CN110916624A - Intelligent pulse feeling method and system for detecting vascular resistance - Google Patents
Intelligent pulse feeling method and system for detecting vascular resistance Download PDFInfo
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- CN110916624A CN110916624A CN201911157222.3A CN201911157222A CN110916624A CN 110916624 A CN110916624 A CN 110916624A CN 201911157222 A CN201911157222 A CN 201911157222A CN 110916624 A CN110916624 A CN 110916624A
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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
- A61B5/02007—Evaluating blood vessel condition, e.g. elasticity, compliance
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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
- A61B5/02028—Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
Abstract
The invention discloses an intelligent pulse feeling method and system for detecting vascular resistance, wherein the method comprises the following steps: collecting pulse wave data; performing time domain analysis on the acquired pulse wave data to obtain characteristic parameters; the characteristic parameters comprise a dominant wave amplitude and a central notch descending amplitude; calculating according to the characteristic parameters and a preset rule to obtain a vascular resistance coefficient; evaluating the vascular resistance coefficient according to a preset rule to obtain a vascular resistance evaluation result; the method and the system detect the pulse wave signals through the pulse condition acquisition equipment, perform time domain analysis on the waveforms of the pulse wave signals, further calculate the arterial blood vessel resistance coefficient by using the relevant characteristic values, judge the blood vessel hardness of the patient according to the arterial blood vessel resistance coefficient, contribute to predicting or diagnosing the potential risk of cardiovascular diseases and strive for precious time for preventing and treating the cardiovascular diseases; the method and the system are simple to operate, do not damage the human body, and can reflect the peripheral vascular resistance of the user quickly and intuitively.
Description
Technical Field
The invention relates to the technical field of medical treatment, in particular to an intelligent pulse feeling method and system for detecting vascular resistance.
Background
Arteriosclerosis is a risk signal for cardiovascular diseases and aging, and causes arterial system diseases, resulting in increased systolic pressure, decreased diastolic pressure, increased left ventricular load, coronary perfusion disorder, often endangering human life. However, the lesion level of arteriosclerosis is difficult to find in the clinic in time, so that the optimal treatment time is missed. Diagnosis of arteriosclerosis may be achieved by detecting a vascular resistance parameter. Vascular resistance, the total resistance to blood flow in the vascular system, occurs mostly in arterioles, particularly arterioles.
At present, the means for clinically evaluating the vascular resistance mainly comprise vascular ultrasound, arterial angiography, magnetic resonance angiography and the like, and the methods pay attention to morphological change. Although the methods of vascular ultrasound, magnetic resonance and the like can also obtain the functional status of arterial vascular resistance by observing the change of arterial lumen diameter in the systolic period and the diastolic period, and play an irreplaceable role in clinic, the changes detected by the methods are always in the state of irreversible damage to blood vessels with relatively obvious organic lesions, so that the effect of the blood vessels on preventing cardiovascular diseases is limited. Meanwhile, the examination has the characteristics of strong operation specificity, high price, invasiveness and the like, so that the examination is not suitable for screening the prompt arteriosclerosis in the population.
Disclosure of Invention
In order to solve the problems of delayed detection of vascular resistance function and high price with strong specialization existing in the background technology, the invention provides an intelligent pulse diagnosis method and system for detecting vascular resistance, wherein the method and system detect pulse wave signals through pulse condition acquisition equipment, perform time domain analysis on waveforms of the pulse wave signals, further calculate an arterial vascular resistance coefficient by using a relevant characteristic value, and judge the vascular hardness of a patient according to the arterial vascular resistance coefficient, and the intelligent pulse diagnosis method for detecting the vascular resistance comprises the following steps:
collecting pulse wave data;
performing time domain analysis on the acquired pulse wave data to obtain characteristic parameters; the characteristic parameters comprise a dominant wave amplitude and a central notch descending amplitude;
calculating according to the characteristic parameters and a preset rule to obtain a vascular resistance coefficient;
and evaluating the vascular resistance coefficient according to a preset rule to obtain a vascular resistance evaluation result.
Further, performing time domain analysis on the acquired pulse wave data to obtain characteristic parameters, including:
filtering the pulse wave data;
analyzing the pulse wave data after the filtering processing, and intercepting a complete cycle;
and identifying the characteristic comprising the main wave crest and the strait in the period, and obtaining the characteristic parameter comprising the main wave amplitude and the strait amplitude.
Further, analyzing the pulse wave data after the filtering processing, intercepting a plurality of complete cycles, and obtaining a group of characteristic parameters corresponding to each cycle;
calculating and obtaining a vascular resistance coefficient corresponding to each period according to each group of characteristic parameters;
calculating to obtain the average value of the multiple groups of vascular resistance coefficients, and obtaining a vascular resistance evaluation result according to the vascular resistance coefficient average value.
Further, the preset rule for calculating the vascular resistance coefficient is as follows:
p＝k*h4/h1+a
wherein h is1Amplitude of the dominant wave, h4For the amplitude of the central notch, k and a are correction coefficients.
Further, the evaluation of the vascular resistance coefficient according to a preset rule comprises:
analyzing a linear relation between the vascular resistance coefficient and the vascular resistance condition according to historical data, setting N thresholds for the vascular resistance coefficient according to the vascular resistance condition in the historical data, and grading to obtain N +1 progressive grades;
and comparing the vascular resistance coefficient with the N thresholds, determining the grade of the vascular resistance coefficient, and outputting the evaluation result of the grade.
The intelligent pulse feeling system for detecting vascular resistance comprises:
the acquisition unit is used for acquiring the pulse wave data;
the time domain analysis unit is used for carrying out time domain analysis on the acquired pulse wave data to obtain characteristic parameters; the characteristic parameters comprise a dominant wave amplitude and a central notch descending amplitude;
the vascular resistance coefficient calculation unit is used for calculating and obtaining a vascular resistance coefficient according to the characteristic parameters and a preset rule;
and the evaluation unit is used for evaluating the vascular resistance coefficient according to a preset rule to obtain a vascular resistance evaluation result.
Further, the time domain analysis unit is configured to perform filtering processing on the pulse wave data;
the time domain analysis unit analyzes the pulse wave data after the filtering processing and intercepts a complete cycle;
the time domain analysis unit identifies the features including the main wave peak and the strait in the period and obtains feature parameters including the main wave amplitude and the strait amplitude.
Further, the time domain analysis unit is configured to analyze the filtered pulse wave data, intercept multiple complete cycles, and obtain a set of characteristic parameters corresponding to each cycle;
the vascular resistance coefficient calculation unit calculates and obtains a vascular resistance coefficient corresponding to each period according to each group of characteristic parameters;
the evaluation unit calculates and obtains the mean value of a plurality of groups of vascular resistance coefficients, and obtains the vascular resistance evaluation result according to the mean value of the vascular resistance coefficients.
Further, the preset rule for calculating the vascular resistance coefficient by the vascular resistance coefficient calculating unit is as follows:
p＝k*h4/h1+a
wherein h is1Amplitude of the dominant wave, h4For the amplitude of the central notch, k and a are correction coefficients.
Further, the evaluation unit is used for performing linear relation analysis on the vascular resistance coefficient and the vascular resistance condition according to historical data, setting N thresholds on the vascular resistance coefficient according to the vascular resistance condition in the historical data, and dividing the grades to obtain N +1 progressive grades;
and the evaluation unit compares the vascular resistance coefficient with the N thresholds, determines the grade of the vascular resistance coefficient, and outputs the evaluation result of the grade.
The invention has the beneficial effects that: the technical scheme of the invention provides an intelligent pulse feeling method and system for detecting vascular resistance; the method and the system detect the pulse wave signals through the pulse condition acquisition equipment, perform time domain analysis on the waveform of the pulse wave signals, further calculate the arterial blood vessel resistance coefficient by using the relevant characteristic values, judge the blood vessel hardness of the patient according to the arterial blood vessel resistance coefficient, contribute to predicting or diagnosing the potential risk of cardiovascular diseases and strive for precious time for preventing and treating the cardiovascular diseases. The method and the system are simple to operate, do not damage the human body, and can reflect the peripheral vascular resistance of the user quickly and intuitively.
Drawings
A more complete understanding of exemplary embodiments of the present invention may be had by reference to the following drawings in which:
FIG. 1 is a flow chart of an intelligent pulse feeling method for detecting vascular resistance in accordance with an embodiment of the present invention;
FIG. 2 is a pulse diagram of the relationship between the amplitude and the duration of a pulse wave during a cycle in accordance with an embodiment of the present invention;
fig. 3 is a block diagram of an intelligent pulse feeling system for detecting vascular resistance according to an embodiment of the present invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same units/elements are denoted by the same reference numerals.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
FIG. 1 is a flow chart of an intelligent pulse feeling method for detecting vascular resistance in accordance with an embodiment of the present invention; as shown in fig. 1, the method includes:
the acquisition of the pulse wave data may be achieved in a number of ways, for example:
the first method is as follows: measuring an electrocardiosignal, analyzing the electrocardiosignal and extracting pulse wave data;
the second method comprises the following steps: the pressure sensor is arranged on the wrist where the radial artery of the user to be measured passes through, the influence of pulse wave fluctuation of the user on the pressure sensor is measured, and then pulse wave data are obtained.
The third method comprises the following steps: the pulse wave data is obtained by adopting a photoelectric volume method, and a sensor used by the pulse wave data acquisition device consists of a light source and a photoelectric converter which are fixed on the finger or the earlobe of a patient through a bandage or a clip. The light source is generally a light emitting diode of a certain wavelength (500nm to 700nm) selective for oxygen and hemoglobin in arterial blood. When light beam penetrates through peripheral blood vessel of human body, the light transmittance of the light beam is changed due to blood congestion volume change of artery pulsation, and at the moment, the light reflected by human body tissue is received by the photoelectric converter, converted into electric signal, amplified and output, and pulse wave data is obtained. Step 120, performing time domain analysis on the acquired pulse wave data to obtain characteristic parameters; the characteristic parameters comprise a dominant wave amplitude and a central notch descending amplitude;
the time domain analysis of the acquired pulse wave data specifically includes:
step 121, filtering the pulse wave data;
the pair filtering processing comprises the steps of removing abnormal values in the pulse wave data through a jitter marking method and removing the influence of respiration on the pulse wave data through a low-pass filtering mode;
furthermore, the filtering processing can also be used for filtering the pulse wave data through a sliding window smoothing processing method, a wavelet threshold denoising method and the like to obtain smooth and clear pulse wave data;
step 122, analyzing the pulse wave data after the filtering processing, and intercepting a complete cycle;
as shown in fig. 2, the one complete pulse cycle has a plurality of characteristic parameters, which specifically include: dominant wave amplitude h1Amplitude of the main channel h2Amplitude of the wave before the dicrotic pulse h3Amplitude of the central isthmus h4Amplitude of the heavy pulse wave h5And the time value t from the starting point of the pulse wave to each peak and valley point1、t2、t3、t4、t5And a period of pulsation t.
Dominant wave amplitude h1The height from the peak of the main wave to the baseline of the pulse wave pattern (when the baseline is parallel to the time axis). Mainly reflects the blood ejection function of the left ventricle and the compliance of the aorta, namely h is the state of strong contractility of the left ventricle and good compliance of the aorta1High, otherwise small.
Dominant notch amplitude h2The amplitude of a valley between the dominant wave and the prepulse waveform. Its physiological significance and h3Consistent and often neglected in pulse map analysis.
Amplitude h of the wave before dicrotic3The height from the peak top to the baseline of the pulse wave chart before the dicrotic pulse. Mainly reflecting the elasticity and peripheral resistance of arterial vessels. Such as arterial vessels due to high wall tension, orHardening, or increased peripheral resistance, can cause h3The amplitude increases. The elevation of the pulse wave is generally accompanied with the advance of the time phase, which reflects the increase of the conduction speed of the pulse reflection wave in the state of high tension and high resistance of the artery vessel.
Amplitude h of the descending gorges4The height from the bottom of the descending canyon to the baseline of the pulse oscillogram. The height of the descending isthmus mainly reflects the peripheral resistance of the arterial blood vessels, and the peripheral resistance is increased and expressed as h4Increasing; and vice versa.
Amplitude of the dicrotic wave h5The height between the baseline parallel lines from the top of the dicrotic peak to the bottom of the descending canyon. Amplitude of the dicrotic wave h5Mainly reflecting the elasticity (compliance) of the aorta and the aortic valve function, when the aorta compliance is reduced, h5Decrease, or hardening of the aortic valve, incomplete closure h5It can be 0 (the peak of the dicrotic wave is at the same level as the bottom of the descending canyon) or even negative (the peak of the dicrotic wave is lower than the bottom of the descending canyon).
t1Is the time value from the start point of the pulse diagram to the main wave peak point. t is t1Corresponding to the rapid ejection phase of the left ventricle.
t2The chronaxy from the starting point of the pulse pattern to the main channel.
t3Is the time value between the starting point of the pulse diagram and the wave before the dicrotic pulse.
t4The time value, t, between the starting point of the pulse diagram and the descending isthmus4Corresponding to the systolic phase of the left ventricle.
t5For the time between descending the isthmus and the ending point of the pulse diagram, t5Corresponding to the diastole of the left ventricle.
t is the time value from the starting point to the ending point of the pulse diagram. Corresponding to one cardiac cycle of the left ventricle, also known as the pulsatile cycle. But when atrial fibrillation, or extrasystole, the pulse pattern does not coincide exactly with the cardiac cycle of the electrocardiogram.
Step 123, identifying the features including the main wave crest and the strait in the period, and obtaining feature parameters including the main wave amplitude and the strait amplitude;
namely to obtain theDominant wave amplitude h1And the amplitude h of the central isthmus4。
the preset rule for calculating the vascular resistance coefficient is as follows:
p＝k*h4/h1+a
wherein h is1Amplitude of the dominant wave, h4For the amplitude of the central notch, k and a are correction coefficients.
And 140, evaluating the vascular resistance coefficient according to a preset rule to obtain a vascular resistance evaluation result.
The evaluating of the vascular resistance coefficient according to the preset rule specifically comprises:
step 141, performing linear relation analysis on the vascular resistance coefficient and the vascular resistance condition according to the historical data, setting N thresholds on the vascular resistance coefficient according to the vascular resistance condition in the historical data, and dividing the thresholds into levels to obtain N +1 progressive levels;
and 142, comparing the vascular resistance coefficient with the N thresholds, determining the grade of the vascular resistance coefficient, and outputting the evaluation result of the grade.
In this embodiment, the levels may be classified into four levels, i.e., a lower level, a medium level, a higher level, and an excessively high level, by using three thresholds, and it is determined which level the blood vessel resistance coefficient belongs to.
Furthermore, in order to improve the accuracy of calculation and prevent inaccurate measurement caused by abnormal conditions of one period, when the pulse wave data after filtering processing is analyzed, a plurality of complete periods are intercepted, and a group of characteristic parameters corresponding to each period are obtained;
calculating and obtaining a vascular resistance coefficient corresponding to each period according to each group of characteristic parameters;
calculating to obtain the average value of the multiple groups of vascular resistance coefficients, and obtaining a vascular resistance evaluation result according to the vascular resistance coefficient average value.
Fig. 3 is a block diagram of an intelligent pulse feeling system for detecting vascular resistance according to an embodiment of the present invention. As shown in fig. 3, the system includes:
an acquisition unit 310, wherein the acquisition unit 310 is used for acquiring pulse wave data;
the time domain analysis unit 320 is configured to perform time domain analysis on the acquired pulse wave data to obtain a feature parameter; the characteristic parameters comprise a dominant wave amplitude and a central notch descending amplitude;
further, the time domain analyzing unit 320 is configured to perform filtering processing on the pulse wave data;
the time domain analysis unit 320 analyzes the filtered pulse wave data, and intercepts a complete cycle;
the time domain analysis unit 320 identifies the features including the main peak and the strait in the period, and obtains the feature parameters including the main peak amplitude and the strait amplitude.
A blood vessel resistance coefficient calculating unit 330, wherein the blood vessel resistance coefficient calculating unit 330 is configured to calculate and obtain a blood vessel resistance coefficient according to the characteristic parameter and a preset rule;
further, the preset rule for the blood vessel resistance coefficient calculation unit 330 to calculate the blood vessel resistance coefficient is as follows:
p＝k*h4/h1+a
wherein h is1Amplitude of the dominant wave, h4For the amplitude of the central notch, k and a are correction coefficients.
The evaluation unit 340 is used for evaluating the vascular resistance coefficient according to a preset rule to obtain a vascular resistance evaluation result.
Further, the evaluation unit 340 is configured to perform linear relationship analysis on the vascular resistance coefficient and the vascular resistance condition according to the historical data, set N thresholds for the vascular resistance coefficient according to the vascular resistance condition in the historical data, and classify the vascular resistance coefficient to obtain N +1 progressive grades;
the evaluation unit 340 compares the vascular resistance coefficient with the N thresholds, determines the level of the vascular resistance coefficient, and outputs the evaluation result of the level.
Further, the time domain analyzing unit 320 is configured to analyze the filtered pulse wave data, intercept multiple complete cycles, and obtain a set of characteristic parameters corresponding to each cycle;
the vascular resistance coefficient calculation unit 330 calculates and obtains a vascular resistance coefficient corresponding to each period according to each group of characteristic parameters;
the evaluation unit 340 calculates and obtains a mean value of a plurality of sets of vascular resistance coefficients, and obtains a vascular resistance evaluation result according to the mean value of the vascular resistance coefficients.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the disclosure may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. Reference to step numbers in this specification is only for distinguishing between steps and is not intended to limit the temporal or logical relationship between steps, which includes all possible scenarios unless the context clearly dictates otherwise.
Moreover, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the disclosure and form different embodiments. For example, any of the embodiments claimed in the claims can be used in any combination.
Various component embodiments of the disclosure may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. The present disclosure may also be embodied as device or system programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present disclosure may be stored on a computer-readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the disclosure, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The disclosure may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several systems, several of these systems may be embodied by one and the same item of hardware.
The foregoing is directed to embodiments of the present disclosure, and it is noted that numerous improvements, modifications, and variations may be made by those skilled in the art without departing from the spirit of the disclosure, and that such improvements, modifications, and variations are considered to be within the scope of the present disclosure.
Claims (10)
1. An intelligent pulse feeling method for detecting vascular resistance, the method comprising:
collecting pulse wave data;
performing time domain analysis on the acquired pulse wave data to obtain characteristic parameters; the characteristic parameters comprise a dominant wave amplitude and a central notch descending amplitude;
calculating according to the characteristic parameters and a preset rule to obtain a vascular resistance coefficient;
and evaluating the vascular resistance coefficient according to a preset rule to obtain a vascular resistance evaluation result.
2. The method of claim 1, wherein the time domain analyzing the acquired pulse wave data to obtain the characteristic parameters comprises:
filtering the pulse wave data;
analyzing the pulse wave data after the filtering processing, and intercepting a complete cycle;
and identifying the characteristic comprising the main wave crest and the strait in the period, and obtaining the characteristic parameter comprising the main wave amplitude and the strait amplitude.
3. The method of claim 2, wherein:
analyzing the pulse wave data after filtering processing, intercepting a plurality of complete cycles, and obtaining a group of characteristic parameters corresponding to each cycle;
calculating and obtaining a vascular resistance coefficient corresponding to each period according to each group of characteristic parameters;
calculating to obtain the average value of the multiple groups of vascular resistance coefficients, and obtaining a vascular resistance evaluation result according to the vascular resistance coefficient average value.
4. The method of claim 1, wherein: the preset rule for calculating the vascular resistance coefficient is as follows:
p＝k*h4/h1+a
wherein h is1Amplitude of the dominant wave, h4For the amplitude of the central notch, k and a are correction coefficients.
5. The method of claim 1, wherein evaluating the vascular resistance coefficient according to a predetermined rule comprises:
analyzing a linear relation between the vascular resistance coefficient and the vascular resistance condition according to historical data, setting N thresholds for the vascular resistance coefficient according to the vascular resistance condition in the historical data, and grading to obtain N +1 progressive grades;
and comparing the vascular resistance coefficient with the N thresholds, determining the grade of the vascular resistance coefficient, and outputting the evaluation result of the grade.
6. An intelligent pulse feeling system for detecting vascular resistance, the system comprising:
the acquisition unit is used for acquiring the pulse wave data;
the time domain analysis unit is used for carrying out time domain analysis on the acquired pulse wave data to obtain characteristic parameters; the characteristic parameters comprise a dominant wave amplitude and a central notch descending amplitude;
the vascular resistance coefficient calculation unit is used for calculating and obtaining a vascular resistance coefficient according to the characteristic parameters and a preset rule;
and the evaluation unit is used for evaluating the vascular resistance coefficient according to a preset rule to obtain a vascular resistance evaluation result.
7. The system of claim 6, wherein:
the time domain analysis unit is used for filtering the pulse wave data;
the time domain analysis unit analyzes the pulse wave data after the filtering processing and intercepts a complete cycle;
the time domain analysis unit identifies the features including the main wave peak and the strait in the period and obtains feature parameters including the main wave amplitude and the strait amplitude.
8. The system of claim 7, wherein:
the time domain analysis unit is used for analyzing the pulse wave data after the filtering processing, intercepting a plurality of complete cycles and obtaining a group of characteristic parameters corresponding to each cycle;
the vascular resistance coefficient calculation unit calculates and obtains a vascular resistance coefficient corresponding to each period according to each group of characteristic parameters;
the evaluation unit calculates and obtains the mean value of a plurality of groups of vascular resistance coefficients, and obtains the vascular resistance evaluation result according to the mean value of the vascular resistance coefficients.
9. The system of claim 6, wherein: the preset rule of the blood vessel resistance coefficient calculation unit for calculating the blood vessel resistance coefficient is as follows:
p＝k*h4/h1+a
wherein h is1Amplitude of the dominant wave, h4For the amplitude of the central notch, k and a are correction coefficients.
10. The system of claim 6, wherein:
the evaluation unit is used for carrying out linear relation analysis on the vascular resistance coefficient and the vascular resistance condition according to historical data, setting N thresholds on the vascular resistance coefficient according to the vascular resistance condition in the historical data, and grading to obtain N +1 progressive grades;
and the evaluation unit compares the vascular resistance coefficient with the N thresholds, determines the grade of the vascular resistance coefficient, and outputs the evaluation result of the grade.
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