight, medical history and blood vessel blood information, more comprehensive personalized data is provided for subsequent blood pressure evaluation, and physiological characteristics of the user are considered; by wearing the piezoelectric pulse sensor and the electrocardiosignal, pulse signals and electrocardiosignals are monitored in real time, so that continuous physiological data acquisition is possible, and a more comprehensive monitoring result can be provided; the blood pressure instrument is utilized to acquire normal blood pressure data of a user and establish a pulse wave blood pressure formula, so that accurate assessment of blood pressure is improved, and influences of different physiological characteristics are considered.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a flow chart of a blood index non-invasive medical analysis method based on fuzzy control in accordance with an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, a non-invasive medical analysis method for blood index based on fuzzy control includes the following steps:
s1, acquiring physical information of a medical user, monitoring in real time through a non-invasive monitoring sensor worn by the medical user, and acquiring pulse signals and electrocardiosignals.
In the description of the present invention, acquiring physical information of a medical user, monitoring in real time by a non-invasive monitoring sensor worn by the medical user, and acquiring pulse signals and electrocardiosignals includes the steps of:
s11, acquiring physical information comprising the identity and physiological characteristics of the medical user, wherein the physical information comprises the identity, age, sex, height, weight, medical history and blood vessel information of the user.
And S12, respectively wearing the piezoelectric pulse sensor and the electrocardio sensor at the wrist and chest positions of the medical user for acquiring pulse signals and electrocardio signals of the medical user.
In the description of the invention, it is first necessary to select an appropriate position to wear the sensor. Typically, piezoelectric pulse sensors are worn on the wrist, as the wrist is where the pulse is most easily detected. An electrocardiograph is typically worn at the chest to capture cardiac electrical signals. The piezoelectric pulse sensor is mounted on the wrist of the medical user, typically secured to the wrist by means of a strap or patch. Meanwhile, attaching or mounting an electrocardiograph to the chest of a medical user generally involves attaching electrodes or wearing chest straps. Before wearing, it is necessary to ensure the accuracy and stability of the sensor, usually by means of a calibration procedure. Calibration may ensure that the signals captured by the sensors are of high quality and correlated with physiological characteristics. Once the sensor is worn and calibration is complete, data acquisition may begin. The piezoelectric pulse sensor will record the pulse signal of the medical user, while the electrocardiograph will record the cardiac electrical signal. These signals will be transmitted to a data processing system for further analysis.
S13, measuring normal blood pressure data of the medical user in a normal state by using a blood pressure meter.
S14, establishing a pulse wave blood pressure formula according to the normal blood pressure data and the pulse wave conduction time.
In the description of the present invention, establishing a pulse wave blood pressure formula according to blood pressure data and pulse wave transit time includes the steps of:
s141, measuring the pulse wave transmission distance between the piezoelectric pulse sensor and the electrocardio sensor worn by the medical user.
S142, establishing a pulse wave blood pressure formula by utilizing a mathematical relationship between the blood pressure data and the pulse wave conduction time, calculating the normal pulse wave conduction time of the medical user in a normal state by utilizing the normal blood pressure data, and verifying the pulse wave blood pressure formula by measuring the actual pulse wave conduction time.
The expression of the pulse wave blood pressure formula is as follows:
in the method, in the process of the invention,MTthe pulse wave transit time is indicated,Lthe pulse wave transmission distance is represented by the pulse wave transmission distance,ρthe density of the blood is indicated and,drepresents the inner diameter of the blood vessel,arepresents the thickness of the wall of the blood vessel,gindicating the acceleration of gravity and,BPrepresents the data of the blood pressure,Ethe elastic modulus at zero blood pressure is indicated,γmodulus representing the relationship between vascular pressure and elasticity.
S15, the piezoelectric pulse sensor and the electrocardio sensor transmit the acquired pulse signals and electrocardio signals to a data processing receiving end through a signal adjustment interface circuit.
After the piezoelectric pulse sensor and the electrocardiosignal collect pulse signals and electrocardiosignals, the signals are transmitted to a data processing receiving end for further processing and analysis through a signal adjustment interface circuit. The following is a detailed description of this process:
signal conditioning interface circuit: this is an important part for adjusting and processing the analog signal acquired from the sensor to make it suitable for digital processing. The interface circuit comprises functions of amplification, filtering, denoising, analog-to-digital conversion and the like so as to ensure the accuracy and the reliability of signals.
And (3) digital processing: after the signal conditioning interface circuit, the analog signal is converted to a digital signal. These digital signals may be recognized and processed by a computer or by a dedicated device. This stage typically involves a sampling and quantization process to convert the analog signal into discrete digital data.
And (3) data transmission: the digital signal can be transmitted to the data processing receiving end in a wired or wireless mode. Wireless transmission involves sending data to a receiving device using a wireless communication protocol, such as bluetooth or Wi-Fi.
S2, processing the pulse signals and the electrocardiosignals, generating pulse waves and electrocardiograms according to the reserved time length, and calculating blood pressure evaluation data of the medical user in a cooperative calculation mode.
In the description of the present invention, processing the pulse signal and the electrocardiograph signal, generating the pulse wave and the electrocardiograph according to the reserved time length, and calculating the blood pressure evaluation data of the medical user by using the cooperative calculation mode includes the following steps:
s21, filtering and noise reduction preprocessing is carried out on the pulse signals and the electrocardiosignals, and the pulse signals and the electrocardiosignals are respectively converted into pulse waves and electrocardiograms with uniform phase delay.
In the description of the present invention, filtering and noise reduction preprocessing is performed on pulse signals and electrocardiograph signals, and the pulse signals and the electrocardiograph signals are respectively converted into pulse waves and electrocardiographs with uniform phase delays, wherein the steps include:
s211, filtering the electrocardiosignal by using a 0.05Hz high-pass filter, and filtering the pulse signal by using a 0.03Hz high-pass filter, so as to ensure that the two waveforms have the same phase delay.
A 0.05Hz high pass filter is applied to the electrocardiographic signal to remove low frequency noise and dc components. Meanwhile, a high-pass filter of 0.03Hz is applied to the pulse signal, also in order to remove low-frequency noise and direct current components. These high pass filters ensure that the pulse signal has similar characteristics in the frequency domain as the electrocardiographic signal and has the same phase delay.
S212, eliminating distortion signals at the 1S position in the pulse signals and the electrocardiosignals.
The purpose of this procedure is to eliminate the distorted signal at the 1 second (1 s) position, to ensure that the signal acquired from the sensor is of high quality, free from noise or interference, and to be able to accurately reflect the physiological state of the medical user. The distorted signal may be due to various causes, such as electromagnetic interference, motion, or other external factors. This step therefore uses signal processing techniques to detect and filter out those outlier signals at the 1 second position.
S213, converting the pulse signals and the electrocardiosignals after filtering and elimination into the pulse waves and the electrocardiograms which are visually displayed according to the reserved time length of the displayable window.
S22, detecting the peak of the pulse wave.
In the description of the present invention, detecting the peak of the pulse wave includes the steps of:
s221, acquiring the amplitude range and the amplitude of the pulse wave.
First, it is necessary to determine waveform characteristics of the pulse wave, including the range of the amplitude and the magnitude of the amplitude. The amplitude is the distance between the peak and the trough of the waveform, while the amplitude is half the height of the waveform. This information helps define the peak of the pulse wave.
S222, setting the range of the peak of the pulse wave and the lowest point of the signal.
In pulse waveforms, it is necessary to define the range in which the vertices are located, i.e., within which time window to find the vertices. Typically, the peak of the pulse wave is at the highest point of the waveform, but to ensure accuracy, a small range needs to be defined to search for the peak. Furthermore, the nadir of the signal may also need to be considered to ensure that only one complete pulse wave is detected.
S223, searching the peak of the pulse wave in the peak range of the pulse wave through frequency detection.
Once the range of vertices is defined, signal processing techniques, such as frequency detection or derivative analysis, are used to search for pulse wave vertices within the range. Frequency detection is typically used to detect extreme points of a signal, thereby finding the vertices of the waveform.
S23, detecting the peak of the electrocardiogram.
In the description of the present invention, detecting vertices of an electrocardiogram includes the steps of:
s231, reducing the signal amplitude of the negative wave (Q-wave), the positive wave (T-wave), the wavelet (U-wave) and the descending wave (S-wave) by using a double square function, increasing the signal amplitude of the peak wave, and extracting the peak wave (R-wave) from the electrocardiogram.
S232, searching the area where the peak point of the peak wave is located by using a preset waveform threshold value, and determining the position of the peak point of the peak wave by using a slope detection method.
Once the signal amplitude is adjusted to emphasize R-wave, it is then necessary to search the ECG for the vertices of R-wave by setting a preset waveform threshold to identify areas likely to contain R-wave. Slope detection may then be used to determine the exact vertex position of R-wave. The slope detection method utilizes the slope change of the detection signal to find the extreme point of the waveform, thereby determining the vertex position.
S24, measuring pulse wave conduction time between pulse waves and the peak of an electrocardiogram, and calculating the systolic pressure of the medical user by using a pulse wave blood pressure formula (the blood pressure data in the pulse wave blood pressure formula is obtained by calculating by using the pulse wave conduction time between the peaks as input).
S25, detecting the valley points in the pulse wave and the electrocardiogram respectively, measuring the pulse wave conduction time between the pulse wave and the valley points in the electrocardiogram, and calculating the diastolic pressure of the medical user by using a pulse wave blood pressure formula (the blood pressure data in the pulse wave blood pressure formula obtained by calculation is the diastolic pressure by using the pulse wave conduction time between the valley points as input).
S26, taking the calculated systolic pressure and diastolic pressure as blood pressure evaluation data of the medical user in the current state.
S3, carrying out fuzzy reasoning by utilizing a classification recognition model based on a fuzzy control theory according to the blood pressure evaluation data and the body information, and outputting a blood pressure analysis result of the medical user.
In the description of the invention, according to blood pressure evaluation data and body information, fuzzy reasoning is carried out by utilizing a classification recognition model based on a fuzzy control theory, and a blood pressure analysis result of a medical user is output, which comprises the following steps:
s31, taking systolic pressure and diastolic pressure, electrocardiogram, pulse wave and body information in blood pressure evaluation data as classification factor sets.
S32, establishing a classification level set for describing the health of the user, and blurring by using a fuzzy language.
Establishing a classification level set describing the health of the user is one of the key steps of fuzzy control. This classification level set is typically designed to contain a set of different health states, each described using fuzzy language, to better address uncertainty and ambiguity. For example, the following set of classification levels may be created:
1. is very healthy;
2. health;
3. normal;
4. slight abnormalities;
5. moderate anomalies;
6. severe abnormalities;
each level may be described using a fuzzy language, such as:
"very healthy" can be described as "very high" with a fuzzy set.
"health" may be described as "high" with a fuzzy set.
"normal" may be described as "medium" with a fuzzy set.
A "slight anomaly" may be described as "low" with a fuzzy set.
A "moderate anomaly" may be described as "lower" by a fuzzy set.
"severe anomalies" can be described as "very low" with a fuzzy set.
The fuzzy language herein describes the membership of each health state. For example, for a particular patient, they may have a membership of 0.7 in the "healthy" state of health, and a membership of 0.3 in the "mild abnormality", which means they are closer to "healthy".
The use of fuzzy languages helps to address the ambiguity of health conditions, as the health status of different patients may not be a clear, precise numerical value, but rather a fuzzy concept. This fuzzy description allows the fuzzy control to be better adapted to individual differences of different patients.
S33, setting membership of the classification factors to the classification level, and establishing a fuzzy relation matrix.
Membership means the degree of attribution of each classification factor to each classification class. The following is the step of establishing a fuzzy relation matrix:
determining classification factors and classification grades: first, factors for classification are determined, which include individual components of blood pressure evaluation data, an electrocardiogram, pulse waves, and body information. At the same time, different classification levels are well defined, such as "very healthy", "normal", etc.
Defining membership functions for each classification factor: for each classification factor, its membership functions are defined, which describe the degree of membership of the factor to each classification level. Membership functions are typically expressed in the form of triangles, trapezoids, or bell-shaped curves.
Setting membership degree: a membership value is determined for each class level of each classification factor. These values are typically between 0 and 1, representing membership between the classification factor and the classification level. For example, for a classification factor of systolic blood pressure, the membership in the "healthy" class might be 0.8, while the membership in the "normal" class might be 0.4.
Establishing a fuzzy relation matrix: and constructing a fuzzy relation matrix by using the set membership value, wherein the rows of the matrix represent classification factors, the columns represent classification grades, and each element represents membership between the corresponding factor and the grade.
Applying the maximum membership principle: and when fuzzy reasoning is carried out, determining the classification level corresponding to each classification factor by using the maximum membership rule. For each factor, the class level with the highest membership value is selected as the class level for that factor.
S34, calculating the weight of each classification factor in the classification factor set, and establishing a weight set.
In the description of the present invention, calculating weights of respective classification factors in a classification factor set, and establishing a weight set includes the steps of:
s341, decomposing the medical analysis problem into a target layer, a criterion layer, an index layer and the like by using an analytic hierarchy process, and respectively determining factors corresponding to each layer.
Decomposing the medical analysis problem into a target layer, a criterion layer, an index layer and other base layers by using an analytic hierarchy process (Analytic Hierarchy Process, AHP), and respectively determining the factors corresponding to the layers as follows:
1. target layer (Objective Level): at this level, the overall goal is blood pressure analysis of the medical user. This is the highest level of overall problem.
2. Criterion layer (Criterion Level): the criteria layer contains key factors or criteria that affect the blood pressure analysis. For blood pressure analysis, it may include:
systolic and diastolic pressures: these two indices are used to assess blood pressure conditions.
Electrocardiogram: the characteristics of an electrocardiogram may be related to blood pressure and thus also an important criterion.
Pulse wave: the pulse wave characteristics may provide information about vascular function and may also be related to blood pressure.
Physical information: including age, sex, height, weight, etc., can also affect blood pressure.
3. Index layer (Indicator Level): the index layer further refines the criteria and breaks it down into specific indices or elements. For example:
a systolic pressure;
diastolic blood pressure;
electrocardiographic features (e.g., R-wave, Q, T, U, S-wave, etc.);
pulse wave characteristics;
age, sex, height, weight, etc.;
4. base layer (element Level): the base layer contains the most specific factors, typically data or information for specific evaluation. For example, the base layer may include actual blood pressure values, electrocardiogram data, pulse wave data, and physical characteristic data for each medical user.
By this layering approach, the relationship between each hierarchy, and the importance of each factor to achieve the overall goal, can be more clearly understood. This helps to determine the weights of the factors for subsequent fuzzy reasoning and blood pressure analysis of the medical user. The analytic hierarchy process can also determine weights through comparison of expert opinions to improve the objectivity and accuracy of decisions.
S342, according to the relative importance degree of the factor of each subordinate level of each level, giving score to each factor in the subordinate level to obtain the judging matrix of the level.
S343, obtaining a feature vector corresponding to the maximum feature according to the judgment matrix, and taking the feature vector as the corresponding weight of each element in each layer after passing the consistency test.
S344, the relative importance of each layer of factors to the previous stage is obtained, the comprehensive importance of each layer of factors about the medical analysis target is obtained from top to bottom from the uppermost layer, and all the classification factors are optimized and ordered.
And S35, multiplying the classification level set by the fuzzy relation matrix to obtain a fuzzy comprehensive evaluation result vector.
S36, analyzing the fuzzy comprehensive evaluation result vector according to the maximum membership rule to obtain a blood pressure classification grade which is used as a blood pressure analysis result of the medical user.
And finally, determining which classification level has the highest membership degree according to the maximum membership degree principle. This level will be considered as a result of the blood pressure analysis of the medical user reflecting their health status.
And S4, visually displaying the pulse wave, electrocardiogram and blood pressure analysis results obtained by real-time monitoring, and carrying out early warning and reminding in time according to a preset early warning threshold.
In summary, by means of the above technical solution of the present invention, by fusing the pulse signal, the electrocardiograph signal and the patient body information, real-time monitoring is realized, the blood index change of the patient is allowed to be continuously tracked, and more comprehensive and accurate blood pressure evaluation data is provided by the collaborative calculation and fuzzy control theory, which is helpful for improving understanding of the health condition of the patient; by means of fuzzy reasoning, the blood pressure analysis result of the patient can be accurately identified and output, doctors are helped to make more accurate diagnosis and treatment decisions, and through visual display and early warning functions, the patient and the doctors can more easily understand and track blood indexes and timely take preventive and intervention measures, so that the efficiency and individuation degree of medical management are improved. By acquiring detailed physical information of a medical user, including identity, age, gender, height, weight, medical history and blood vessel blood information, more comprehensive personalized data is provided for subsequent blood pressure evaluation, and physiological characteristics of the user are considered; by wearing the piezoelectric pulse sensor and the electrocardiosignal, pulse signals and electrocardiosignals are monitored in real time, so that continuous physiological data acquisition is possible, and a more comprehensive monitoring result can be provided; the blood pressure instrument is utilized to acquire normal blood pressure data of a user and establish a pulse wave blood pressure formula, so that accurate assessment of blood pressure is improved, and influences of different physiological characteristics are considered.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
Claims (10)
1. A non-invasive medical analysis method of blood index based on fuzzy control, characterized in that the method comprises the following steps:
s1, acquiring physical information of a medical user, monitoring in real time through a non-invasive monitoring sensor worn by the medical user, and acquiring pulse signals and electrocardiosignals;
s2, processing the pulse signals and the electrocardiosignals, generating pulse waves and electrocardiograms according to reserved time length, and calculating blood pressure evaluation data of the medical user in a cooperative calculation mode;
s3, carrying out fuzzy reasoning by utilizing a classification recognition model based on a fuzzy control theory according to the blood pressure evaluation data and the body information, and outputting a blood pressure analysis result of the medical user;
and S4, visually displaying the pulse wave, the electrocardiogram and the blood pressure analysis result obtained by real-time monitoring, and carrying out early warning and reminding in time according to a preset early warning threshold.
2. The non-invasive medical analysis method for blood index based on fuzzy control of claim 1, wherein the acquiring the physical information of the medical user, the real-time monitoring is performed by the non-invasive monitoring sensor worn by the medical user, and the acquiring the pulse signal and the electrocardiosignal comprises the following steps:
s11, acquiring physical information comprising the identity and physiological characteristics of a medical user, wherein the physical information comprises the identity, age, sex, height, weight, medical history and blood vessel information of the user;
s12, respectively wearing a piezoelectric pulse sensor and an electrocardio sensor at the wrist and chest positions of the medical user, and acquiring pulse signals and electrocardio signals of the medical user;
s13, measuring normal blood pressure data of the medical user in a conventional state by using a blood pressure meter;
s14, establishing a pulse wave blood pressure formula according to the normal blood pressure data and the pulse wave conduction time;
s15, the piezoelectric pulse sensor and the electrocardio sensor are connected through a signal adjustment interface circuit, and the acquired pulse signals and the electrocardio signals are sent to a data processing receiving end.
3. The non-invasive medical analysis method according to claim 2, wherein the establishing a pulse wave blood pressure formula according to the normal blood pressure data and pulse wave transit time comprises the steps of:
s141, measuring pulse wave transmission distance between the piezoelectric pulse sensor worn by the medical user and the electrocardio sensor;
s142, establishing a pulse wave blood pressure formula by utilizing a mathematical relationship between blood pressure data and pulse wave conduction time, calculating normal pulse wave conduction time of the medical user in a normal state by utilizing the normal blood pressure data, and verifying the pulse wave blood pressure formula by measuring actual pulse wave conduction time.
4. A non-invasive medical analysis method of blood index based on fuzzy control according to claim 3, wherein the expression of the pulse wave blood pressure formula is:；
in the method, in the process of the invention,MTrepresenting pulse transit time;
Lrepresenting the pulse wave transmission distance;
ρrepresents blood density;
drepresents the inside diameter of the blood vessel;
arepresents the vessel wall thickness;
grepresenting gravitational acceleration;
BPrepresenting blood pressure data;
Eelastic modulus at zero blood pressure;
γmodulus representing the relationship between vascular pressure and elasticity.
5. A non-invasive medical analysis method according to claim 3, wherein the processing the pulse signal and the electrocardiographic signal to generate pulse wave and electrocardiogram according to a predetermined time length, and calculating the blood pressure evaluation data of the medical user by using a cooperative calculation method comprises the following steps:
s21, filtering and noise reduction preprocessing is carried out on the pulse signals and the electrocardiosignals, and the pulse signals and the electrocardiosignals are respectively converted into pulse waves and electrocardiograms with uniform phase delay;
s22, detecting the peak of the pulse wave;
s23, detecting the vertex of the electrocardiogram;
s24, measuring pulse wave conduction time between the pulse wave and the peak of the electrocardiogram, and calculating the systolic pressure of the medical user by using the pulse wave blood pressure formula;
s25, detecting the pulse wave and the valley point in the electrocardiogram respectively, measuring the pulse wave conduction time between the pulse wave and the valley point of the electrocardiogram, and calculating the diastolic pressure of the medical user by using the pulse wave blood pressure formula;
s26, taking the calculated systolic pressure and diastolic pressure as blood pressure evaluation data in the current state of the medical user.
6. The non-invasive medical analysis method according to claim 5, wherein the filtering and noise reduction preprocessing is performed on the pulse signal and the electrocardiograph signal, and the filtering and noise reduction preprocessing is respectively converted into pulse waves and electrocardiographs with uniform phase delays, and the steps comprise:
s211, filtering the electrocardiosignal by using a 0.05Hz high-pass filter, and filtering the pulse signal by using a 0.03Hz high-pass filter, so as to ensure that the two waveforms have the same phase delay;
s212, eliminating distortion signals at the 1S position in the pulse signal and the electrocardiosignal at the same time;
s213, converting the pulse signals and the electrocardiosignals after filtering elimination into pulse waves and electrocardiograms which are visually displayed according to the reserved time length of the displayable window.
7. The non-invasive medical analysis method according to claim 6, wherein the detecting the peak of the pulse wave comprises the steps of:
s221, acquiring the amplitude range and the amplitude of the pulse wave;
s222, setting a range of the peak of the pulse wave and a signal minimum point;
s223, searching the peak of the pulse wave in the peak range of the pulse wave through frequency detection.
8. The non-invasive medical analysis method according to claim 7, wherein the detecting the vertex of the electrocardiogram comprises the steps of:
s231, reducing signal amplitude of negative waves, positive waves, wavelets and descending waves by using a double square function, increasing signal amplitude of peak waves, and extracting the peak waves from the electrocardiogram;
s232, searching the area where the peak point of the peak wave is located by using a preset waveform threshold value, and determining the position of the peak point of the peak wave by using a slope detection method.
9. The non-invasive medical analysis method according to claim 2, wherein the outputting the blood pressure analysis result of the medical user by fuzzy reasoning using a classification recognition model based on a fuzzy control theory according to the blood pressure evaluation data and the body information comprises the steps of:
s31, taking the systolic pressure and diastolic pressure, the electrocardiogram, the pulse wave and the body information in the blood pressure evaluation data as classification factor sets;
s32, establishing a classification level set for describing the health of the user, and fuzzifying by using a fuzzy language;
s33, setting membership of the classification factors to the classification level, and establishing a fuzzy relation matrix;
s34, calculating the weight of each classification factor in the classification factor set, and establishing a weight set;
s35, multiplying the classification level set by the fuzzy relation matrix to obtain a fuzzy comprehensive evaluation result vector;
s36, analyzing the fuzzy comprehensive evaluation result vector according to the maximum membership rule to obtain a blood pressure classification grade which is used as a blood pressure analysis result of the medical user.
10. The non-invasive medical analysis method according to claim 9, wherein the calculating weights of the respective classification factors in the classification factor set and establishing a weight set comprises the steps of:
s341, decomposing the medical analysis problem into a target layer, a criterion layer, an index layer and other base layers by using an analytic hierarchy process, and respectively determining factors corresponding to the layers;
s342, assigning scores to each factor in the subordinate hierarchy according to the relative importance degree of the pairwise comparison between each factor in the subordinate hierarchy of each hierarchy, and obtaining a judgment matrix of the hierarchy;
s343, obtaining a feature vector corresponding to the maximum feature according to the judgment matrix, and taking the feature vector as the respective corresponding weight of the elements in each layer after passing the consistency test;
s344, the relative importance of each layer of factors to the previous stage is obtained, the comprehensive importance of each layer of factors about the medical analysis target is obtained from top to bottom from the uppermost layer, and all the classification factors are optimized and ordered.
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