CN111214218B - Multi-physiological parameter detection equipment - Google Patents
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Abstract
The embodiment of the disclosure provides a detection device for multiple physiological parameters, which comprises a camera, a processor and a display. The camera is configured to capture video of tissue color changes. The processor is configured to decompose the video frame by frame to obtain a picture set consisting of a plurality of video pictures; performing blind source separation on the pictures in the picture set respectively to generate a volume blood flow pulse wave imaging PPGi signal; and determining characteristic parameters according to the PPGi signals, and performing fitting calculation on the characteristic parameters to obtain a plurality of physiological parameters. The display is configured to present a plurality of physiological parameters, so that a user can acquire the physiological parameters through the display, the detection mode of the physiological parameters is more flexible and convenient, resources are saved, and the user can acquire the physiological parameters of the user through the camera on the handheld terminal, so that the user can perform health management according to the needs at any time.
Description
Technical Field
The disclosure relates to the technical field of medical equipment, and in particular relates to detection equipment for multiple physiological parameters.
Background
Along with the continuous development of smart phones, the accessory functions of the smart phones are more and more abundant, and the accessory functions of the smart phones are more and more powerful. At present, a part of mobile phones are additionally provided with a function of measuring heart rate through a camera, but the function of measuring heart rate can only be realized, and the function of measuring other physiological parameters can not be realized, and along with the increasing attention of people on health, the research of using standard equipment such as mobile phones and the like to finish more physiological parameter measurement is necessary. In addition, most of the current physiological parameter acquisition devices are independent devices which are uploaded to a mobile phone end by using a wireless transmission technology to complete integration, so that the device is inconvenient to use to a certain extent, corresponding fees are required to be paid additionally, and the cost is too high for users.
Disclosure of Invention
Aiming at the technical problems in the prior art, the disclosure provides a detection device for multiple physiological parameters, so that a user can acquire the multiple physiological parameters through a camera.
The disclosed embodiments provide a detection device for multiple physiological parameters, comprising:
a camera configured to capture video of tissue color changes;
a processor configured to decompose the video frame by frame to obtain a picture set consisting of a plurality of video pictures; performing blind source separation on the pictures in the picture set respectively to generate a volume blood flow pulse wave imaging PPGi signal; determining characteristic parameters according to the PPGi signals, and performing fitting calculation on the characteristic parameters to obtain a plurality of physiological parameters;
a display configured to present a plurality of physiological parameters.
In some embodiments, the processor is further configured to denoise the PPGi signal using a digital signal processing method.
In some embodiments, the processor is specifically configured to determine the characteristic parameter as follows:
determining a characteristic value participating in the characteristic parameter calculation according to a characteristic parameter calculation mode;
determining the characteristic parameter according to the characteristic value extracted from the PPGi signal;
wherein the characteristic values include: the peak value, the trough value, the maximum rising slope point, the first order conduction maximum value and the first order conduction minimum value of the PPGi signal.
In some embodiments, the processor is specifically configured to determine the characteristic value as follows:
identifying peak values, trough values and maximum rising slope points of the PPGi signals in each identification period by adopting differential zero crossing points and an adaptive threshold algorithm;
an adaptive threshold algorithm is used to identify the first order derivative maximum and the first order derivative minimum of the PPGi signal in each identification period.
In some embodiments, the identification order of the feature values is as follows: the trough value, the maximum rising slope point, the first derivative maximum, the peak value, and the first derivative minimum.
In some embodiments, the processor is specifically configured to determine the characteristic parameter as follows:
the systole time ST is determined from the trough-to-peak systole time.
In some embodiments, the processor is specifically configured to determine the characteristic parameter as follows:
the diastolic time DT is determined from the peak-to-trough diastolic time.
In some embodiments, the processor is specifically configured to determine the characteristic parameter as follows:
the heartbeat time interval PPI is determined according to the time interval of the adjacent wave crests, and the first parameter is determined according to the time from the first-order wave crest to the wave trough.
In some embodiments, the processor is specifically configured to determine the characteristic parameter as follows:
determining a waveform characteristic parameter K of the PPGi signal according to the following formula:
wherein Ps, pd and Pm are respectively a peak value, a trough value and an average value of the PPGi signal in a recognition period, and the average value is determined according to the following formulaWherein P (T) is a waveform function of the PPGi signal, and T is an identification period.
In some embodiments, the processor is specifically configured to determine the physiological parameter as follows:
the heart rate HR is determined according to the following formula: hr= (60 x predetermined sampling rate)/PPI.
In some embodiments, the processor is specifically configured to determine the physiological parameter as follows:
the systolic pressure SBP is determined according to the following formula: sbp=a·st+b·dt+c·ppgd1dt+d·amp+e;
the diastolic pressure is determined according to the following formula
Wherein a, b, c, d, e is a constant, PPGd1DT is the first parameter, amp is the amplitude of the peak value of the PPGi signal.
In some embodiments, the processor is specifically configured to determine the physiological parameter as follows:
blood oxygen Spo was determined according to the following formula 2 ：
Wherein,and->Is of wavelength lambda i Measuring the peak value of the PPGi signalAnd trough values, g and h are constants.
In some embodiments, the processor is specifically configured to determine the physiological parameter as follows:
the cardiac output CO is determined according to the following formula:
where m is a predetermined correction coefficient.
In some embodiments, the processor is further configured to: and determining a damage index based on the corresponding relation between the physiological parameter and the cardiovascular system so as to judge the damage according to the damage index.
In some embodiments, the processor is specifically configured to determine the impairment index F according to the following formula:
wherein RC is a high value in the normal range of the index, RF is a low value in the normal range of the index, value is a value of the physiological parameter, power [ ] is a function for returning the power of a given number, and alpha and beta are experimentally calculated.
Compared with the prior art, the beneficial effects of the embodiment of the disclosure are that: according to the method, the video of tissue color change is collected through the camera, the video is decomposed into the picture set frame by the processor, the pictures in the picture set are subjected to blind source separation to generate the PPGi signal, and the characteristic parameters determined according to the PPGi signal are subjected to fitting calculation to finally obtain a plurality of physiological parameters (such as heart rate, blood oxygen, blood pressure, cardiac output and other physiological parameters), so that a user can obtain the physiological parameters through the display, the detection mode of the physiological parameters is more flexible and convenient, resources are saved, and the user can obtain the physiological parameters of the user through the camera on the handheld terminal, so that the user can carry out health management according to requirements at any time.
Drawings
In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. The same reference numerals with letter suffixes or different letter suffixes may represent different instances of similar components. The accompanying drawings illustrate various embodiments by way of example in general and not by way of limitation, and together with the description and claims serve to explain the disclosed embodiments. Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts. Such embodiments are illustrative and not intended to be exhaustive or exclusive of the present apparatus or method.
FIG. 1 is a block diagram of a multi-physiological parameter sensing device according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of a detection device for detecting physiological parameters according to an embodiment of the present disclosure;
fig. 3 is a flow chart of determining feature parameters according to an embodiment of the present disclosure.
The reference numerals in the drawings denote components:
100-a detection device for multiple physiological parameters; 101-a camera; 102-a processor; 103-display.
Detailed Description
In order to better understand the technical solutions of the present disclosure, the following detailed description of the present disclosure is provided with reference to the accompanying drawings and the specific embodiments. Embodiments of the present disclosure will be described in further detail below with reference to the drawings and specific embodiments, but not by way of limitation of the present disclosure.
The terms "first," "second," and the like, as used in this disclosure, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises" and the like means that elements preceding the word encompass the elements recited after the word, and not exclude the possibility of also encompassing other elements. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
In this disclosure, when a particular device is described as being located between a first device and a second device, there may or may not be an intervening device between the particular device and either the first device or the second device. When it is described that a particular device is connected to other devices, the particular device may be directly connected to the other devices without intervening devices, or may be directly connected to the other devices without intervening devices.
All terms (including technical or scientific terms) used in this disclosure have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs, unless specifically defined otherwise. It will be further 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 unless expressly so defined herein.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
The embodiment of the disclosure provides a multi-physiological parameter detection device, as shown in fig. 1, which comprises a camera, a processor and a display. As shown in fig. 1 and fig. 2, the camera is configured to collect video of tissue color change (S201), and the camera may be any camera configured on any electronic device, where the electronic device may be a notebook computer, a handheld terminal, or the like, and the electronic device may be a camera with a camera function, which is not limited in this application.
Further, with continued reference to fig. 2, the processor is configured to decompose the video frame by frame to obtain a picture set composed of a plurality of video pictures (S202), that is, by decomposing the video frame by frame to obtain the picture set, the situation of color change of the organization in the video can be displayed one by one. Based on the above step S202, the images in the image set are subjected to blind source separation to generate the volume flow pulse wave imaging PPGi signal (S203), and it should be noted that, as known to those skilled in the art, blind source separation refers to analysis of an original signal that is not observed from a plurality of observed mixed signals. That is, blind source separation of pictures in a picture set can generate PPGi signals.
Continuing with the step S203, with reference to fig. 2, the characteristic parameters are determined according to the PPGi signals, and the characteristic parameters are subjected to fitting calculation to obtain a plurality of physiological parameters (S204). It should be noted that, as known to those skilled in the art, fitting refers to knowing a number of discrete function values of a function, and by adjusting a number of undetermined coefficients in the function, the difference (least squares meaning) between the function and a known point set is minimized. By performing the fitting calculation on the characteristic parameters, a plurality of physiological parameters (such as heart rate, blood oxygen, blood pressure, cardiac output and other physiological parameters) can be obtained, so that a user can obtain a plurality of physiological parameters through the electronic equipment with the camera instead of singly obtaining one physiological parameter. Further, the display is configured to present a plurality of the above-mentioned physiological parameters (S205) such that the user can monitor and manage his own health based on the physiological parameters of heart rate, blood oxygen, blood pressure, cardiac output etc. displayed by the display.
According to the method, the video of tissue color change is collected through the camera, the video is decomposed into the picture set frame by the processor, the pictures in the picture set are subjected to blind source separation to generate the PPGi signal, and the characteristic parameters determined according to the PPGi signal are subjected to fitting calculation to finally obtain a plurality of physiological parameters (such as heart rate, blood oxygen, blood pressure, cardiac output and other physiological parameters), so that a user can obtain the physiological parameters through the display, the detection mode of the physiological parameters is more flexible and convenient, resources are saved, and the user can obtain the physiological parameters of the user through the camera on the handheld terminal, so that the user can carry out health management according to requirements at any time.
In some embodiments, continuing with fig. 2, the above-described processor is further configured to denoise the PPGi signal using a digital signal processing method (S206). It should be noted that, as known by those skilled in the art, the digital signal processing converts the motion change of the object into a series of numbers, and extracts useful information from the series of numbers by using a calculation method, so as to meet the needs of practical application. The digital signal processing has high processing precision, flexible changing function, stable performance and high processing efficiency. In addition, in the process of digitalization and transmission, the digital image in reality is often influenced by interference of imaging equipment and external environment noise and the like, and noise in the digital image can be effectively reduced by adopting image denoising processing. That is, the embodiment of the disclosure performs denoising processing on the PPGi signal by using a digital signal processing manner, so as to obtain a PPGi signal with better quality.
In some embodiments, the processor is specifically configured to determine the characteristic parameters (as shown in fig. 3) as follows: step S301 is first executed to determine feature values involved in the feature parameter calculation according to the feature parameter calculation mode. Step S302 is then performed to determine the characteristic parameters from the characteristic values extracted from the PPGi signal. Wherein the characteristic values include: peak value, trough value, maximum rising slope point, first order derivative maximum value and first order derivative minimum value of PPGi signal.
It can be understood that the calculation modes of each characteristic parameter are different, and the characteristic value participating in calculating the characteristic parameter can be determined according to the calculation mode of one characteristic parameter, so that the characteristic value required by calculating the characteristic parameter can be acquired in a targeted manner, and the calculation efficiency is improved. And finally determining the specific numerical value of the characteristic parameter through the obtained corresponding characteristic value and a calculation mode based on the characteristic parameter. Specifically, based on the above-mentioned peak value, trough value, maximum rising slope point, first order derivative maximum value, first order derivative minimum value, and other characteristic values, a plurality of characteristic parameters can be calculated to determine a plurality of physiological parameters.
In some embodiments, the processor is specifically configured to determine the feature value as follows: identifying peak values, trough values and maximum rising slope points of the PPGi signal in each identification period by adopting differential zero crossing points and an adaptive threshold algorithm, wherein it is understood that the peaks from positive to negative are differentiated, and the peaks from negative to positive are troughs; an adaptive threshold algorithm is used to identify the first order derivative maximum and the first order derivative minimum of the PPGi signal in each identification period. It will be appreciated that the above-described identification period may be a heart beat period. The first-order derivative maximum value and the first-order derivative minimum value are the maximum value and the minimum value determined by first-order derivative with respect to the waveform function of the PPGi signal.
It should be noted that, as known to those skilled in the art, the adaptive threshold algorithm is a method for performing image calculation by replacing the global threshold with the local threshold of the image, and is specifically specific to a picture with an excessively changed shade or a picture with a less obvious color difference in a range. In performing the adaptive threshold algorithm, a dynamically updated adaptive threshold is first introduced, which is determined based on the peak value in the last recognition period, and which can be updated once per unit time to detect the above-mentioned feature value within the range of the adaptive threshold. The adaptive threshold may be updated once every second or once every two seconds, which is not particularly limited in this application. By adopting the self-adaptive threshold algorithm, the algorithm complexity can be effectively reduced, and the operation efficiency is improved.
It can be understood that the peak value, the trough value and the maximum rising slope point of the PPGi signal in each recognition period are recognized by adopting a differential zero-crossing point and an adaptive threshold algorithm, wherein the differential zero-crossing point is an algorithm commonly adopted in the peak value, the trough value and the maximum rising slope point of the regularity signal, and the application is not repeated herein.
In some embodiments, the identification order of the feature values is as follows: (1) trough values; (2) a maximum rising slope point and a first derivative maximum; (3) a peak value; (4) first order derivative minimum. It can be understood that the valley value is an identification starting point in one identification period, and the operation amount can be effectively reduced by adopting the identification sequence, so that the operation efficiency is improved.
In some embodiments, the above processor is specifically configured to determine the following respective characteristic parameters as follows:
(1) The systole time ST is determined from the trough-to-peak systole time.
(2) The diastolic time DT is determined from the peak-to-trough diastolic time.
It is understood that each time the heart contracts and relaxes, one cardiac cycle is constituted. The systole time ST is determined by the trough-to-trough systole time, and the diastole time DT is determined by the trough-to-trough diastole time.
(3) The heartbeat time interval PPI is determined according to the time interval of the adjacent wave crests, and the first parameter is determined according to the time from the first-order wave crest to the wave trough. The heartbeat time interval PPI refers to the difference between the highest point and the lowest point, i.e. the potential difference from the positive peak to the negative peak. That is, the beat time interval PPI is determined by the time interval of adjacent peaks.
(4) The waveform characteristic parameter K of the PPGi signal is determined according to the following formula:
wherein Ps, pd and Pm are respectively the peak value, the trough value and the average value of the PPGi signal in one identification period, and the average value is determined according to the following formulaWherein P (T) is a waveform function of the PPGi signal, and T is an identification period.
In some embodiments, the processor is specifically configured to determine the following respective physiological parameters as follows:
(1) The heart rate HR is determined according to the following formula: hr= (60 x predetermined sampling rate)/PPI.
It will be appreciated that the sampling rate is selected according to sampling laws. Those skilled in the art will appreciate that the sampled value may contain all information of the original signal as long as the sampling frequency is greater than or equal to twice the highest frequency of the effective signal, so that the sampled signal may be restored to the original signal without distortion. By the method, the proper sampling rate can be obtained.
Based on the above steps, the heart rate, which is the number of beats per minute in a normal person's rest state, can be determined by calculating the determined heart beat time interval PPI and a predetermined sampling rate.
(2) The systolic pressure SBP is determined according to the following formula:
SBP＝a·ST+b·DT+c·PPGd1DT+d·Amp+e，
it will be appreciated that the above systolic pressure SBP is the pressure in the artery that rises when the human heart contracts, and is highest in the middle of systole, where the pressure of the blood against the inner wall of the blood vessel is called systolic pressure SBP.
(3) The diastolic pressure DBP is determined according to the following formula:
it will be appreciated that the above-mentioned diastolic pressure DBP is the pressure generated when the arterial blood vessel is elastically retracted upon diastole of the human heart, and is referred to as the diastolic pressure DBP.
Wherein a, b, c, d, e is a constant, and can be repeatedly verified according to the experimental result to obtain a suitable a, b, c, d, e value; PPGd1DT is a first parameter, namely the time from the first order guided wave peak to the trough; amp is the amplitude of the peak value of the PPGi signal, and the amplitude is the amplitude corresponding to the point corresponding to the peak value.
(4) Blood oxygen Spo was determined according to the following formula 2 ：
Wherein,and->Is of wavelength lambda i And measuring the peak value and the trough value of the PPGi signal, wherein g and h are constants, and the values of g and h can be obtained by repeated verification according to experimental results.
It is understood that the above blood oxygen refers to oxygen in blood.
(5) The cardiac output CO is determined according to the following formula:
where m is a predetermined correction factor, which is determined by experimental calculation. The cardiac output CO refers to the amount of blood pumped by the left or right ventricle per minute, i.e. the product of heart rate and stroke volume.
It is to be understood that the correction coefficient refers to a coefficient which is added to the calculation formula in order to embody the actual performance as much as possible when the data calculation, the formula expression, and the like deviate due to the ideal and reality, and investigation, and the like.
In some embodiments, the processor is further configured to: and determining a damage index based on the corresponding relation between the physiological parameter and the cardiovascular system so as to judge the damage according to the damage index. It can be understood that the physiological parameters such as heart rate, blood oxygen, blood pressure and cardiac output all have corresponding relations with the heart vessels, when the heart rate, blood oxygen, blood pressure and cardiac output are abnormal, the corresponding heart vessels possibly have diseases, and the damage index is determined according to the corresponding relations between the physiological parameters and the heart vessels, so that the current damage condition of the heart vessels is determined, and the user can further know the health condition of the user.
In practice, the processor is specifically configured to determine the impairment index F according to the following formula:
wherein RC is a high value in the normal range of the index, RF is a low value in the normal range of the index, value is a value of a physiological parameter, power [ ] is a function for returning to the power of a given number, alpha and beta are determined by experimental calculation, and the damage index F corresponding to each physiological parameter is compared with a threshold value in the normal range to provide a damage index F of the physiological parameter on the cardiovascular system, so that a user can complete disease risk prediction based on the damage index F and further manage self health.
Furthermore, although exemplary embodiments have been described herein, the scope thereof includes any and all embodiments having equivalent elements, modifications, omissions, combinations (e.g., of the various embodiments across schemes), adaptations or alterations based on the present disclosure. Elements in the claims are to be construed broadly based on the language employed in the claims and are not limited to examples described in the present specification or during the practice of the present application, which examples are to be construed as non-exclusive. It is intended, therefore, that the specification and examples be considered as exemplary only, with a true scope and spirit being indicated by the following claims and their full scope of equivalents.
The above description is intended to be illustrative and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. For example, other embodiments may be used by those of ordinary skill in the art upon reading the above description. In addition, in the above detailed description, various features may be grouped together to streamline the disclosure. This is not to be interpreted as an intention that the disclosed features not being claimed are essential to any claim. Rather, the disclosed subject matter may include less than all of the features of a particular disclosed embodiment. Thus, the following claims are hereby incorporated into the detailed description as examples or embodiments, with each claim standing on its own as a separate embodiment, and it is contemplated that these embodiments may be combined with one another in various combinations or permutations. The scope of the disclosure should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.
The above embodiments are merely exemplary embodiments of the present disclosure, which are not intended to limit the present disclosure, the scope of which is defined by the claims. Various modifications and equivalent arrangements of parts may be made by those skilled in the art, which modifications and equivalents are intended to be within the spirit and scope of the present disclosure.
Claims (10)
1. A multi-physiological parameter sensing device, comprising:
a camera configured to capture video of tissue color changes;
a processor configured to decompose the video frame by frame to obtain a picture set consisting of a plurality of video pictures; performing blind source separation on the pictures in the picture set respectively to generate a volume blood flow pulse wave imaging PPGi signal; determining characteristic parameters according to the PPGi signals, and performing fitting calculation on the characteristic parameters to obtain a plurality of physiological parameters;
a display configured to present a plurality of physiological parameters; wherein,
the processor is specifically configured to determine the characteristic parameters as follows:
determining a characteristic value participating in the characteristic parameter calculation according to a characteristic parameter calculation mode;
determining the characteristic parameter according to the characteristic value extracted from the PPGi signal;
wherein the characteristic values include: the peak value, the trough value, the maximum rising slope point, the first order conduction maximum value and the first order conduction minimum value of the PPGi signal;
the processor is further configured to determine the characteristic value as follows:
identifying peak values, trough values and maximum rising slope points of the PPGi signals in each identification period by adopting differential zero crossing points and an adaptive threshold algorithm;
adopting an adaptive threshold algorithm to identify a first-order derivative maximum value and a first-order derivative minimum value of the PPGi signal in each identification period; the identification sequence of the characteristic values is as follows: the trough value, the maximum rising slope point and the first derivative maximum value, the peak value and the first derivative minimum value; the identification period is a heart beat period, and the first-order derivative maximum value and the first-order derivative minimum value are respectively a maximum value and a minimum value which are determined by carrying out first-order derivative on a waveform function of the PPGi signal;
the processor is specifically configured to determine the physiological parameter as follows:
the systolic pressure SBP is determined according to the following formula: sbp=a·st+b·dt+c·ppgd1dt+d·amp+e;
the diastolic pressure DBP is determined according to the following formula:
wherein a, b, c, d, e is a constant, ST is systole time, DT is diastole time, PPGd1DT is a first parameter, amp is the amplitude of the peak value of the PPGi signal, and K is a waveform characteristic parameter; wherein the first parameter is determined by determining a heartbeat time interval PPI according to the time interval of adjacent peaks and the time from the first-order leading peak to the trough;
the processor is specifically configured to determine the impairment index F according to the following formula:
wherein RC is a high value in the normal range of the index, RF is a low value in the normal range of the index, value is a value of the physiological parameter, power [ ] is a function for returning the power of a given number, and alpha and beta are experimentally calculated.
2. The multi-physiological parameter sensing device of claim 1, wherein the processor is further configured to denoise the PPGi signal using a digital signal processing method.
3. The multi-physiological parameter sensing device of claim 1, wherein the processor is specifically configured to determine the characteristic parameter as follows:
the systole time ST is determined from the trough-to-peak systole time.
4. A multi-physiological parameter sensing device according to claim 3, wherein said processor is specifically configured to determine said characteristic parameter as follows:
the diastolic time DT is determined from the peak-to-trough diastolic time.
5. The multi-physiological parameter sensing device of claim 4, wherein the processor is specifically configured to determine the characteristic parameter as follows:
the heartbeat time interval PPI is determined according to the time interval of the adjacent wave crests, and the first parameter is determined according to the time from the first-order wave crest to the wave trough.
6. The multi-physiological parameter sensing device of claim 5, wherein the processor is specifically configured to determine the characteristic parameter as follows:
determining a waveform characteristic parameter K of the PPGi signal according to the following formula:
wherein Ps, pd, pm are respectively a peak value, a trough value and an average value of the PPGi signal in a recognition period, and the average value Pm is determined according to the following formula:wherein P (T) is a waveform function of the PPGi signal, and T is an identification period.
7. The multi-physiological parameter sensing device of claim 6, wherein the processor is specifically configured to determine the physiological parameter as follows:
the heart rate HR is determined according to the following formula:
hr= (60 x predetermined sampling rate)/PPI.
8. The multi-physiological parameter sensing device of claim 6, wherein the processor is specifically configured to determine the physiological parameter as follows:
blood oxygen Spo was determined according to the following formula 2 ：
Wherein, and->Is of wavelength lambda i The peak and trough values of the PPGi signal were measured, g and h being constants.
9. The multi-physiological parameter sensing device of claim 1, wherein the processor is specifically configured to determine the physiological parameter as follows:
the cardiac output CO is determined according to the following formula:
where m is a predetermined correction coefficient.
10. The multi-physiological parameter sensing device of any one of claims 7 to 9, wherein the processor is further configured to: and determining a damage index based on the corresponding relation between the physiological parameter and the cardiovascular system so as to judge the damage according to the damage index.
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Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH10248818A (en) * | 1997-03-17 | 1998-09-22 | Matsushita Electric Ind Co Ltd | Non-invasive sphygmomanometer |
| CN103263271A (en) * | 2013-05-27 | 2013-08-28 | 天津点康科技有限公司 | Non-contact automatic blood oxygen saturation degree measurement system and measurement method |
| CN103815890A (en) * | 2014-03-08 | 2014-05-28 | 哈尔滨工业大学 | Method for detecting heart rate by utilizing intelligent mobile phone camera |
| CN104055496A (en) * | 2014-01-15 | 2014-09-24 | 中国航天员科研训练中心 | Method for estimating exercise load level based on cardiogenic signals |
| CN104068841A (en) * | 2014-07-07 | 2014-10-01 | 成都康拓邦科技有限公司 | Measuring method and device for measuring systole time parameter |
| KR20160000810A (en) * | 2014-06-24 | 2016-01-05 | 주식회사 메디코아 | Acceleration plethysmography analysis apparatus and method using wave form frequency distribution |
| CN105286815A (en) * | 2015-11-02 | 2016-02-03 | 重庆大学 | Pulse wave signal feature point detection method based on waveform time domain features |
| CN105455798A (en) * | 2015-10-19 | 2016-04-06 | 东南大学 | Continuous blood pressure measuring system and calibration measurement method based on Android mobile phone terminal |
| CN106175742A (en) * | 2016-07-19 | 2016-12-07 | 北京心量科技有限公司 | A kind of heart sign acquisition methods and device |
| CN106539572A (en) * | 2016-11-02 | 2017-03-29 | 中国科学院电子学研究所 | A kind of continuity blood pressure measuring method of multi-parameter fusion |
| CN106889979A (en) * | 2016-12-30 | 2017-06-27 | 中国科学院电子学研究所 | A kind of continuity blood pressure measuring method based on electrocardiosignal and blood oxygen volume ripple |
| CN107296593A (en) * | 2017-05-31 | 2017-10-27 | 江苏斯坦德利医疗科技有限公司 | A kind of hemodynamic parameter acquisition methods and device |
| CN107928654A (en) * | 2017-12-11 | 2018-04-20 | 重庆邮电大学 | A kind of pulse wave signal blood pressure detecting method based on neutral net |
| CN108261192A (en) * | 2016-12-30 | 2018-07-10 | 深圳先进技术研究院 | Continuous BP measurement method, apparatus and equipment |
| CN109009034A (en) * | 2018-07-10 | 2018-12-18 | 京东方科技集团股份有限公司 | blood pressure measuring method, terminal and storage medium |
| CN109528216A (en) * | 2019-01-18 | 2019-03-29 | 京东方科技集团股份有限公司 | The detection method and device of fetal hemoglobin saturation |
| CN109730723A (en) * | 2019-03-11 | 2019-05-10 | 京东方科技集团股份有限公司 | Determine method, artery sclerosis detection device and the system of pulse transit time |
| CN109893115A (en) * | 2019-03-11 | 2019-06-18 | 武汉大学 | A kind of processing analysis method based on human body weak biological electric signal |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090216144A1 (en) * | 2008-02-25 | 2009-08-27 | Bruce Hopenfeld | Hopping methods for the detection of QRS onset and offset |
| JP5850861B2 (en) * | 2010-01-29 | 2016-02-03 | エドワーズ ライフサイエンシーズ コーポレイションＥｄｗａｒｄｓ Ｌｉｆｅｓｃｉｅｎｃｅｓ Ｃｏｒｐｏｒａｔｉｏｎ | Eliminating the effects of irregular cardiac cycles in determining cardiovascular parameters |
-
2020
- 2020-01-13 CN CN202010031655.0A patent/CN111214218B/en active Active
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH10248818A (en) * | 1997-03-17 | 1998-09-22 | Matsushita Electric Ind Co Ltd | Non-invasive sphygmomanometer |
| CN103263271A (en) * | 2013-05-27 | 2013-08-28 | 天津点康科技有限公司 | Non-contact automatic blood oxygen saturation degree measurement system and measurement method |
| CN104055496A (en) * | 2014-01-15 | 2014-09-24 | 中国航天员科研训练中心 | Method for estimating exercise load level based on cardiogenic signals |
| CN103815890A (en) * | 2014-03-08 | 2014-05-28 | 哈尔滨工业大学 | Method for detecting heart rate by utilizing intelligent mobile phone camera |
| KR20160000810A (en) * | 2014-06-24 | 2016-01-05 | 주식회사 메디코아 | Acceleration plethysmography analysis apparatus and method using wave form frequency distribution |
| CN104068841A (en) * | 2014-07-07 | 2014-10-01 | 成都康拓邦科技有限公司 | Measuring method and device for measuring systole time parameter |
| CN105455798A (en) * | 2015-10-19 | 2016-04-06 | 东南大学 | Continuous blood pressure measuring system and calibration measurement method based on Android mobile phone terminal |
| CN105286815A (en) * | 2015-11-02 | 2016-02-03 | 重庆大学 | Pulse wave signal feature point detection method based on waveform time domain features |
| CN106175742A (en) * | 2016-07-19 | 2016-12-07 | 北京心量科技有限公司 | A kind of heart sign acquisition methods and device |
| CN106539572A (en) * | 2016-11-02 | 2017-03-29 | 中国科学院电子学研究所 | A kind of continuity blood pressure measuring method of multi-parameter fusion |
| CN106889979A (en) * | 2016-12-30 | 2017-06-27 | 中国科学院电子学研究所 | A kind of continuity blood pressure measuring method based on electrocardiosignal and blood oxygen volume ripple |
| CN108261192A (en) * | 2016-12-30 | 2018-07-10 | 深圳先进技术研究院 | Continuous BP measurement method, apparatus and equipment |
| CN107296593A (en) * | 2017-05-31 | 2017-10-27 | 江苏斯坦德利医疗科技有限公司 | A kind of hemodynamic parameter acquisition methods and device |
| CN107928654A (en) * | 2017-12-11 | 2018-04-20 | 重庆邮电大学 | A kind of pulse wave signal blood pressure detecting method based on neutral net |
| CN109009034A (en) * | 2018-07-10 | 2018-12-18 | 京东方科技集团股份有限公司 | blood pressure measuring method, terminal and storage medium |
| CN109528216A (en) * | 2019-01-18 | 2019-03-29 | 京东方科技集团股份有限公司 | The detection method and device of fetal hemoglobin saturation |
| CN109730723A (en) * | 2019-03-11 | 2019-05-10 | 京东方科技集团股份有限公司 | Determine method, artery sclerosis detection device and the system of pulse transit time |
| CN109893115A (en) * | 2019-03-11 | 2019-06-18 | 武汉大学 | A kind of processing analysis method based on human body weak biological electric signal |
Non-Patent Citations (5)
| Title |
|---|
| 《基于普通摄像头的非接触式生理参数检测技术研究》;马良;《中国优秀硕士学位论文全文数据库，医药卫生科技辑（2018）》;20180115;E080-91 * |
| 基于VB与Access的脉搏信号处理系统的设计;郭静玉;白雪飞;侯海燕;;计算技术与自动化(第02期);全文 * |
| 基于微型摄像头的容积血流脉搏波成像关键技术;刘赫;王磊;;科研信息化技术与应用(第06期);全文 * |
| 基于改进型斜率阈值法的脉搏波特征提取研究;张梦龙;李晓风;许金林;黄万风;;电子测量技术(第04期);全文 * |
| 基于普通摄像头的非接触式生理参数检测技术研究;马良;《中国优秀硕士学位论文全文数据库，医药卫生科技辑（2018）》;E080-91 * |
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