CN109512393B - Recursive method, system and storage medium for measuring confidence of blood oxygen saturation - Google Patents
Recursive method, system and storage medium for measuring confidence of blood oxygen saturation Download PDFInfo
- Publication number
- CN109512393B CN109512393B CN201811482852.3A CN201811482852A CN109512393B CN 109512393 B CN109512393 B CN 109512393B CN 201811482852 A CN201811482852 A CN 201811482852A CN 109512393 B CN109512393 B CN 109512393B
- Authority
- CN
- China
- Prior art keywords
- updating
- linear regression
- confidence
- blood oxygen
- sum
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
- A61B5/14551—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases
-
- 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
- A61B5/7221—Determining signal validity, reliability or quality
Abstract
The invention discloses a recursion method, a recursion system and a storage medium of a degree of confidence of oxyhemoglobin saturation measurement, wherein the method comprises the following steps: carrying out initialization configuration on the number of the data points; constructing a linear regression model according to the initialization configuration result; obtaining the confidence of the blood oxygen saturation measurement result according to the linear regression model; judging whether the confidence of the blood oxygen saturation measurement result meets the threshold requirement, if so, outputting the confidence of the blood oxygen saturation measurement result; otherwise, executing the next step until the confidence coefficient of the blood oxygen saturation degree measurement result meets the threshold requirement; updating the number of the data points; and updating the linear regression model through a recursion algorithm according to the updating result of the data point, and returning to execute the step of obtaining the confidence coefficient of the blood oxygen saturation measurement result according to the linear regression model. The invention improves the reliability of the blood oxygen saturation measurement result, is more scientific and can be widely applied to the technical field of biomedical signal processing.
Description
Technical Field
The invention relates to the technical field of biomedical signal processing, in particular to a recursion method, a recursion system and a storage medium of a blood oxygen saturation degree measurement confidence coefficient.
Background
The relaxation and contraction of the heart drives blood flow through the lungs, combining oxygen with reduced hemoglobin (Hb) into oxygenated hemoglobin (HbO 2), which is released after blood transport to the capillaries. Sufficient oxygen is the material basis for realizing the metabolism of human tissue cells and maintaining life activities. Blood oxygen saturation is an important physiological parameter that reflects the oxygen content in blood, and is directly related to the respiratory system, circulatory system, and cardiopulmonary function. At present, the blood oxygen saturation is widely applied to the physical sign detection of intensive care, family health care and high-risk occupations such as firefighters, pilots and the like.
The detection method of the blood oxygen saturation can be divided into two types of invasive detection and non-invasive detection. Among them, the Van Slyke pressure detection method and the oxygen electrode method are mainly used for invasive blood oxygen saturation detection. The main means of non-invasive detection is photoplethysmography (PPG). The blood volume of blood vessels changes along with the diastole and the systole, which causes different absorption rates of light, and the reflected or transmitted light intensity also changes in a pulsating and periodic manner. The pulse wave blood oxygen analyzer utilizes a photoplethysmography to calculate the blood oxygen saturation according to the Lambert-Beer law by recording the reflection or transmission light intensity of 660nm red light and 940nm infrared light. In the actual measurement, the accurate calculation of the pulse oximetry signal characteristic value R is the key to realize the noninvasive detection of the blood oxygen saturation based on the photoplethysmography.
The traditional R value extraction method needs to decompose pulse waves into an alternating current component and a direct current component, wherein the alternating current component reflects the absorption of HbO2 and Hb in blood to light, and the direct current component reflects the absorption of non-blood tissues in finger tips, such as muscles, bones, fat, water and the like to light. The ac component is usually calculated by using a peak-to-valley method, i.e., approximately considering the difference between the peak value and the valley value in one pulse period as the amplitude of the ac component. In the measurement and alternating current-direct current decomposition processes, introduced and generated interference and random noise influence the R value precision calculated by a peak-valley value method, and the precision is generally improved by adopting the average of peak-valley values of a plurality of periods, so that the real-time performance of calculation is influenced. A method of using a linear regression model to calculate the R value has been proposed, which makes full use of data of all sampling points, rather than relying on only the peak-to-valley values of the pulse wave, and improves the stability of the calculation result. However, photodetection is susceptible to external light conditions and finger tip motion artifacts that cause blood engorgement and changes in the light transmission path. All the factors can cause the distortion of the measurement result, and the conditions of missed detection and false detection are caused. At present, the blood oxygen saturation tester lacks scientific analysis on the reliability of a measurement result, and how to reasonably evaluate the reliability of a measurement value is a problem which is urgently needed to be solved in the industry.
Disclosure of Invention
To solve the above technical problems, the present invention aims to: provided are a recursion method, a system and a storage medium for measuring confidence of blood oxygen saturation with high reliability.
On one hand, the technical scheme adopted by the invention is as follows:
the recurrence method of the blood oxygen saturation degree measurement confidence degree comprises the following steps:
carrying out initialization configuration on the number of the data points;
constructing a linear regression model according to the initialization configuration result;
obtaining the confidence of the blood oxygen saturation measurement result according to the linear regression model;
judging whether the confidence of the blood oxygen saturation measurement result meets the threshold requirement, if so, outputting the confidence of the blood oxygen saturation measurement result; otherwise, executing the next step until the confidence coefficient of the blood oxygen saturation degree measurement result meets the threshold requirement;
updating the number of the data points;
and updating the linear regression model through a recursion algorithm according to the updating result of the data point, and returning to execute the step of obtaining the confidence coefficient of the blood oxygen saturation measurement result according to the linear regression model.
Further, the step of constructing a linear regression model according to the initialization configuration result includes the following steps:
calculating intermediate parameters according to the initialization configuration result;
calculating a fitting coefficient of the linear regression equation according to the intermediate parameters to obtain the linear regression equation;
calculating the sum of the squares of the total deviation average differences and the sum of the squares of the residual errors according to a linear regression equation;
and calculating the confidence degree of the blood oxygen saturation measurement result according to the sum of the squares of the total deviation average differences and the sum of the squares of the residual errors.
Further, the step of updating the linear regression model by a recursive algorithm according to the update result of the data point includes the following steps:
updating the intermediate parameters according to the updating result of the data points;
updating the linear regression equation according to the updated intermediate parameters;
updating the sum of the squares of the total mean deviation and the sum of the squares of the residual errors according to the updated linear regression equation;
and updating the confidence of the blood oxygen saturation measurement result according to the updated total mean-squared-deviation sum and the updated residual sum-squared.
Further, the method also comprises the following steps:
and generating a pulse blood oxygen signal characteristic value according to the linear regression model.
The technical scheme adopted by the other aspect of the invention is as follows:
a system for recurrence of oximetry confidence comprising:
the initialization module is used for carrying out initialization configuration on the number of the data points;
the construction module is used for constructing a linear regression model according to the initialization configuration result;
the acquisition module is used for acquiring the confidence coefficient of the blood oxygen saturation measurement result according to the linear regression model;
the judging module is used for judging whether the confidence coefficient of the oxyhemoglobin saturation measurement result meets the threshold requirement, and if so, the confidence coefficient of the oxyhemoglobin saturation measurement result is output; otherwise, executing the next step until the confidence coefficient of the blood oxygen saturation degree measurement result meets the threshold requirement;
the updating module is used for updating the number of the data points;
and the recurrence module is used for updating the linear regression model through a recurrence algorithm according to the updating result of the data point and returning to the execution acquisition module.
Further, the building module comprises:
the first calculation unit is used for calculating the intermediate parameters according to the initialization configuration result;
the second calculation unit is used for calculating the fitting coefficient of the linear regression equation according to the intermediate parameter to obtain the linear regression equation;
the third calculation unit is used for calculating the sum of the squares of the total deviation average differences and the sum of the squares of the residual errors according to a linear regression equation;
and the fourth calculating unit is used for calculating the confidence coefficient of the blood oxygen saturation measurement result according to the sum of the squares of the total mean deviation differences and the sum of the squares of the residual errors.
Further, the recurrence module includes:
the first updating unit is used for updating the intermediate parameter according to the updating result of the data point;
the second updating unit is used for updating the linear regression equation according to the updated intermediate parameters;
the third updating unit is used for updating the sum of the squares of the total mean deviation and the sum of the squares of the residual errors according to the updated linear regression equation;
and updating the confidence of the blood oxygen saturation measurement result according to the updated total mean-squared-deviation sum and the updated residual sum-squared.
Further, still include:
and the generating module is used for generating a pulse blood oxygen signal characteristic value according to the linear regression model.
The technical scheme adopted by the other aspect of the invention is as follows:
a system for recurrence of oximetry confidence comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method for recursive confidence in oximetry measurements.
The technical scheme adopted by the other aspect of the invention is as follows:
a storage medium having stored therein processor-executable instructions, which when executed by a processor, are operable to perform the method of recursive blood oxygen saturation measurement confidence.
The invention has the beneficial effects that: the method is based on the linear regression model, updates the linear regression model through a recursion algorithm, and finally obtains the confidence coefficient of the oxyhemoglobin saturation measurement result, thereby realizing the reliability evaluation of the measurement value, avoiding the conditions of missed detection and false detection caused by the influence of interference factors such as motion artifact, noise and the like, improving the reliability of the oxyhemoglobin saturation measurement result, and being more scientific.
Drawings
FIG. 1 is a flowchart illustrating steps according to an embodiment of the present invention.
Detailed Description
The invention will be further explained and explained with reference to the drawings and the embodiments in the description. The step numbers in the embodiments of the present invention are set for convenience of illustration only, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.
Referring to fig. 1, an embodiment of the present invention provides a method for recursive confidence of blood oxygen saturation measurement, including the following steps:
s1, carrying out initialization configuration on the number of the data points;
in this embodiment, the number n of data points is initialized to 2;
s2, constructing a linear regression model according to the initialization configuration result;
further as a preferred embodiment of step S2, the step S2 includes the following steps:
s20, acquiring the nth time point data of the red light and the infrared light respectively as Ird(n) and Iir(n) represents; calculating the intermediate variable x (n) ═ Ird(n)(Iir(n)-Iir(n-1)) and y (n) ═ Ird(n)(Iir(n)-Iir(n-1)), for the convenience of writing, are respectively marked as xnAnd yn；
S21, calculating intermediate parameters according to the initialization configuration result;
in this embodiment, first, the initial value of the statistic is calculated according to the initial value of the number of data points, that is: sn,0＝n，
And
sn,0representing the number of data points; sn,1Represents xnThe sum of (1); sn,2Represents ynThe sum of (1); sn,3Represents xnThe sum of squares of; sn,4Represents xnynThe sum of (1); sn,5Represents ynThe sum of squares of;
in addition, the statistics are essentially first and second moments of the data, reflecting the overall information in the sample.
Then, based on the calculated values of the statistics, the intermediate parameters of the linear regression equation are calculated, namely:
wherein, cn,11、cn,12、cn,21And cn,22Representing intermediate parameters in the implementation of the recursion algorithm.
S22, calculating a fitting coefficient of the linear regression equation according to the intermediate parameters to obtain the linear regression equation;
specifically, the present embodiment calculates the linear regression equation Y ═ b1+b2Fitting coefficient b of X at n data pointsn,1＝cn,11sn,2+cn,12sn,4And bn,2＝cn,21sn,2+cn,22sn,4. Wherein, the pulse blood oxygen signal characteristic value R is the parameter bn,2. Wherein, b1And b2Is the undetermined parameter of the regression equation; bn,1And bn,2B calculated for n data points1And b2The pulse blood oxygen signal characteristic value R is the parameter bn,2。
S23, calculating the sum of the squares of the total deviation average differences and the sum of the squares of the residual differences according to a linear regression equation;
and S24, calculating the confidence of the blood oxygen saturation measurement result according to the sum of the squares of the total mean deviation differences and the sum of the squares of the residual errors.
Specifically, the present embodiment calculates the sum of the squares of the total deviation averages
Residual sum of squares SSEn＝bn,1sn,2+bn,2sn,4Coefficient of confidence Cn＝1-SSEn/SSTn。
S3, obtaining the confidence of the blood oxygen saturation measurement result according to the linear regression model;
the confidence coefficient obtained in this embodiment is the confidence coefficient C calculated in step S24n＝1-SSEn/SSTn。
S4, judging whether the confidence of the blood oxygen saturation measurement result meets the threshold requirement, if so, outputting the confidence of the blood oxygen saturation measurement result; otherwise, step S5 is executed until the confidence of the blood oxygen saturation measurement result satisfies the threshold requirement.
Specifically, the embodiment determines whether the confidence coefficient in step S3 reaches a preset threshold, and if so, executes step S7; otherwise, the process returns to step S5 to perform the next recursion calculation.
S5, updating the number of the data points;
specifically, the number of data points in this embodiment is increased as follows: n is n + 1.
S6, updating the linear regression model through a recursion algorithm according to the updating result of the data points;
further as a preferred embodiment of step S6, the step S6 includes the steps of:
s61, updating the intermediate parameters according to the updating result of the data points;
specifically, the present embodiment further calculates an intermediate parameter according to the update result n ═ n + 1:
first, using a recurrence formula, the values of the update statistics are as follows:
sn+1,0＝sn+1，sn+1,1＝sn,1+xn+1，sn+1,2＝sn,2+yn+1，
sn+1,4＝sn,4+xn+1yn+1and
then, using a recurrence formula, the values of the intermediate parameters are updated as follows:
s62, updating the linear regression equation according to the updated intermediate parameters;
specifically, the present embodiment further calculates the linear regression equation Y ═ b according to the updated intermediate parameters1+b2Coefficient b of Xn+1,1＝cn+1,11sn+1,2+cn+1,12sn+1,4And bn+1,2＝cn+1,21sn+1,2+cn+1,22sn+1,4Wherein, the pulse blood oxygen signal characteristic value R is updated to the parameter bn+1,2。
S63, updating the sum of the squares of the total mean deviation and the sum of the squares of the residual errors according to the updated linear regression equation;
s64, updating the confidence coefficient of the blood oxygen saturation measurement result according to the updated total mean square deviation sum and the updated residual square deviation sum; if the confidence at this time does not satisfy the threshold, the process returns to step S5 to enter the next recursion calculation flow.
Specifically, the present embodiment further calculates the sum of the squares of the total deviation averages according to the updated linear regression equation
Residual sum of squares SSEn+1＝bn+1,1sn+1,2+bn+1,2sn+1,4Coefficient of confidence Cn+1＝1-SSEn+1/SSTn+1。
Further as a preferred embodiment, the method further comprises the following steps:
and S7, generating a pulse blood oxygen signal characteristic value according to the linear regression model.
In this embodiment, when the confidence coefficient determined in step S4 reaches the preset threshold, the pulse oximetry signal characteristic value R is output.
Corresponding to the method of fig. 1, an embodiment of the present invention further provides a system for recursive confidence of blood oxygen saturation measurement, including:
the initialization module is used for carrying out initialization configuration on the number of the data points;
the construction module is used for constructing a linear regression model according to the initialization configuration result;
the acquisition module is used for acquiring the confidence coefficient of the blood oxygen saturation measurement result according to the linear regression model;
the judging module is used for judging whether the confidence coefficient of the oxyhemoglobin saturation measurement result meets the threshold requirement, and if so, the confidence coefficient of the oxyhemoglobin saturation measurement result is output; otherwise, executing the next step until the confidence coefficient of the blood oxygen saturation degree measurement result meets the threshold requirement;
the updating module is used for updating the number of the data points;
and the recurrence module is used for updating the linear regression model through a recurrence algorithm according to the updating result of the data point and returning to the execution acquisition module.
Further as a preferred embodiment, the building module comprises:
the first calculation unit is used for calculating the intermediate parameters according to the initialization configuration result;
the second calculation unit is used for calculating the fitting coefficient of the linear regression equation according to the intermediate parameter to obtain the linear regression equation;
the third calculation unit is used for calculating the sum of the squares of the total deviation average differences and the sum of the squares of the residual errors according to a linear regression equation;
and the fourth calculating unit is used for calculating the confidence coefficient of the blood oxygen saturation measurement result according to the sum of the squares of the total mean deviation differences and the sum of the squares of the residual errors.
Further as a preferred embodiment, the recurrence module includes:
the first updating unit is used for updating the intermediate parameter according to the updating result of the data point;
the second updating unit is used for updating the linear regression equation according to the updated intermediate parameters;
the third updating unit is used for updating the sum of the squares of the total mean deviation and the sum of the squares of the residual errors according to the updated linear regression equation;
and updating the confidence of the blood oxygen saturation measurement result according to the updated total mean-squared-deviation sum and the updated residual sum-squared.
Further, as a preferred embodiment, the method further comprises:
and the generating module is used for generating a pulse blood oxygen signal characteristic value according to the linear regression model.
The embodiment of the invention also provides a recursion system of the degree of confidence of the blood oxygen saturation measurement, which comprises:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method for recursive confidence in oximetry measurements.
The contents in the above method embodiments are all applicable to the present system embodiment, the functions specifically implemented by the present system embodiment are the same as those in the above method embodiment, and the beneficial effects achieved by the present system embodiment are also the same as those achieved by the above method embodiment.
Furthermore, a storage medium is provided, in which processor-executable instructions are stored, and when executed by a processor, the processor-executable instructions are used for executing the method for recursive confidence of blood oxygen saturation measurement.
In conclusion, the present invention designs a set of recursive algorithm for calculating the pulse oximetry signal characteristic value R and the confidence coefficient C. The algorithm fully utilizes the data of all sampling points, but does not need to store a large amount of original data in operation, only keeps a plurality of statistics as intermediate variables, and updates the final calculation result continuously according to the intermediate variables. Therefore, the recursion algorithm avoids the waste of hardware resources caused by storing the original data, and simultaneously greatly improves the operation speed. Therefore, the calculation of the pulse blood oxygen signal characteristic value and the confidence coefficient can be efficiently completed in real time by using the recursion algorithm of the invention, and theoretical support is provided for a hardware system to realize rapid and efficient pulse blood oxygen detection.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. The recursion method of the confidence degree of the blood oxygen saturation measurement is characterized in that: the method comprises the following steps:
carrying out initialization configuration on the number of the data points;
according to the initialization configuration result, red light data and infrared light data of at least one time point are obtained to construct a linear regression model; the step of constructing the linear regression model specifically comprises: calculating intermediate parameters according to the initialization configuration result; calculating a fitting coefficient of a linear regression equation according to the intermediate parameters to obtain the linear regression equation; calculating the sum of the squares of the total deviation average differences and the sum of the squares of the residual errors according to the linear regression equation; calculating the confidence coefficient of the blood oxygen saturation measurement result according to the sum of the squares of the total deviation average differences and the sum of the squares of the residual errors; the intermediate parameter is obtained by calculating the light intensity of the red light data and the light intensity of the infrared light data;
judging whether the confidence of the blood oxygen saturation measurement result meets the threshold requirement, if so, outputting the confidence of the blood oxygen saturation measurement result; otherwise, executing the next step until the confidence coefficient of the blood oxygen saturation degree measurement result meets the threshold requirement;
updating the number of the data points;
updating the linear regression model through a recursion algorithm according to the updating result of the data point, and returning to execute the step of obtaining the confidence coefficient of the blood oxygen saturation measuring result according to the linear regression model; the recursion algorithm comprises: updating the intermediate parameter according to the updating result of the data point; updating the linear regression equation according to the updated intermediate parameters; updating the sum of the squares of the total mean deviation and the sum of the squares of the residual errors according to the updated linear regression equation; and updating the confidence of the blood oxygen saturation measurement result according to the updated total mean-squared-deviation sum and the updated residual sum-squared.
2. The method of claim 1, wherein the confidence level of the oximetry measurement is calculated by: further comprising the steps of:
and generating a pulse blood oxygen signal characteristic value according to the linear regression model.
3. The recursion system of the degree of confidence of the blood oxygen saturation measurement is characterized in that: the method comprises the following steps:
the initialization module is used for carrying out initialization configuration on the number of the data points;
the construction module is used for constructing a linear regression model according to the initialization configuration result and acquiring red light data and infrared light data of at least one time point; the step of constructing the linear regression model specifically comprises: calculating intermediate parameters according to the initialization configuration result; calculating a fitting coefficient of a linear regression equation according to the intermediate parameters to obtain the linear regression equation; calculating the sum of the squares of the total deviation average differences and the sum of the squares of the residual errors according to the linear regression equation; calculating the confidence coefficient of the blood oxygen saturation measurement result according to the sum of the squares of the total deviation average differences and the sum of the squares of the residual errors; the intermediate parameter is obtained by calculating the light intensity of the red light data and the light intensity of the infrared light data;
the acquisition module is used for acquiring the confidence coefficient of the blood oxygen saturation measurement result according to the linear regression model;
the judging module is used for judging whether the confidence coefficient of the oxyhemoglobin saturation measurement result meets the threshold requirement, and if so, the confidence coefficient of the oxyhemoglobin saturation measurement result is output; otherwise, the updating module updates until the confidence of the blood oxygen saturation measurement result meets the threshold requirement;
the updating module is used for updating the number of the data points;
the recursive module is used for updating the linear regression model through a recursive algorithm according to the updating result of the data point and returning to the execution acquisition module; the recursion algorithm comprises: updating the intermediate parameter according to the updating result of the data point; updating the linear regression equation according to the updated intermediate parameters; updating the sum of the squares of the total mean deviation and the sum of the squares of the residual errors according to the updated linear regression equation; and updating the confidence of the blood oxygen saturation measurement result according to the updated total mean-squared-deviation sum and the updated residual sum-squared.
4. The system for recurrence of oximetry confidence according to claim 3, wherein: the building module comprises:
the first calculation unit is used for calculating the intermediate parameters according to the initialization configuration result;
the second calculation unit is used for calculating the fitting coefficient of the linear regression equation according to the intermediate parameter to obtain the linear regression equation;
the third calculation unit is used for calculating the sum of the squares of the total deviation average differences and the sum of the squares of the residual errors according to a linear regression equation;
and the fourth calculating unit is used for calculating the confidence coefficient of the blood oxygen saturation measurement result according to the sum of the squares of the total mean deviation differences and the sum of the squares of the residual errors.
5. The system for recurrence of oximetry confidence according to claim 3, wherein: the recurrence module comprises:
the first updating unit is used for updating the intermediate parameter according to the updating result of the data point;
the second updating unit is used for updating the linear regression equation according to the updated intermediate parameters;
the third updating unit is used for updating the sum of the squares of the total mean deviation and the sum of the squares of the residual errors according to the updated linear regression equation;
and updating the confidence of the blood oxygen saturation measurement result according to the updated total mean-squared-deviation sum and the updated residual sum-squared.
6. The system for recurrence of oximetry confidence according to claim 3, wherein: further comprising:
and the generating module is used for generating a pulse blood oxygen signal characteristic value according to the linear regression model.
7. The recursion system of the degree of confidence of the blood oxygen saturation measurement is characterized in that: the method comprises the following steps:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method of recursive blood oxygen saturation measurement confidence of claim 1 or 2.
8. A storage medium having stored therein instructions executable by a processor, the storage medium comprising: the processor-executable instructions, when executed by a processor, are for performing the method of recursive blood oxygen saturation measurement confidence of claim 1 or 2.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811482852.3A CN109512393B (en) | 2018-12-05 | 2018-12-05 | Recursive method, system and storage medium for measuring confidence of blood oxygen saturation |
| PCT/CN2019/116480 WO2020114190A1 (en) | 2018-12-05 | 2019-11-08 | Recursion method and system for confidence degree of blood oxygen saturation measurement, and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811482852.3A CN109512393B (en) | 2018-12-05 | 2018-12-05 | Recursive method, system and storage medium for measuring confidence of blood oxygen saturation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109512393A CN109512393A (en) | 2019-03-26 |
| CN109512393B true CN109512393B (en) | 2021-03-02 |
Family
ID=65794907
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811482852.3A Active CN109512393B (en) | 2018-12-05 | 2018-12-05 | Recursive method, system and storage medium for measuring confidence of blood oxygen saturation |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN109512393B (en) |
| WO (1) | WO2020114190A1 (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109512393B (en) * | 2018-12-05 | 2021-03-02 | 深圳技术大学 | Recursive method, system and storage medium for measuring confidence of blood oxygen saturation |
| CN112545461A (en) * | 2020-12-05 | 2021-03-26 | 深圳市美的连医疗电子股份有限公司 | Method, device and system for detecting non-invasive hemoglobin concentration value and computer readable storage medium |
| CN112603287B (en) * | 2020-12-16 | 2022-11-11 | 南昌逸勤科技有限公司 | Heart rate curve correction method and device applied to wearable equipment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6385471B1 (en) * | 1991-09-03 | 2002-05-07 | Datex-Ohmeda, Inc. | System for pulse oximetry SpO2 determination |
| CN104114090A (en) * | 2011-12-23 | 2014-10-22 | 通用电气公司 | Method, arrangement, sensor, and computer program product for non-invasively measuring hemoglobin concentrations in blood |
| CN105962949A (en) * | 2016-06-14 | 2016-09-28 | 上海理工大学 | Noninvasive blood glucose calculating method based on near-infrared light energy conservation law and signal collecting device |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8064975B2 (en) * | 2006-09-20 | 2011-11-22 | Nellcor Puritan Bennett Llc | System and method for probability based determination of estimated oxygen saturation |
| CN101347334B (en) * | 2007-07-19 | 2012-09-05 | 深圳迈瑞生物医疗电子股份有限公司 | Method and device for measuring blood oxygen saturation |
| US20090324033A1 (en) * | 2008-06-30 | 2009-12-31 | Nellcor Puritan Bennett Ireland | Signal Processing Systems and Methods for Determining Slope Using an Origin Point |
| CN101872444B (en) * | 2010-05-21 | 2012-07-25 | 杭州电子科技大学 | Batch-to-batch optimization method of batch process by combining medium-term correction strategy |
| CN102095526B (en) * | 2011-01-30 | 2012-07-25 | 中南大学 | Method for predicating gas temperature of circular cooler based on sintering heat loss calculation |
| CN106837305B (en) * | 2016-12-28 | 2020-06-09 | 中国石油天然气股份有限公司 | Method and device for determining underground liquid level depth of pumping well |
| CN108464836B (en) * | 2018-02-09 | 2021-09-03 | 重庆东渝中能实业有限公司 | System and method for detecting blood oxygen saturation for community medical treatment |
| CN109512393B (en) * | 2018-12-05 | 2021-03-02 | 深圳技术大学 | Recursive method, system and storage medium for measuring confidence of blood oxygen saturation |
-
2018
- 2018-12-05 CN CN201811482852.3A patent/CN109512393B/en active Active
-
2019
- 2019-11-08 WO PCT/CN2019/116480 patent/WO2020114190A1/en active Application Filing
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6385471B1 (en) * | 1991-09-03 | 2002-05-07 | Datex-Ohmeda, Inc. | System for pulse oximetry SpO2 determination |
| CN104114090A (en) * | 2011-12-23 | 2014-10-22 | 通用电气公司 | Method, arrangement, sensor, and computer program product for non-invasively measuring hemoglobin concentrations in blood |
| CN105962949A (en) * | 2016-06-14 | 2016-09-28 | 上海理工大学 | Noninvasive blood glucose calculating method based on near-infrared light energy conservation law and signal collecting device |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2020114190A1 (en) | 2020-06-11 |
| CN109512393A (en) | 2019-03-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US6393311B1 (en) | Method, apparatus and system for removing motion artifacts from measurements of bodily parameters | |
| US9737266B2 (en) | Systems and methods for determining respiration information from a photoplethysmograph | |
| US7991448B2 (en) | Method, apparatus, and system for removing motion artifacts from measurements of bodily parameters | |
| US8880576B2 (en) | Systems and methods for determining respiration information from a photoplethysmograph | |
| US6519486B1 (en) | Method, apparatus and system for removing motion artifacts from measurements of bodily parameters | |
| US9675274B2 (en) | Systems and methods for determining respiration information from a photoplethysmograph | |
| US9220440B2 (en) | Determining a characteristic respiration rate | |
| US9693709B2 (en) | Systems and methods for determining respiration information from a photoplethysmograph | |
| US9402554B2 (en) | Systems and methods for determining respiration information from a photoplethysmograph | |
| US20100274102A1 (en) | Processing Physiological Sensor Data Using a Physiological Model Combined with a Probabilistic Processor | |
| CN109512393B (en) | Recursive method, system and storage medium for measuring confidence of blood oxygen saturation | |
| US20190343406A1 (en) | System and Method for Noninvasively Measuring Ventricular Stroke Volume and Cardiac Output | |
| Yang et al. | Estimation and validation of arterial blood pressure using photoplethysmogram morphology features in conjunction with pulse arrival time in large open databases | |
| Solem et al. | Prediction of intradialytic hypotension using photoplethysmography | |
| WO2020114191A1 (en) | Blood oxygen saturation measurement confidence calculation method, system, and storage medium | |
| US9554712B2 (en) | Systems and methods for generating an artificial photoplethysmograph signal | |
| US20220233112A1 (en) | Method, computer programming product and electronic device for calculating blood oxygen saturation | |
| JP2023546451A (en) | System and method for blood pressure measurements from optical data | |
| CN112932475A (en) | Method and device for calculating blood oxygen saturation, electronic equipment and storage medium | |
| Ri et al. | Determination of Oxygen Saturation and Photoplethysmogram from Near Infrared Scattering Images |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: No. 3002, Lantian Road, Shijing street, Pingshan District, Shenzhen City, Guangdong Province Applicant after: Shenzhen Technical University Address before: 518118 Zhuyun garden, Pingshan District, Shenzhen City, Guangdong Province Applicant before: Shenzhen University of Technology |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |