WO2023093457A1 - 连续血糖检测方法、系统及可读存储介质 - Google Patents

连续血糖检测方法、系统及可读存储介质 Download PDF

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WO2023093457A1
WO2023093457A1 PCT/CN2022/128295 CN2022128295W WO2023093457A1 WO 2023093457 A1 WO2023093457 A1 WO 2023093457A1 CN 2022128295 W CN2022128295 W CN 2022128295W WO 2023093457 A1 WO2023093457 A1 WO 2023093457A1
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interstitial fluid
signal
low
amplitude
phase
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PCT/CN2022/128295
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French (fr)
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刘小凡
吴之夏
陈勋
裘丹
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上海微创生命科技有限公司
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—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
    • A61B5/1486—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 enzyme electrodes, e.g. with immobilised oxidase
    • 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
    • A61B5/14532—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 for measuring glucose, e.g. by tissue impedance measurement
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters

Definitions

  • the present application relates to the field of medical technology, in particular to a continuous blood glucose detection method, system and readable storage medium.
  • CGM Continuous glucose monitoring
  • BG blood glucose
  • IG interstitial fluid glucose
  • amplitude deviation and phase deviation are the decisive factors affecting the accuracy of CGM detection, which directly affect the mean relative difference (MARD, Mean Average Relative Difference) for measuring the accuracy and practicability of CGM detection. Therefore, how to effectively reduce the amplitude deviation and phase deviation between BG-IG during CGM detection is an urgent problem to be solved at present.
  • the purpose of this application is to provide a continuous blood glucose detection method, system and readable storage medium to solve the problem of accurate detection caused by excessive amplitude deviation and phase deviation between BG-IG when using the existing CGM detection method for CGM detection Problems of low performance and practicality.
  • a continuous blood glucose detection method including:
  • the interstitial fluid glucose signal is subjected to low-pass filter deconvolution processing corresponding to the amplitude and corresponding phase, so as to obtain the blood glucose signal.
  • performing low-pass filter deconvolution processing on the interstitial fluid glucose signal with corresponding amplitude and corresponding phase includes:
  • the interstitial fluid glucose signal is low-pass filtered and reversed using the low-pass filter model of the second amplitude and the second phase. convolution processing;
  • the pass filter model performs low-pass filter deconvolution processing on the interstitial fluid glucose signal
  • the first amplitude, the second amplitude, and the third amplitude decrease sequentially, and the first phase, the second phase, and the third phase decrease sequentially.
  • the set time is 15 minutes, and the first set value and the second set value are 14.9 mg/dL-15.1 mg/dL.
  • the following low-pass filter model is used to perform low-pass filter deconvolution processing on the interstitial fluid glucose signal corresponding to the amplitude and corresponding phase:
  • IG represents the interstitial fluid glucose signal
  • BG represents the blood glucose signal
  • ⁇ represents the diffusion coefficient
  • t represents the signal acquisition time
  • the continuous blood glucose detection method after obtaining the blood glucose signal, the continuous blood glucose detection method further includes:
  • Low-order filtering is performed on the blood sugar signal to obtain a linear blood sugar signal.
  • the method for obtaining the interstitial fluid glucose signal includes:
  • the electrical signal is converted to an interstitial fluid glucose signal.
  • the present application also provides a continuous blood glucose detection system, including:
  • a signal acquisition module configured to acquire interstitial fluid glucose signals
  • a calculation module configured to perform low-pass filtering and deconvolution processing on the interstitial fluid glucose signal by adopting a low-pass filtering model corresponding to the amplitude and corresponding phase according to the change rate of the interstitial fluid glucose signal to obtain a blood glucose signal .
  • the calculation module adopts a low-pass filter model with corresponding amplitude and corresponding phase according to the difference in the rate of change of the interstitial fluid glucose signal to analyze the interstitial fluid glucose signal.
  • Performing low-pass filter deconvolution processing includes:
  • the interstitial fluid glucose signal is low-pass filtered and reversed using the low-pass filter model of the second amplitude and the second phase. convolution processing;
  • the pass filter model performs low-pass filter deconvolution processing on the interstitial fluid glucose signal
  • the first amplitude, the second amplitude, and the third amplitude decrease sequentially, and the first phase, the second phase, and the third phase decrease sequentially.
  • the set time is 15 minutes, and the first set value and the second set value are 14.9 mg/dL ⁇ 15.1 mg/dL.
  • the continuous blood glucose detection system further includes:
  • the signal processing module is configured to perform low-order filtering processing on the blood sugar signal to obtain a linear blood sugar signal.
  • the signal acquisition module acquiring the interstitial fluid glucose signal includes:
  • the electrical signal is converted to an interstitial fluid glucose signal.
  • the present application also provides a readable storage medium, where a computer program is stored on the readable storage medium, and when the computer program is executed by a processor, the continuous blood glucose detection method as described in any one of the above items is realized.
  • the continuous blood glucose detection method, system and readable storage medium include: obtaining the interstitial fluid glucose signal;
  • the phase low-pass filter model performs low-pass filter deconvolution processing on the interstitial fluid glucose signal to obtain a blood glucose signal. That is, according to the difference in the rate of change of the interstitial fluid glucose signal, low-pass filter models with different amplitudes and phases are used to calculate the interstitial fluid glucose signal in segments to obtain a more accurate blood glucose signal, thus solving the filter band The problem of the extra time delay and the dynamic time delay and amplitude deviation caused by blood glucose and interstitial fluid glucose at different rates of change.
  • Fig. 1 is a flow chart of the continuous blood glucose detection method provided by the embodiment of the present application.
  • Fig. 2 is an example diagram of the value range of the first amplitude and the first phase in the embodiment of the present application
  • FIG. 3 is an example diagram of the value range of the second amplitude and the second phase in the embodiment of the present application
  • Fig. 4 is an example diagram of the value range of the third amplitude and the third phase in the embodiment of the present application.
  • Fig. 5 is an example diagram of the relationship between BG and IG when the blood glucose concentration changes in the embodiment of the present application is relatively stable;
  • Figure 6 is an example diagram of the relationship between BG and IG when the blood glucose concentration rises rapidly in the embodiment of the present application;
  • Figure 7 is an example diagram of the relationship between BG and IG when the blood glucose concentration drops rapidly in the embodiment of the present application
  • Fig. 8 is a waveform comparison diagram of IG 0 (t), IG 1 (t) and BG (t) using the continuous blood glucose detection method provided by this embodiment;
  • Fig. 9 is a comparison diagram before and after the low-order filtering processing of the blood glucose signal by using the MA filter in the embodiment of the present application.
  • FIG. 10 is a comparison diagram before and after low-order filtering of the blood glucose signal by using the S-G filter in the embodiment of the present application;
  • Fig. 11 is a block diagram of the continuous blood glucose detection system provided by the embodiment of the present application.
  • the inventor has found that the delay between BG-IG is different in the state of signal steady-state change, the state of rapid growth and the state of rapid decline, so the delay between IG-BG is determined by using the low-pass filter model. When compensating, using a single processing mode is not optimal for eliminating latency.
  • the embodiment of the present application provides a continuous blood glucose (CGM) detection method
  • the CGM detection method includes the following steps:
  • a low-pass filter model with corresponding amplitude and corresponding phase is used to perform low-pass filtering and deconvolution processing on the interstitial fluid glucose signal to obtain a blood glucose signal (blood glucose Signal).
  • the continuous blood glucose detection method uses low-pass filter models with different amplitudes and phases to calculate the interstitial fluid glucose signal in segments according to the difference in the rate of change of the interstitial fluid glucose signal, so as to obtain a more accurate Accurate blood glucose signal, thus solving the problem of the extra delay brought by the filter and the dynamic time delay and amplitude deviation caused by blood glucose and interstitial fluid glucose at different rates of change.
  • acquiring the interstitial fluid glucose signal may include: acquiring an electrical signal collected by a sensor reacting with the interstitial fluid glucose, and converting the electrical signal into an interstitial fluid glucose signal.
  • a sensor reacting with the interstitial fluid glucose
  • converting the electrical signal into an interstitial fluid glucose signal there is a linear relationship between the electrical signal collected by the sensor and the interstitial fluid glucose reaction and the interstitial fluid glucose signal, so after obtaining the electrical signal, the linear relationship between the two can be used to The electrical signal is converted to an interstitial fluid glucose signal.
  • the amplitude of the high-frequency signal is attenuated and the low-frequency signal is greatly shifted in phase. From the perspective of signal processing, although this processing method can compensate the phase shift of high-frequency signals and low-frequency signals, it will also intervene in the amplitude and phase of low-frequency signals that should be relatively stable. , the compensation for the amplitude deviation of the low-frequency signal is insufficient.
  • step S12 of this embodiment specifically, according to the difference in the rate of change of the interstitial fluid glucose signal, a low-pass filter model with corresponding amplitude and corresponding phase is used to perform low-pass filtering and deconvolution on the interstitial fluid glucose signal.
  • Product processing can include:
  • the interstitial fluid glucose signal is low-pass filtered and reversed using the low-pass filter model of the second amplitude and the second phase. convolution processing;
  • the pass filter model performs low-pass filter deconvolution processing on the interstitial fluid glucose signal
  • the first amplitude, the second amplitude, and the third amplitude decrease sequentially, and the first phase, the second phase, and the third phase decrease sequentially. That is, the first amplitude, the second amplitude, and the third amplitude can be interpreted as high amplitude, medium amplitude, and low amplitude in sequence, and the first phase, the second phase, and the third phase can be understood as In turn, it is understood as high phase, medium phase, and low phase.
  • the set time may be 15 minutes, and the values of the first set value and the second set value It may be 14.9 mg/dL to 15.1 mg/dL, for example, specifically 15 mg/dL.
  • the low-pass filter model of the first amplitude and the first phase is used to perform low-pass filter deconvolution processing on the interstitial fluid glucose information ; If the detected blood glucose level drops by more than 15 mg/dL within 15 minutes, it is determined that the state of blood glucose change is a rapid decline, and the low-pass filter model of the second amplitude and second phase is used to perform low-pass filter deconvolution processing on the interstitial fluid glucose information ; If the detected blood glucose level change within 15min is between +15mg/dL and -15mg/dL, it is determined that the state of blood glucose change is a steady change, and then the low-pass filter model of the third amplitude and the third phase is used to filter the interstitial fluid Glucose signals were processed by low-pass filtering and deconvolution.
  • the blood glucose signal is calculated using the low-amplitude and low-phase low-pass filter model; when the interstitial fluid glucose signal drops rapidly, the high-amplitude and high-phase A low-pass filter model is used to calculate the blood glucose signal.
  • the low-pass filter model of medium amplitude and medium phase is used to calculate the blood glucose signal.
  • the low-pass filter with the first amplitude and the first phase is used to perform low-pass filter deconvolution processing with high amplitude and high phase offset on the low-frequency signal, wherein the The first amplitude and the first phase satisfy the low-frequency part corresponding to the normalized frequency 0.1 ⁇ rad/sample, the amplitude attenuation of the filter reaches -31dB, and the phase attenuation reaches -62 degrees, which can maximize the signal
  • the amplitude of the low frequency part is compensated to achieve the estimation of blood glucose in the state of rapid rise.
  • the low-pass filter of the second amplitude and the second phase performs low-pass filter deconvolution processing on the low-frequency part signal with a medium amplitude and a medium phase offset, wherein the second amplitude .
  • the second phase satisfies the amplitude attenuation of the filter at the low frequency part corresponding to the normalized frequency 0.1 ⁇ rad/sample to reach -4dB, the phase attenuation reaches -25 degrees, and the amplitude of the low frequency part of the signal can be moderately limited Compensation, in order to achieve the estimation of blood glucose in the state of rapid decline.
  • the low-pass filter of the third amplitude and the third phase performs low-pass filter inversion of low amplitude and low phase offset on its low-frequency signal, wherein the third amplitude, the third phase
  • the above-mentioned third phase satisfies the amplitude attenuation of the filter at the low frequency part corresponding to the normalized frequency 0.1 ⁇ rad/sample to reach -1dB, and the phase attenuation to -0.25 degrees to achieve the calculation of blood glucose in a steady state.
  • the first amplitude, the second amplitude, the third amplitude, the first phase, the second phase, and the third phase correspond to normalized
  • the specific value when the frequency is 0.1 ⁇ rad/sample is only used to illustrate the mutual relationship and does not constitute a limitation to this application.
  • the normalized frequency of 0.1 ⁇ rad/sample is used to filter
  • the high, medium and low phase and amplitude in this embodiment are distinguished by the value of the phase and amplitude of the device, because the normalized frequency 0.1 ⁇ rad/sample can be used to better explain the low frequency part of the signal, and it does not It cannot be construed as a limitation of this application.
  • the relationship between the blood glucose signal (BG) and the interstitial fluid glucose signal (IG) is shown in Figure 5. It can be seen from Figure 5 that the phase deviation between BG and IG is small , and there is only a small amplitude deviation, so the blood glucose signal can be better calculated by performing low-pass filter deconvolution with low amplitude and low phase offset on the low-frequency signal.
  • the low-pass filtering model adopted can be as follows:
  • IG represents the interstitial fluid glucose signal
  • BG represents the blood glucose signal
  • ⁇ represents the diffusion coefficient
  • t represents the signal acquisition time
  • the model is solved by using the differential state expression of the diffusion relationship between interstitial fluid and blood.
  • This formula interprets the interstitial fluid signal as a low-pass filter on the blood glucose signal.
  • ⁇ is the highest prior probability of data statistics.
  • the coefficients are used initially and adjusted by the acquired individual signals.
  • the solution of this model is based on the steady state of the signal.
  • the blood glucose value detected by this model is not the optimal solution.
  • the corresponding amplitude and corresponding phase are used to perform low-pass filter deconvolution processing on IG(t), Then the blood sugar signal is obtained, so that the detected blood sugar value can always maintain the optimal solution.
  • FIG. 8 it shows that the blood glucose continuous detection method provided by this embodiment is used to obtain IG 0 (t) in step S11, and then use step S12 to perform low-pass filter deconvolution processing on IG (t).
  • the obtained IG 1 (t) and the signal waveform diagram of the finally detected BG(t) it can be seen from the figure that the amplitude deviation and phase deviation between IG 1 (t) and BG(t) are smaller than IG 0
  • the amplitude deviation and phase deviation between (t) and BG(t) that is, the amplitude deviation and phase deviation between BG-IG can be reduced by using the blood glucose continuous detection method provided in this embodiment.
  • the continuous blood glucose detection method provided in this embodiment further includes: step S13, performing low-order filtering processing on the blood glucose signal.
  • smoothing filters such as a smoothing average filter (MA filter) and an S-G filter (Savitzky-Golay Filter) can be used to perform low-order filtering on the blood sugar signal to obtain a linear blood sugar signal.
  • MA filter smoothing average filter
  • S-G filter Savitzky-Golay Filter
  • the S-G filter is used to perform low-order filtering processing on the blood sugar signal. It can be seen from FIG. 9 that when the MA filter is used to perform low-order filtering processing on the blood sugar signal, the time delay However, it can be seen from Figure 10 that when using a filter to perform low-order filtering on the blood glucose signal, the low-order polynomial is used to recursively fit the curve, which ensures the elimination of noise and reduces time delay.
  • the embodiment of the present application also provides a continuous blood glucose detection system, including:
  • a signal acquisition module 100 configured to acquire interstitial fluid glucose signals
  • the calculation module 200 is used to perform low-pass filtering and deconvolution processing on the interstitial fluid glucose signal by adopting a low-pass filtering model corresponding to the amplitude and corresponding phase according to the change rate of the interstitial fluid glucose signal, so as to obtain blood glucose Signal.
  • the continuous blood glucose detection system provided in the embodiment of the present application further includes:
  • the signal processing module 300 is configured to perform low-order filtering processing on the blood glucose signal.
  • the signal acquisition module 100 is used to realize the step S11 shown in FIG. 1
  • the calculation module 200 is used to realize the step S12 shown in FIG. 1
  • the signal processing module 300 is used to realize the steps shown in FIG. 1 S13. Therefore, for the specific description of the functions that can be realized by the signal acquisition module 100, the calculation module 200 and the signal processing module 300, please refer to the related steps S11-S13 shown in FIG. 1 in the above continuous blood glucose detection method. Description, no more repetitions.
  • the continuous blood glucose detection system can achieve similar technical effects to the aforementioned continuous blood glucose detection method, which will not be repeated here.
  • the signal acquisition module 100, the calculation module 200, and the signal processing module 300 can be implemented in one device, or any one of the modules can be split or, in the continuous blood glucose detection system, at least part of the functions of one or more modules of the signal acquisition module 100, the calculation module 200, and the signal processing module 300 can be combined with at least part of other modules Some functions are combined and implemented in one function module.
  • at least one of the signal acquisition module 100, the calculation module 200, and the signal processing module 300 may be at least partially implemented as a computer program module, when When the program is run by the computer, it can execute the functions of the corresponding modules as shown in FIG. 1 .
  • This embodiment also provides a readable storage medium, and a computer program is stored in the readable storage medium, and when the computer program is executed by a processor, the continuous blood glucose detection method provided by this embodiment is realized.
  • the readable storage medium may be a tangible device capable of holding and storing instructions for use by an instruction execution device, such as but not limited to an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or the above-mentioned any suitable combination. More specific examples (a non-exhaustive list) of readable storage media include: portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanically encoded device, and any suitable combination of the above.
  • an instruction execution device such as but not limited to an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or the above-mentioned any suitable combination. More specific examples (a non-exhaustive list) of readable storage media include: portable computer disks, hard disks
  • the computer programs described herein may be downloaded from a readable storage medium to respective computing/processing devices, or to external computers or external storage devices over a network, such as the Internet, local area network, wide area network, and/or wireless network.
  • the computer program can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server .
  • the processor can be a device with data processing capability and/or program execution capability such as a central processing unit (CPU), a network processor (NP), a tensor processing unit (TPU) or a graphics processing unit (GPU); It is a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component.
  • the central processing unit (CPU) may be an X86 or ARM architecture or the like.
  • the continuous blood glucose detection method, system and readable storage medium include: obtaining the interstitial fluid glucose signal;
  • the phase low-pass filter model performs low-pass filter deconvolution processing on the interstitial fluid glucose signal to obtain a blood glucose signal. That is, according to the difference in the rate of change of the interstitial fluid glucose signal, low-pass filter models with different amplitudes and phases are used to calculate the interstitial fluid glucose signal in segments to obtain a more accurate blood glucose signal, thus solving the filter band The problem of the extra time delay and the dynamic time delay and amplitude deviation caused by blood glucose and interstitial fluid glucose at different rates of change.

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Abstract

一种连续血糖检测方法、系统及可读存储介质，包括：获取组织间液葡萄糖信号（S11）；以及，根据所述组织间液葡萄糖信号的变化速率的不同采取对应幅度及对应相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理，以得到血糖信号（S12）。即，根据组织间液葡萄糖信号变化速率的不同，搭配不同幅度及相位的低通滤波模型对组织间液葡萄糖信号进行分段式计算，以得到更为精确的血糖信号，从而解决了滤波器带来的额外延时及血液葡萄糖与组织间液葡萄糖在不同变化速率下所产生的动态时延与幅度偏差的问题。

Description

连续血糖检测方法、系统及可读存储介质 技术领域
本申请涉及医疗技术领域，特别涉及一种连续血糖检测方法、系统及可读存储介质。
背景技术
连续血糖监测(CGM，Continuous Glucose Monitoring)检测数据的取得基于传感器与组织间液之间的反应，通过传感器的酶层与组织间液葡萄糖产生氧化还原反应并形成电信号，而后对电信号进行处理以获得当前的血糖值。但一直以来对CGM检测在临床上的应用的有两个限制条件：(1)电信号的噪声；(2)血液葡萄糖(BG)与组织间液葡萄糖(IG)存在的幅度偏差与延时偏差。其中，幅度偏差与相位偏差(延时偏差)为影响CGM检测准确性的决定性因素，直接影响了衡量CGM检测准确性与实用性的平均相对差(MARD，Mean Average Relative Difference)。因此，在进行CGM检测时，如何能有效减小BG-IG之间的幅度偏差与相位偏差是目前亟待解决的问题。
发明内容
本申请的目的在于提供一种连续血糖检测方法、系统及可读存储介质，以解决在利用现有CGM检测方法进行CGM检测时，BG-IG之间的幅度偏差与相位偏差过大导致检测准确性与实用性不高的问题。
为解决上述技术问题，本申请提供一种连续血糖检测方法，包括：
获取组织间液葡萄糖信号；以及，
根据所述组织间液葡萄糖信号的变化速率的不同，对所述组织间液葡萄糖信号进行对应幅度及对应相位的低通滤波逆卷积处理，以得到血糖信号。
可选的，根据所述组织间液葡萄糖信号的变化速率的不同，对所述组织间液葡萄糖信号进行对应幅度及对应相位的低通滤波逆卷积处理包括：
若所述组织间液葡萄糖信号在设定时间内的上升值大于第一设定值，则利用第一幅度及第一相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理；
若所述组织间液葡萄糖信号在设定时间内的下降值大于第二设定值，则利用第二幅度及第二相位的低通滤波模型对所述组织间液葡萄糖信号进行低 通滤波逆卷积处理；
若所述组织间液葡萄糖信号在设定时间内的上升值不大于所述第一设定值，且下降值不大于所述第二设定值，则利用第三幅度及第三相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理；
其中，所述第一幅度、所述第二幅度、所述第三幅度依次降低，所述第一相位、所述第二相位、所述第三相位依次降低。
可选的，在所述的连续血糖检测方法中，所述设定时间为15min，所述第一设定值和所述第二设定值为14.9mg/dL～15.1mg/dL。
可选的，采用如下低通滤波模型对所述组织间液葡萄糖信号进行对应幅度及对应相位的低通滤波逆卷积处理：
Figure PCTCN2022128295-appb-000001
其中，IG表示组织间液葡萄糖信号，BG表示血糖信号，τ表示扩散系数，t表示信号采集时间。
可选的，在所述的连续血糖检测方法中，在得到所述血糖信号之后，所述连续血糖检测方法还包括：
对所述血糖信号进行低阶滤波处理，以得到线性血糖信号。
可选的，在所述的连续血糖检测方法中，获取组织间液葡萄糖信号的方法包括：
获取传感器与组织间液葡萄糖反应所采集的电信号，以及，
将所述电信号转换为组织间液葡萄糖信号。
本申请还提供一种连续血糖检测系统，包括：
信号获取模块，用于获取组织间液葡萄糖信号；以及，
计算模块，用于根据所述组织间液葡萄糖信号的变化速率的不同采取对应幅度及对应相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理，以得到血糖信号。
可选的，在所述的连续血糖检测系统中，所述计算模块根据所述组织间液葡萄糖信号的变化速率的不同采取对应幅度及对应相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理包括：
若所述组织间液葡萄糖信号在设定时间内的上升值大于第一设定值，则利用第一幅度及第一相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理；
若所述组织间液葡萄糖信号在设定时间内的下降值大于第二设定值，则利用第二幅度及第二相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理；
若所述组织间液葡萄糖信号在设定时间内的上升值不大于所述第一设定值，或下降值不大于所述第二设定值，则利用第三幅度及第三相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理；
其中，所述第一幅度、所述第二幅度、所述第三幅度依次降低，所述第一相位、所述第二相位、所述第三相位依次降低。
可选的，在所述的连续血糖检测系统中，所述设定时间为15min，所述第一设定值和所述第二设定值为14.9mg/dL～15.1mg/dL。
可选的，在所述的连续血糖检测系统中，所述连续血糖检测系统还包括：
信号处理模块，用于对所述血糖信号进行低阶滤波处理，以得到线性血糖信号。
可选的，在所述的连续血糖检测系统中，所述信号获取模块获取组织间液葡萄糖信号包括：
获取传感器与组织间液葡萄糖反应所采集的电信号，以及，
将所述电信号转换为组织间液葡萄糖信号。
本申请还提供一种可读存储介质，所述可读存储介质上存储有计算机程序，所述计算机程序被处理器执行时，实现如上任一项所述的连续血糖检测方法。
综上所述，本申请提供的连续血糖检测方法、系统及可读存储介质，包括：获取组织间液葡萄糖信号；以及，根据所述组织间液葡萄糖信号的变化速率的不同采取对应幅度及对应相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理，以得到血糖信号。即，根据组织间液葡萄糖信号变化速率的不同，搭配不同幅度及相位的低通滤波模型对组织间液葡萄糖信号进行分段式计算，以得到更为精确的血糖信号，从而解决了滤波器带来的额外延时及血液葡萄糖与组织间液葡萄糖在不同变化速率下所产生的动态时延与幅度偏差的问题。
附图说明
图1为本申请实施例提供的连续血糖检测方法的流程图；
图2为本申请实施例中第一幅度及第一相位的取值范围示例图；
图3为本申请实施例中第二幅度及第二相位的取值范围示例图；
图4为本申请实施例中第三幅度及第三相位的取值范围示例图；
图5为本申请实施例中血糖浓度变化较为平稳时，BG与IG之间的关系示例图；
图6为本申请实施例中血糖浓度快速上升时，BG与IG之间的关系示例图；
图7为本申请实施例中血糖浓度快速下降时，BG与IG之间的关系示例图；
图8为利用本施例提供的连续血血糖检测方法IG 0(t)、IG 1(t)及BG(t)的波形对比图；
图9为本申请实施例中利用MA滤波器对血糖信号进行低阶滤波处理前后的对比图；
图10为本申请实施例中利用S-G滤波器对血糖信号进行低阶滤波处理前后的对比图；
图11为本申请实施例提供的连续血糖检测系统的组成框图。
具体实施方式
为使本申请的目的、优点和特征更加清楚，以下结合附图和具体实施例对本申请作详细说明。需说明的是，附图均采用非常简化的形式且未按比例绘制，仅用以方便、明晰地辅助说明本申请实施例的目的。此外，附图所展示的结构往往是实际结构的一部分。特别的，各附图需要展示的侧重点不同，有时会采用不同的比例。还应当理解的是，除非特别说明或者指出，否则说明书中的术语“第一”、“第二”、“第三”等描述仅仅用于区分说明书中的各个组件、元素、步骤等，而不是用于表示各个组件、元素、步骤之间的逻辑关系或者顺序关系等。
发明人研究发现，BG-IG之间的延时由于在信号稳态变化状态，快速增长状态及快速下降状态均不相同的情况，故在利用低通滤波模型对IG-BG之间的延时进行补偿时，使用单一处理模式并不可达到消除延时的最佳效果。
基于上述发现，请参考图1，本申请实施例提供一种连续血糖(CGM)检测方法，所述CGM检测方法包括如下步骤：
S11，获取组织间液葡萄糖信号；
S12，根据所述组织间液葡萄糖信号的变化速率的不同采取对应幅度及对 应相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理，以得到血液葡萄糖信号(血糖信号)。
本申请实施例提供的所述连续血糖检测方法，根据组织间液葡萄糖信号变化速率的不同，搭配不同幅度及相位的低通滤波模型对组织间液葡萄糖信号进行分段式计算，以得到更为精确的血糖信号，从而解决了滤波器带来的额外延时及血液葡萄糖与组织间液葡萄糖在不同变化速率下所产生的动态时延与幅度偏差的问题。
步骤S11中，获取组织间液葡萄糖信号可包括：获取传感器与组织间液葡萄糖反应所采集的电信号，以及，将所述电信号转换为组织间液葡萄糖信号。一般而言，利用传感器与组织间液葡萄糖反应所采集的电信号，与组织间液葡萄糖信号之间存在线性关系，故在得到所述电信号之后，可利用两者之间的线性关系，将所述电信号转换为组织间液葡萄糖信号。
在利用低通滤波模型计算组织间液葡萄糖信号时，对高频率信号进行了幅度的衰减及对低频率信号进行了较大的相位偏移。从信号处理的角度来看，该处理方法虽可对高频信号及低频信号的相位偏移进行补偿，但也会对本应相对稳定部分的低频信号进行幅度与相位上的干预，在血糖变化迅速时，则又对低频信号的幅度偏差补偿不足。
故，本实施例步骤S12中，具体的，根据所述组织间液葡萄糖信号的变化速率的不同采取对应幅度及对应相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理可包括：
若所述组织间液葡萄糖信号在设定时间内的上升值大于第一设定值，则利用第一幅度及第一相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理；
若所述组织间液葡萄糖信号在设定时间内的下降值大于第二设定值，则利用第二幅度及第二相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理；
若所述组织间液葡萄糖信号在设定时间内的上升值不大于所述第一设定值，且下降值不大于所述第二设定值，则利用第三幅度及第三相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理；
其中，所述第一幅度、所述第二幅度、所述第三幅度依次降低，所述第一相位、所述第二相位、所述第三相位依次降低。即，所述第一幅度，所述第二幅度、所述第三幅度可依次理解为高幅度、中幅度、低幅度，所述第一 相位、所述第二相位、所述第三相位可依次理解为高相位、中相位、低相位。
参考连续血糖检测产品中较为常用的判定快速变化的血糖变化阈值，在一些实施例中，所述设定时间可为15min，所述第一设定值和所述第二设定值的取值可为14.9mg/dL～15.1mg/dL，例如具体可为15mg/dL。即，若15min内检测血糖值上升超过15mg/dL，则判定血糖变化状态为快速上升，采用第一幅度及第一相位的低通滤波模型对组织间液葡萄糖信息进行低通滤波逆卷积处理；若15min内内检测血糖值下降超过15mg/dL，则判定血糖变化状态为快速下降，采用第二幅度及第二相位的低通滤波模型对组织间液葡萄糖信息进行低通滤波逆卷积处理；若15min内检测血糖值变化处于+15mg/dL与-15mg/dL之间，则判定血糖变化状态为平稳变化，则采用第三幅度及第三相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理。
即可简单理解为，当组织间液葡萄糖信号快速上升时，则利用低幅度及低相位的低通滤波模型计算血糖信号，当组织间液葡萄糖信号快速下降时，则利用高幅度及高相位的低通滤波模型计算血糖信号，当组织间液葡萄糖信号稳定变化时，则利用中幅度及中相位的低通滤波模型计算血糖信号。
作为示例，如图2所示，采用所述第一幅度、所述第一相位的低通滤波器对低频部分信号进行高幅度、高相位偏移的低通滤波逆卷积处理，其中，所述第一幅度及所述第一相位满足在归一化频率0.1πrad/sample所对应的低频部分，该滤波器的幅度衰减达到-31dB,相位衰减达到-62度，可较大限度的对信号的低频部分进行幅度补偿，以达到对快速上升状态时对血液葡萄糖的推算。如图3所示，所述第二幅度、所述第二相位的低通滤波器对低频部分信号进行中幅度、中相位偏移的低通滤波逆卷积处理，其中，所述第二幅度、所述第二相位满足在归一化频率0.1πrad/sample所对应的低频部分该滤波器的幅度衰减以达到-4dB,相位衰减达到-25度，可中等限度的对信号的低频部分进行幅度补偿，以达到对快速下降状态时对血液葡萄糖的推算。如图4所示，所述第三幅度、所述第三相位的低通滤波器对其低频信号进行低幅度、低相位偏移的低通滤波逆卷，其中，所述第三幅度、所述第三相位满足在归一化频率0.1πrad/sample所对应的低频部分该滤波器的幅度衰减以达到-1dB,相位衰减-0.25度，以达到平稳状态时对血液葡萄糖的推算。本实施例中，图2～图3所示，所述第一幅度、所述第二幅度、第三幅度、所述第一相位、所述第二相位、所述第三相位对应于归一频率0.1πrad/sample时的具体取值，仅用以说明相互大小关系，不构成对于本申请的限制，另外，应当理解的是， 在上述描述中，利用归一化频率0.1πrad/sample时滤波器的相位、幅度的取值来对本实施例中高、中、低的相位及幅度度来进行区分，是因为归一化频率0.1πrad/sample能用以较好的说明信号的低频部分，其并不能构成对于本申请的限制。
当组织间液葡萄糖信号变化较为平稳时，血糖信号(BG)与组织间液葡萄糖信号(IG)之间的关系如图5所示，从图5可以看出，BG与IG相位上偏差较小，且只存在较小的幅度偏差，故通过对低频信号进行低幅度、低相位偏移的低通滤波逆卷积即可较好的推算出血糖信号。
当组织间液葡萄糖信号变化为快速上升时，BG与IG之间的关系如图6所示，从图6可以看出，IG在幅度与相位上均落后于BG，此时可采用对低频信号进行高幅度、高相位偏移的低通滤波逆卷积处理，以对延时和幅度偏差进行补偿，即可较好的进行推算出血糖信号。
当组织间液葡萄糖信号变化为快速下降时，BG与IG之间的关系如图7所示，从图7可以看出，BG与IG在幅度及相位的偏差与以上两种情况相比处于中等，故此时可采用对低频信号进行高幅度、高相位偏移的低通滤波逆卷积处理，以对延时和幅度偏差进行补偿，即可较好的进行推算出血糖信号。
本实施例中，采用的所述低通滤波模型可如下：
Figure PCTCN2022128295-appb-000002
其中，IG表示组织间液葡萄糖信号，BG表示血糖信号，τ表示扩散系数，t表示信号采集时间。
该模型利用组织间液和血液之间的扩散关系的微分状态式求解，该算式将组织间液信号理解为对血糖信号进行低通滤波后得到，其中，τ以数据统计的先验概率最高的系数作为初始，并通过获取的个体信号进行调节。
该模型的求解以信号稳态为前提，当组织间液葡萄糖信号快速变化时，利用该模型所检测得到的血糖值并非最优解。
在该模型的基础上，本实施例，在得到IG(t)后，根据IG(t)的变化速率的不同采取对应幅度及对应相位对将IG(t)进行低通滤波逆卷积处理，而后得到血糖信号，从而可使检测得到的血糖值始终保持最优解。
在另外一些实施例中，也可采用其它低通滤波模型，在此不再赘述。
如图8所示，其示意出采用本实施例提供的所述血糖连续检测方法，在步骤S11得到IG 0(t)，而后利用步骤S12对IG(t)进行低通滤波逆卷积处 理所得到的IG 1(t)，以及最终检测得到的BG(t)的信号波形图，其图中看出，IG 1(t)与BG(t)之间的辐度偏差及相位偏差小于IG 0(t)与BG(t)之间的辐度偏差及相位偏差，即，利用本实施例提供的所述血糖连续检测方法，BG-IG之间的幅度偏差与相位偏差均得以减少。
较佳的，本实施例提供的所述连续血糖检测方法还包括：步骤S13，对所述血糖信号进行低阶滤波处理。具体的，可采用平滑平均滤波器(MA滤波器)、S-G滤波器(Savitzky-Golay Filter)等平滑滤波器对所述血糖信号进行低阶滤波处理，以得到线性血糖信号。
进一步较佳的，本实施例利用S-G滤波器对所述血糖信号进行低阶滤波处理，从图9可以看出，在利用MA滤波器对所述血糖信号进行低阶滤波处理时，时延较大，而从图10可以看出，在利用滤波器对所述血糖信号进行低阶滤波处理时，由于采用了低阶多项式对曲线进行了递归拟合，即保证了噪声的消除，也减小了时延。
请参见图11，本申请实施例还提供一种连续血糖检测系统，包括：
信号获取模块100，用于获取组织间液葡萄糖信号；以及，
计算模块200，用于根据所述组织间液葡萄糖信号的变化速率的不同采取对应幅度及对应相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理，以得到血糖信号。
较佳的，本申请实施例提供的所述连续血糖检测系统还包括：
信号处理模块300，用于对所述血糖信号进行低阶滤波处理。
即，所述信号获取模块100用于实现图1所示的步骤S11，所述计算模块200用于实现图1所示的步骤S12，所述信号处理模块300用于实现图1所示的步骤S13。从而关于所述信号获取模块100、所述计算模块200和所述信号处理模块300能够实现的功能的具体说明可以参考上述连续血糖检测方法的部分中图1所示的关于步骤S11-S13的相关描述，重复之处不再赘述。此外，所述连续血糖检测系统可以实现与前述连续血糖检测方法相似的技术效果，在此不再赘述。
可以理解的是，所述连续血糖检测系统中，所述信号获取模块100、所述计算模块200、所述信号处理模块300可以合并在一个装置中实现，或者其中的任意一个模块可以被拆分成多个子模块，或者，所述续血糖检测系统中，所述信号获取模块100、所述计算模块200、所述信号处理模块300的一个或多个模块的至少部分功能可以与其他模块的至少部分功能相结合，并在一个 功能模块中实现。根据本申请的实施例，所述连续血糖检测系统中，所述信号获取模块100、所述计算模块200、所述信号处理模块300中的至少一个可以至少被部分地实现为计算机程序模块，当该程序被计算机运行时，可以执行如图1所示的相应模块的功能。
本实施例还提供一种可读存储介质，所述可读存储介质内存储有计算机程序，所述计算机程序被处理器执行时实现本实施例提供的连续血糖检测方法。
所述可读存储介质可以是可以保持和存储由指令执行设备使用的指令的有形设备，例如可以是但不限于电存储设备、磁存储设备、光存储设备、电磁存储设备、半导体存储设备或者上述的任意合适的组合。可读存储介质的更具体的例子(非穷举的列表)包括：便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、静态随机存取存储器(SRAM)、便携式压缩盘只读存储器(CD-ROM)、数字多功能盘(DVD)、记忆棒、软盘、机械编码设备以及上述的任意合适的组合。这里所描述的计算机程序可以从可读存储介质下载到各个计算/处理设备，或者通过网格、例如因特网、局域网、广域网和/或无线网下载到外部计算机或外部存储设备。所述计算机程序可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。
所述处理器可以是中央处理器(CPU)、网络处理器(NP)、张量处理器(TPU)或者图形处理器(GPU)等具有数据处理能力和/或程序执行能力的器件；还可以是数字信号处理器(DSP)、专用集成电路(ASIC)、现场可编程门阵列(FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。中央处理元(CPU)可以为X86或ARM架构等。
综上所述，本申请提供的连续血糖检测方法、系统及可读存储介质，包括：获取组织间液葡萄糖信号；以及，根据所述组织间液葡萄糖信号的变化速率的不同采取对应幅度及对应相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理，以得到血糖信号。即，根据组织间液葡萄糖信号变化速率的不同，搭配不同幅度及相位的低通滤波模型对组织间液葡萄糖信号进行分段式计算，以得到更为精确的血糖信号，从而解决了滤波器带来的额外延时及血液葡萄糖与组织间液葡萄糖在不同变化速率下所产生的动态时延与幅度偏差的问题。
此外还应该认识到，虽然本申请已以较佳实施例披露如上，然而上述实施例并非用以限定本申请。对于任何熟悉本领域的技术人员而言，在不脱离本申请技术方案范围情况下，都可利用上述揭示的技术内容对本申请技术方案做出许多可能的变动和修饰，或修改为等同变化的等效实施例。因此，凡是未脱离本申请技术方案的内容，依据本申请的技术实质对以上实施例所做的任何简单修改、等同变化及修饰，均仍属于本申请技术方案保护的范围。

Claims (12)

  1. 一种连续血糖检测方法，其特征在于，包括：
    获取组织间液葡萄糖信号；以及，
    根据所述组织间液葡萄糖信号的变化速率的不同，对所述组织间液葡萄糖信号进行对应幅度及对应相位的低通滤波逆卷积处理，以得到血糖信号。
  2. 如权利要求1所述的连续血糖检测方法，其特征在于，根据所述组织间液葡萄糖信号的变化速率的不同，对所述组织间液葡萄糖信号进行对应幅度及对应相位的低通滤波逆卷积处理包括：
    若所述组织间液葡萄糖信号在设定时间内的上升值大于第一设定值，则利用第一幅度及第一相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理；
    若所述组织间液葡萄糖信号在设定时间内的下降值大于第二设定值，则利用第二幅度及第二相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理；
    若所述组织间液葡萄糖信号在设定时间内的上升值不大于所述第一设定值，且下降值不大于所述第二设定值，则利用第三幅度及第三相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理；
    其中，所述第一幅度、所述第二幅度、所述第三幅度依次降低，所述第一相位、所述第二相位、所述第三相位依次降低。
  3. 如权利要求2所述的连续血糖检测方法，其特征在于，所述设定时间为15min，所述第一设定值和所述第二设定值为14.9mg/dL～15.1mg/dL。
  4. 如权利要求1所述的连续血糖检测方法，其特征在于，采用如下低通滤波模型对所述组织间液葡萄糖信号进行对应幅度及对应相位的低通滤波逆卷积处理：
    Figure PCTCN2022128295-appb-100001
    其中，IG表示组织间液葡萄糖信号，BG表示血糖信号，τ表示扩散系数，t表示信号采集时间。
  5. 如权利要求1所述的连续血糖检测方法，其特征在于，在得到所述血糖信号之后，所述连续血糖检测方法还包括：
    对所述血糖信号进行低阶滤波处理，以得到线性血糖信号。
  6. 如权利要求1所述的连续血糖检测方法，其特征在于，获取组织间液葡萄糖信号的方法包括：
    获取传感器与组织间液葡萄糖反应所采集的电信号；以及，
    将所述电信号转换为所述组织间液葡萄糖信号。
  7. 一种连续血糖检测系统，其特征在于，包括：
    信号获取模块，用于获取组织间液葡萄糖信号；以及，
    计算模块，用于根据所述组织间液葡萄糖信号的变化速率的不同采取对应幅度及对应相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理，以得到血糖信号。
  8. 如权利要求7所述的连续血糖检测系统，其特征在于，所述计算模块根据所述组织间液葡萄糖信号的变化速率的不同采取对应幅度及对应相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理包括：
    若所述组织间液葡萄糖信号在设定时间内的上升值大于第一设定值，则利用第一幅度及第一相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理；
    若所述组织间液葡萄糖信号在设定时间内的下降值大于第二设定值，则利用第二幅度及第二相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理；
    若所述组织间液葡萄糖信号在设定时间内的上升值不大于所述第一设定值，或下降值不大于所述第二设定值，则利用第三幅度及第三相位的低通滤波模型对所述组织间液葡萄糖信号进行低通滤波逆卷积处理；
    其中，所述第一幅度、所述第二幅度、所述第三幅度依次降低，所述第一相位、所述第二相位、所述第三相位依次降低。
  9. 如权利要求8所述的连续血糖检测系统，其特征在于，所述设定时间为15min，所述第一设定值和所述第二设定值为14.9mg/dL～15.1mg/dL。
  10. 如权利要求7所述的连续血糖检测系统，其特征在于，所述连续血糖检测系统还包括：
    信号处理模块，用于对所述血糖信号进行低阶滤波处理，以得到线性血糖信号。
  11. 如权利要求7所述的连续血糖检测系统，其特征在于，所述信号获取模块获取组织间液葡萄糖信号包括：
    获取传感器与组织间液葡萄糖反应所采集的电信号，以及，
    将所述电信号转换为所述组织间液葡萄糖信号。
  12. 一种可读存储介质，其特征在于，所述可读存储介质上存储有计算机程序，所述计算机程序被处理器执行时，实现如权利要求1～6任一项所述的连续血糖检测方法。
PCT/CN2022/128295 2021-11-24 2022-10-28 连续血糖检测方法、系统及可读存储介质 WO2023093457A1 (zh)

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