CN111991003A - Savitzky-Golay filtering-based continuous blood glucose smoothing method, device, equipment and storage medium - Google Patents

Savitzky-Golay filtering-based continuous blood glucose smoothing method, device, equipment and storage medium Download PDF

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CN111991003A
CN111991003A CN202010808661.2A CN202010808661A CN111991003A CN 111991003 A CN111991003 A CN 111991003A CN 202010808661 A CN202010808661 A CN 202010808661A CN 111991003 A CN111991003 A CN 111991003A
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胡新
武俊帅
李洋
章霖
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Abstract

The invention discloses a Savitzky-Golay filtering-based continuous blood sugar smoothing method, which is characterized by comprising the following steps of: 1) reading t original data points from the dynamic blood glucose meter, wherein t is more than 5; 2) judging whether the read data points are abnormal or not, if so, removing the abnormal data points, and repeatedly reading the original data to replace the abnormal data; 3) and after determining that no abnormal data points exist, smoothing the read reorganized original data points by applying a Savitzky-Golay filter formula. The continuous blood sugar smoothing method can effectively remove noise and overcome the time delay problem existing in the prior smoothing technology. The required calculated amount of the continuous blood sugar smoothing method is in a linear relation with the number of the used historical data points, so the required calculated amount is small, and the continuous blood sugar smoothing method can be completely deployed on mobile equipment to run in real time.

Description

Savitzky-Golay filtering-based continuous blood glucose smoothing method, device, equipment and storage medium
Technical Field
The invention relates to the technical field of blood glucose monitoring, in particular to a Savitzky-Golay filtering-based continuous blood glucose smoothing method, and a device, equipment and a storage medium for realizing the method.
Background
The Continuous Glucose Monitoring System (CGMS) is a technique that can continuously detect the concentration of glucose in tissue fluid. The dynamic blood glucose meter can continuously detect and observe blood glucose changes and provide detailed time sequence data. This detailed information allows the diabetic patient to more effectively manage blood glucose levels, including reducing the number and time of blood glucose changes, hypoglycemia, and morning hyperglycemia, and glycated hemoglobin levels. However, despite the 10 years of development, the dynamic blood glucose meter technology still faces many challenges in many respects, such as sensitivity, stability, time lag between blood glucose and interstitial fluid, and the like. Among them, the most significant error of the dynamic blood glucose meter is due to its physical configuration and electrochemical properties to the random noise part. At present, digital filtering technology is mostly used to remove the random noise part to improve the signal quality. The signal y (t) received by the CGM sensor can be divided into two parts:
y(t)＝x(t)+n(t)， (1)
x (t) is the true blood glucose level at time t, and n (t) is random noise superimposed on the true signal. Generally, the spectrum of random noise is relatively broad, so the spectra of signal n (t) and noise n (t) tend to overlap. The usual low-pass filtering makes it impossible to remove the random noise n (t) from the detected blood glucose signal y (t) without loss. In addition, low pass filtering introduces large time delays during application, thereby distorting the true signal.
Common filters are Infinite impulse filter (IIR), Finite impulse Filter (FIR), Kalman filter (Kalman filter).
The infinite impulse filter has the following mathematical expression:
Figure BDA0002630107230000011
where y is the measured blood glucose value,
Figure BDA0002630107230000012
is the filtered blood glucose value. Current post-filter blood glucose value
Figure BDA0002630107230000013
Related to the previous N filtered blood glucose values and the M measured blood glucose values y. Finite impulse filterCan be expressed as
Figure BDA0002630107230000014
Current post-filter blood glucose value
Figure BDA0002630107230000021
Related to the current and previous M measured blood glucose values y. A simple example of a finite impulse filter is moving average filtering, where all data points are weighted equally. E.g. a window of 5 data points, the following 5 th order filter
Figure BDA0002630107230000022
An effective delay of 2.5 sampling events will occur.
Kalman filtering (Kalman filter) is also a commonly used blood glucose data filtering method. Kalman filtering is applied to linear systems with white noise, where the noise part follows a gaussian distribution. The kalman filter may be solved by an optimization problem for one mean square error. The kalman filtering theory can be described by the following system, in which the filtering operation is performed on the blood glucose signal yk:
xk+1＝f(xk，uk)+vk， (5)
yk＝h(xk，uk)+wk， (6)
where x is the internal state quantity of the system, u is the input to the system, y is the measured output, v is the system process noise, and w is the measured noise. The noise items of the system are assumed to be white noise, namely Gaussian distribution with the mean value of zero, and the covariance is respectively a matrix Q and a matrix R:
Figure BDA0002630107230000023
kalman filtering at each timeStep-calculating an internal state estimator
Figure BDA0002630107230000024
Sum state covariance matrix
Figure BDA0002630107230000025
The kalman filtering process may be expressed as the following mathematical formula:
Figure BDA0002630107230000026
Figure BDA0002630107230000027
Figure BDA0002630107230000028
Figure BDA0002630107230000029
Figure BDA00026301072300000210
where Φ is the state transition matrix and H is the measurement matrix. For the above kalman filter system, the covariance matrix Q of the process noise and the covariance matrix R of the measurement noise are two very important parameters. The estimation of these two parameters is important in order to obtain accurate filtering results. However, during filtering of the dynamic blood glucose meter signal, the noise level varies from patient to patient or from patient to patient at different times due to inter-individual and intra-individual differences. This presents great difficulty in the estimation of Q and R, and thus may seriously affect the accuracy of kalman filtering. On the other hand, the kalman filtering system has high requirements on both the calculation amount and the storage amount, and thus is a challenge for performing a real-time filtering operation on a mobile device. For diabetic patients, especially type 1 diabetic patients, it is very important to alarm abnormal blood glucose events in time, and if the alarm cannot be given in time due to noise or delay problems, adverse effects may be caused to the patients, so that the existing filtering smoothing method is further improved.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide a continuous blood sugar smoothing method based on Savitzky-Golay filtering, which has small calculation amount, can effectively remove noise and overcome the time delay problem in the prior smoothing technology.
A continuous blood sugar smoothing method based on Savitzky-Golay filtering is characterized by comprising the following steps:
1) reading t original data points from the dynamic blood glucose meter, wherein t is more than 5;
2) judging whether the read data points are abnormal or not, if so, removing the abnormal data points, and repeatedly reading the original data to replace abnormal data;
3) after determining no abnormal data points, smoothing the read reorganized original data points by applying the following formula
Figure BDA0002630107230000031
Preferably, the determination of the abnormal data point in step 2) is performed by the following steps: and judging abnormal data by using the sensitivity S as a threshold, and in the dynamic blood sugar measurement process, if the variation of the measured instantaneous blood sugar value compared with the last data point is more than S, determining that the current data is abnormal.
Preferably, the determination of the abnormal data point in step 2) is performed by the following steps: setting a threshold T, smoothing the instantaneous blood glucose value by smoothing method to obtain a smoothed value MgIf the current instantaneous blood sugar level G is equal to the average value MgGreater than T, i.e. | G-MgIf the value is greater than T, the current data is considered to be abnormal.
The patent also discloses a Savitzky-Golay-filtering-based continuous blood glucose smoothing device, comprising a data acquisition unit for reading t original data points from a dynamic blood glucose meter, t > 5;
an abnormal data point judging unit, which is used for judging whether the read data point is abnormal or not, if so, removing the abnormal data point, and repeatedly reading the original data to replace the abnormal data;
a smoothing unit for smoothing the read changed set of original data points by applying the following formula after determining no abnormal data points
Figure BDA0002630107230000032
Preferably, the abnormal data point determination unit determines the abnormal data by using the sensitivity S as a threshold, and in the dynamic blood glucose measurement process, if the measured instantaneous blood glucose value is greater than the change of the previous data point by more than S, the current data is considered to be abnormal.
Preferably, the abnormal data point determining unit sets a threshold T in the determination, and performs smoothing on the instantaneous blood glucose level by using a smoothing method to obtain a smoothed value MgIf the current instantaneous blood sugar level G is equal to the average value MgIs greater than T, i.e. | G-MgIf the value is greater than T, the current data is considered to be abnormal.
The patent also discloses a continuous blood glucose smoothing device based on Savitzky-Golay filtering, comprising a memory in which a computer program is stored and a processor which implements the above-mentioned method steps when executing said computer program.
Preferably, the device is a mobile device.
The patent also discloses a storage medium for storing a computer program which, when executed by a processor, implements the above-mentioned method steps.
Compared with the prior art, the invention has the following advantages: (1) by explicitly solving the optimization problem, and adding a suitable composite superposition, an explicit expression of the filter can be obtained. Therefore, the calculation amount is greatly reduced, and the mobile device is very easy to deploy; (2) through proper design, the filtering method of the invention does not introduce the problem of time delay, and can effectively improve the real-time performance of alarming abnormal blood glucose events; (3) by adopting different composite combinations, the filtering method can be suitable for people of different age groups and patients with type 1 and type 2 diabetes.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be readily apparent to those skilled in the art from the disclosure of the present specification.
Example embodiments will now be described more fully with reference to the accompanying examples. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the invention.
The patent discloses a Savitzky-Golay filtering-based continuous blood sugar smoothing method, which mainly comprises the following steps:
firstly, reading t original data points from a dynamic blood glucose meter, wherein t is more than 5, and the original data points can be read from the dynamic blood glucose meter by using mobile equipment, usually an APP (application) installed on a smart phone;
and then judging whether the read data points are abnormal or not, if so, removing the abnormal data points, and repeatedly reading the original data to replace the abnormal data. The determination method can first use the sensitivity S asAnd judging abnormal data by using a threshold value, and in the dynamic blood sugar measurement process, if the measured instantaneous blood sugar value is more than S than the change of the last data point, determining that the current data is abnormal. Or setting a threshold T, smoothing instantaneous blood glucose value by smoothing method to obtain smoothed value MgIf the current instantaneous blood sugar level G is equal to the average value MgIs greater than T, i.e. | G-MgIf the value is greater than T, the current data is considered to be abnormal.
And finally, smoothing the blood sugar by adopting a Savitzky-Golay filtering method, wherein the Savitzky-Golay filtering method is provided by Savitzky and Golay in the earliest 1964 when the noise of the chemical spectrum analyzer is researched to be smoothed. In the process of smoothing time series data, other smoothing algorithms often cannot extract certain features of the real data spectrum peak, so that the features such as relative width and height can be flattened. In contrast, Savitzky-Golay filtering is advantageous in that the algorithm can preserve certain characteristics of the time series, such as local maxima and local minima.
The basic working principle of Savitzky-Golay filtering is to approximate a series of data points within a window with a local low-order polynomial. In particular for a polynomial of order m
y(t)＝a0+a1t+a2t2+…+amtm，
And 2n +1 consecutive data points yt-n，yt-n+1，…，yt，…，yt+n-1，yt+nAnd m < 2n +1, we can get the local optimal solution a by solving the following optimization problem0，a1，a2，…，am:
Figure BDA0002630107230000051
If the fetch window size is 2n + 1-5, a third order polynomial (m-3) is used as a simple example. Through a series of algebraic operations, we can get the following smoothing formula:
Figure BDA0002630107230000052
Figure BDA0002630107230000053
Figure BDA0002630107230000054
Figure BDA0002630107230000055
Figure BDA0002630107230000056
in equations (13) to (16), the smoothed data are filtered
Figure BDA0002630107230000057
To varying degrees, depend on future data points and introduce delay problems and are therefore unsuitable for real-time filtering.
In equation (17), the smoothed data is filtered
Figure BDA0002630107230000058
Only the historical data points are relied upon and thus can be used for real-time filtering. However, in a normal application process, the ideal denoising effect cannot be obtained by using only the formula (17). One approach is to attempt to approximate the series of data in the window using higher order polynomials in the optimization problem (12). The disadvantage of this approach is that it may produce overfitting conditions, resulting in poor denoising results.
The present invention employs another method of the basic formula (17). We compound equation (17) differently based on the actual data. In this case, we still use a 3 rd order polynomial in the optimization problem (12), but the history number usedMore than 5 points are counted. The formula adopted is
Figure BDA0002630107230000059
We used 7 historical data points:
Figure BDA00026301072300000510
this patent still discloses a continuous blood sugar smoothing device based on Savitzky-Golay filtering, it includes: a data acquisition unit for reading t raw data points from a dynamic blood glucose meter, t > 5; the abnormal data point judging unit is used for judging whether the read data point is abnormal or not, if so, removing abnormal data points, and repeatedly reading the original data to replace the abnormal data; a smoothing unit for smoothing the read reorganized raw data points by applying the following formula after determining no abnormal data points
Figure BDA0002630107230000061
It should be noted that, other corresponding descriptions of the functional units involved in the continuous blood glucose smoothing device based on Savitzky-Golay filtering provided in this embodiment may refer to the description of the above method, and are not repeated herein.
In order to achieve the above object, the present application further provides a continuous blood glucose smoothing device based on Savitzky-Golay filtering, which may be specifically a personal computer, a server, a network device, a mobile device, and the like, where the physical device includes a storage medium and a processor; a storage medium for storing a computer program; a processor for executing a computer program to implement the above-described Savitzky-Golay-filter-based continuous glucose smoothing method. The continuous blood sugar smoothing method can effectively remove noise and overcome the time delay problem existing in the prior smoothing technology. In addition, the required calculated amount of the continuous blood sugar smoothing method is in a linear relation with the number of used historical data points, so that the required calculated amount is small, and the continuous blood sugar smoothing method can be completely deployed on mobile equipment to run in real time.
Accordingly, the present embodiment also provides a storage medium having stored thereon a computer program that, when executed by a processor, implements the continuous blood glucose smoothing method described above. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.), and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments of the present application.
Optionally, the computer device may also include a user interface, a network interface, a camera, radio frequency circuitry, sensors, audio circuitry, a WI-FI module, and so forth. The user interface may include a Display screen (Display), an input unit such as a keypad (Keyboard), etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (e.g., a bluetooth interface, WI-FI interface), etc. The storage medium may further include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the computer device described above, supporting the operation of information handling programs and other software and/or programs. The network communication module is used for realizing communication among components in the storage medium and communication with other hardware and software in the physical device.
Those skilled in the art will appreciate that the physical device structure of the personality prediction of the interviewer provided in the present embodiment does not constitute a limitation of the physical device, and may include more or fewer components, or combine certain components, or arrange different components.
Compared with the prior art, the invention has the following advantages: (1) by explicitly solving the optimization problem, and adding appropriate complex superposition, an explicit expression for the filter can be obtained. Therefore, the calculation amount is greatly reduced, and the mobile device is very easy to deploy; through proper design, the filtering method of the invention does not introduce the problem of time delay, and can effectively improve the real-time performance of alarming abnormal blood glucose events; (3) by adopting different composite combinations, the filtering method can be suitable for people of different age groups and patients with type 1 and type 2 diabetes.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which may be accomplished by those skilled in the art without departing from the spirit and scope of the present invention as set forth in the appended claims.

Claims (9)

1. A continuous blood sugar smoothing method based on Savitzky-Golay filtering is characterized by comprising the following steps:
1) reading t original data points from the dynamic blood glucose meter, wherein t is more than 5;
2) judging whether the read data points are abnormal or not, if so, removing the abnormal data points, and repeatedly reading the original data to replace the abnormal data;
3) after determining no abnormal data points, smoothing the read reorganized original data points by applying the following formula
Figure FDA0002630107220000011
2. The Savitzky-Golay-filter-based continuous glucose smoothing method according to claim 1, wherein: judging abnormal data points in the step 2) through the following steps: and judging abnormal data by using the sensitivity S as a threshold, and in the dynamic blood sugar measurement process, if the variation of the measured instantaneous blood sugar value is larger than S than that of the last data point, determining that the current data is abnormal.
3. The Savitzky-Golay-filter-based continuous glucose smoothing method according to claim 1, wherein: judging abnormal data points in the step 2) through the following steps: setting a threshold T, smoothing the instantaneous blood glucose value by smoothing method to obtain a smoothed value MgIf the current instantaneous blood sugar level G is equal to the average value MgIs greater than T, i.e. | G-MgIf the value is greater than T, the current data is considered to be abnormal.
4. A continuous glucose smoothing device based on Savitzky-Golay filtering, comprising:
a data acquisition unit for reading t raw data points from a dynamic blood glucose meter, t > 5;
an abnormal data point judging unit, which is used for judging whether the read data point is abnormal or not, if so, removing the abnormal data point, and repeatedly reading the original data to replace the abnormal data;
a smoothing unit for smoothing the read shuffled raw data points after determining no abnormal data points using the following formula
Figure FDA0002630107220000012
5. The Savitzky-Golay-filter-based continuous blood glucose smoothing device according to claim 4, wherein the abnormal data point judgment unit judges abnormal data with a sensitivity S as a threshold, and considers that the current data is abnormal if the measured instantaneous blood glucose value is changed more than S from the previous data point during the dynamic blood glucose measurement.
6. The Savitzky-Golay-filter-based continuous blood glucose smoothing device according to claim 4, wherein the abnormal data point determining unit sets a threshold T at the time of determining, and uses a smoothing method to smooth the instantaneous blood glucoseSmoothing the blood sugar value to obtain a smoothed value MgIf the current instantaneous blood sugar level G is equal to the average value MgIs greater than T, i.e. | G-MgIf the value is greater than T, the current data is considered to be abnormal.
7. A Savitzky-Golay filter-based continuous glucose smoothing device, comprising: comprising a memory and a processor, in which a computer program is stored, characterized in that the steps of the method according to any of claims 1 to 3 are implemented when the processor executes the computer program.
8. The Savitzky-Golay-filter-based continuous glucose smoothing device of claim 7,
the device is a mobile device.
9. A storage medium for storing a computer program, characterized in that the computer program, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 3.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112716490A (en) * 2020-12-24 2021-04-30 上海萌草科技有限公司 Continuous blood glucose calibration method and device based on weighted linear regression
CN117612737A (en) * 2024-01-24 2024-02-27 胜利油田中心医院 Intelligent optimization method for diabetes care data

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030125612A1 (en) * 2001-12-27 2003-07-03 Fox James Kelly System for monitoring physiological characteristics
WO2009136372A1 (en) * 2008-05-08 2009-11-12 Universita' Degli Studi Padova Method and device for processing glycemia level data by means of self-adaptive filtering, predicting the future glycemia level and generating alerts
US20120215087A1 (en) * 2009-11-02 2012-08-23 Universita Degli Studi Di Padova Method to recalibrate continuous glucose monitoring data on-line
CN104921736A (en) * 2015-06-08 2015-09-23 浙江大学 Continuous blood glucose monitoring device comprising parameter estimation function filtering module
CN105559797A (en) * 2015-12-23 2016-05-11 上海第二工业大学 Portable dynamic continuous blood glucose monitoring instrument oriented at medical IoT (Internet of Things)
CN106264555A (en) * 2016-10-17 2017-01-04 南方科技大学 Blood-sugar detecting instrument
CN107137093A (en) * 2017-04-20 2017-09-08 浙江大学 A kind of continuous blood sugar monitoring device for including abnormal plasma glucose probability alarm
CN109682976A (en) * 2019-02-28 2019-04-26 东北大学 Continuous blood sugar based on multi-model fusion monitors the online fault detection method of sensor
CN111381540A (en) * 2020-03-19 2020-07-07 乐普医学电子仪器股份有限公司 Control device applied to continuous blood glucose monitoring sensor

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030125612A1 (en) * 2001-12-27 2003-07-03 Fox James Kelly System for monitoring physiological characteristics
WO2009136372A1 (en) * 2008-05-08 2009-11-12 Universita' Degli Studi Padova Method and device for processing glycemia level data by means of self-adaptive filtering, predicting the future glycemia level and generating alerts
US20120215087A1 (en) * 2009-11-02 2012-08-23 Universita Degli Studi Di Padova Method to recalibrate continuous glucose monitoring data on-line
CN104921736A (en) * 2015-06-08 2015-09-23 浙江大学 Continuous blood glucose monitoring device comprising parameter estimation function filtering module
CN105559797A (en) * 2015-12-23 2016-05-11 上海第二工业大学 Portable dynamic continuous blood glucose monitoring instrument oriented at medical IoT (Internet of Things)
CN106264555A (en) * 2016-10-17 2017-01-04 南方科技大学 Blood-sugar detecting instrument
CN107137093A (en) * 2017-04-20 2017-09-08 浙江大学 A kind of continuous blood sugar monitoring device for including abnormal plasma glucose probability alarm
CN109682976A (en) * 2019-02-28 2019-04-26 东北大学 Continuous blood sugar based on multi-model fusion monitors the online fault detection method of sensor
CN111381540A (en) * 2020-03-19 2020-07-07 乐普医学电子仪器股份有限公司 Control device applied to continuous blood glucose monitoring sensor

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
FAHREDDIN SADIKOGLU ET AL: "Filtering continuous glucose monitoring signal using Savitzky-Golay filter and Simple Multivariate Thresholding", 《PROCEDIA COMPUTER SCIENCE》, 30 August 2016 (2016-08-30), pages 342 - 350 *
谢军 等: "血糖近红外光谱分析的Savitzky-Golay 平滑模式与偏最小二乘法因子数的联合优选", 《分析化学研究报告》, 31 March 2010 (2010-03-31), pages 342 - 346 *

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