CN102440785A - 传感器信号处理方法及传感器信号处理装置 - Google Patents
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- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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- 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
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- A—HUMAN NECESSITIES
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- 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
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
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- A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7271—Specific aspects of physiological measurement analysis
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Abstract
本发明提供了一种传感器信号处理方法及传感器信号处理装置。所述传感器信号处理方法包括:从葡萄糖传感器接收葡萄糖传感器数据;确定校准因子,所述校准因子使葡萄糖传感器数据与葡萄糖水平相关;确定所述葡萄糖传感器数据的变化速率;基于第一数学模型确定在间质液中的葡萄糖摄取或消耗参数;基于第二数学模型确定在血液与间质液之间的葡萄糖扩散参数;以及基于所述葡萄糖传感器数据、所述葡萄糖传感器数据的变化速率、所述校准因子、所述葡萄糖摄取或消耗参数、和所述葡萄糖扩散参数来确定葡萄糖水平。
Description
本申请为申请日为2006年8月29日,申请号为200680040174.4,发明名称为“改善连续式葡萄糖传感器的准确度”的发明专利申请的分案申请。
相关公开交叉参考
根据35U.S.C.119(e),这个美国专利申请要求来自共同未决的于2005年8月31日提交的美国临时申请系列号60/713,203和于2006年6月20日提交的系列号60/815,191的优先权,将上述每一主题的全部内容以引用方式结合于本文。在同此附上的附录A中的每个出版物和美国临时申请的主题的全部内容以引用方式结合于本文。
技术领域
本发明涉及葡萄糖监测(监控)的领域,并且更具体地涉及用于连续葡萄糖监测(监控)的方法和系统。
背景技术
现有的证据,如根据美国糖尿病协会(American DiabetesAssociation)的“全国糖尿病调查(National Diabetes Fact Sheet)”,表明目前在美国大约1820万人患有糖尿病;并且在美国,糖尿病是死亡的第六主要原因。在2000年出生的每三个美国人中有一人将发展成II型糖尿病。由于大量的糖尿病患者,以及由于预期会增加的糖尿病发病率,因此对于用于监测葡萄糖水平的准确葡萄糖监测系统存在持续增长的需要。连续式葡萄糖传感器(continuousglucose sensor)被设计成不仅在单个时间点提供实时葡萄糖水平,而且基于每一确定的时段(具有最少的指刺(finger-stick))进行的分析来提供人葡萄糖水平的趋势,这导致改善的血糖(glycemic)/糖尿病控制。
然而,由于难以在动脉或血管中直接测量血糖(下文称作BG),最现代的连续式葡萄糖传感器(下文称作CGS)是通过对在间质液中的间质葡萄糖(下文称作IG)进行取样来产生或获得血糖估计(值)、而不是对BG进行取样来产生血糖估计(值)。来自IG的典型葡萄糖(BG)估计产生自至少两个连续近似步骤:1)血液至间质葡萄糖(BG-至-IG)输送;以及2)从由传感器记录的IG相关的电流导出BG值。因此,虽然CGS技术已取得显著的进展,但就校准、灵敏度、稳定性、以及血液与间质葡萄糖浓度之间的生理时滞而言,准确和可靠的CGS装置的发展继续面临许多挑战。BG和CGS读数之间的差异起因于以下主要因素:生理过程、传感器校准、噪声(干扰)、以及操作(设计,engineering)。生理时滞和梯度动态地随时间、随BG水平、以及交叉受治疗者(across subject)而变化;并且IG的直接频繁的体内取样是极端困难的。因此,CGS的操作性能的评估留下一个中心问题:分离起因于校准、传感器噪声、以及BG/IG梯度的BG/CGS误差部分。
因此,期望一种方法和设备来改善CGS的准确度(精确性)和可靠性。
发明内容
基于本披露内容,将明了本发明的优选具体实施方式的各种目的和优点。根据优选的具体实施方式,通过改善CGS传感器的校准或纠正由生物时滞引起的误差或其组合,本发明可改善CGS的准确度和可靠性。
作为本发明的一种示例性具体实施方式,本文披露了一种用于改善连续式葡萄糖传感器(CGS)的准确度的方法。该方法包括:在第一时间校准CGS;以及在第二时间变化CGS校准,该第二时间是基于动态监测的CGS值、CGS变化的速率和预定准则加以确定的。
作为本发明的另一种示例性具体实施方式,本文披露了一种用于改善连续式葡萄糖传感器(CGS)的准确度的方法。该方法包括:利用第一血糖数据和不同于第一血糖数据的第二血糖数据对CGS进行校准。
作为本发明的又一示例性具体实施方式,本文披露了一种连续式葡萄糖传感(CGS)装置(或连续式葡萄糖检测装置)。该装置包括:第一装置,用于测量间质葡萄糖水平以便获得CGS输出;以及可访问CGS输出的校准模块,用于改善CGS的准确度,进一步包括:可访问CGS输出的监测模块,用于动态地监测CGS和CGS的时间导数;以及基于动态CGS值、CGS值的时间导数和预定准则来指导(指示)另一个校准事件。
作为本发明的又一种示例性具体实施方式,披露了一种计算机可读介质,该介质具有计算机可执行指令以实施一种用于改善连续式葡萄糖传感器的准确度的方法,其中所述方法包括:取得(retrieving)最初血糖值和在最初血糖值的测量中获得的CGS值;随时间监测CGS值和CGS值的时间导数;基于监测的CGS值和CGS值的时间导数确定是否开始另一个校准;以及如果确定开始所述的另一个校准,则校准CGS。
作为本发明的又一种示例性具体实施方式,披露了一种计算机可读介质,该介质具有计算机可执行指令以实施一种用于改善连续式葡萄糖传感器的准确度的方法,其中所述方法包括:在第一时间取得血糖值和在最初血糖值的测量中获得的CGS值;以及在第二时间校准该CGS,该第二时间是通过基本上在该第二时间的一个CGS值、该CGS值的时间导数和预定准则来确定的。
作为本发明的又一种示例性具体实施方式,本文披露了一种用于治疗与血糖有关的疾病的系统。该系统包括:连续式葡萄糖装置;用于将CGS值递送到疾病治疗中心的装置,其中疾病治疗中心能够发出相应的治疗指令或采取相应的治疗行为(treating action)。
本发明的某些优选具体实施方式的各种目的和/或优点可以(在某些优选的实施例中)借助于本文所附的独立权利要求的特征来实现。在从属权利要求中进一步阐述了另外的优选具体实施方式。根据35U.S.C.··112,第六款,在权利要求中,仅由词组“用于...的装置”表示的元件用来解释为装置加功能性的权利要求。
本发明提供了一种传感器信号处理方法,包括:从葡萄糖传感器接收葡萄糖传感器数据;确定校准因子,所述校准因子使葡萄糖传感器数据与葡萄糖水平相关;确定所述葡萄糖传感器数据的变化速率;基于第一数学模型确定在间质液中的葡萄糖摄取或消耗参数;基于第二数学模型确定在血液与间质液之间的葡萄糖扩散参数;以及基于所述葡萄糖传感器数据、所述葡萄糖传感器数据的变化速率、所述校准因子、所述葡萄糖摄取或消耗参数、和所述葡萄糖扩散参数来确定葡萄糖水平。
根据本发明的方法,其中,所述葡萄糖传感器数据通过使所述葡萄糖传感器数据滤波而预处理。
根据本发明的方法,其中,所述滤波包括利用卡尔曼滤波器来滤波。
根据本发明的方法,其中,所述滤波包括采用贝叶斯估计技术。
根据本发明的方法,其中,所述葡萄糖传感器数据的变化速率通过使用利用指数权的短区间多项式光滑技术来确定。
根据本发明的方法,其中,所述第一数学模型基于米-门氏动力学。
根据本发明的方法,其中,所述第一数学模型使用用于葡萄糖转运子的米-门氏常数。
根据本发明的方法,其中,所述葡萄糖转运子是GLUT-4。
根据本发明的方法,其中,所述葡萄糖摄取或消耗参数取决于胰岛素水平和锻炼中的至少一种。
根据本发明的方法,其中,所述第二数学模型基于浓度梯度类扩散模型。
根据本发明的方法,其中,所述葡萄糖扩散参数基于毛细管壁对葡萄糖的渗透性。
根据本发明的方法,其中,所述葡萄糖传感器数据是实时数据。
根据本发明的方法,其中,所述葡萄糖传感器数据是追溯数据。
根据本发明的方法,其中,所述校准因子基于在第一时间的第一葡萄糖传感器数据测量和相应的第一参照血液葡萄糖测量来确定。
根据本发明的方法,其中,所述校准因子进一步基于在第二时间的第二葡萄糖传感器数据测量和相应的第二参照血液葡萄糖测量来确定,其中,所述第二葡萄糖传感器数据测量仅仅在所述第一葡萄糖传感器数据测量与所述第二葡萄糖传感器数据测量之间的差异大于预定差异阈值时使用。
本发明还提供了一种传感器信号处理装置,包括:处理单元;以及可操作地耦接于至少一个处理单元的存储器,所述存储器用于存储指令,当所述处理单元执行所述指令时,使得所述处理单元从葡萄糖传感器接收葡萄糖传感器数据;确定校准因子,所述校准因子使葡萄糖传感器数据与葡萄糖水平相关;确定所述葡萄糖传感器数据的变化速率;基于第一数学模型确定在间质液中的葡萄糖摄取或消耗参数;基于第二数学模型确定在血液与间质液之间的葡萄糖扩散参数;以及基于所述葡萄糖传感器数据、所述葡萄糖传感器数据的变化速率、所述校准因子、所述葡萄糖摄取或消耗参数、和所述葡萄糖扩散参数来确定葡萄糖水平。
根据本发明的装置,其中,所述存储器用于存储指令,当所述处理单元执行所述指令时,使得所述处理单元对所述葡萄糖传感器数据滤波。
根据本发明的装置,其中,所述滤波包括利用卡尔曼滤波器来滤波。
根据本发明的装置,其中,所述滤波包括采用贝叶斯估计技术。
根据本发明的装置,其中,所述葡萄糖传感器数据的变化速率通过使用利用指数权的短区间多项式光滑技术来确定。
根据本发明的装置,其中,所述第一数学模型基于米-门氏动力学。
根据本发明的装置,其中,所述第一数学模型使用用于葡萄糖转运子的米-门氏常数。
根据本发明的装置,其中,所述葡萄糖转运子是GLUT-4。
根据本发明的装置,其中,所述葡萄糖摄取或消耗参数取决于胰岛素水平和锻炼中的至少一种。
根据本发明的装置,其中,所述第二数学模型基于浓度梯度类扩散模型。
根据本发明的装置,其中,所述葡萄糖扩散参数基于毛细管壁对葡萄糖的渗透性。
根据本发明的装置,其中,所述葡萄糖传感器数据是实时数据。
根据本发明的装置,其中,所述葡萄糖传感器数据是追溯数据。
根据本发明的装置,其中,所述校准因子基于在第一时间的第一葡萄糖传感器数据测量和相应的第一参照血液葡萄糖测量来确定。
根据本发明的装置,其中,所述校准因子进一步基于在第二时间的第二葡萄糖传感器数据测量和相应的第二参照血液葡萄糖测量来确定,其中,所述第二葡萄糖传感器数据测量仅仅在所述第一葡萄糖传感器数据测量与所述第二葡萄糖传感器数据测量之间的差异大于预定差异阈值时使用。
附图说明
根据以下连同附图的详细描述,可以最好地理解本发明的优选具体实施方式,其中:
图1示意地图解说明了一种利用根据本发明一个实施例的连续式葡萄糖传感器来测量血糖水平的方法;
图2是示出了包括图1的校准仪的连续式葡萄糖传感器的分解图;
图3是图解说明了根据本发明一个实施例的校准仪和时滞校正器的功能模块的示意图;
图4a是根据本发明一个实施例的校准模块的方块图;
图4b是根据本发明一个实施例的校准过程的流程图;
图5图解说明了其中在校准期间的血糖差异以及通过本发明的示例性方法改善的CGS的准确度;
图6是示出了利用本发明的时滞校正方法为改善CGS的准确度所执行的步骤的流程图;
图7是示出了根据本发明的又一个实施例为校准CGS所执行的步骤的流程图;
图8是示出了利用本发明的一种示例性方法的改善的CGS测量结果的曲线图;
图9是示出了利用本发明的一种示例性方法的改善的CGS测量结果的另一曲线图;
图10是示出了利用本发明的一种示例性方法的改善的CGS测量结果的又一曲线图;
图11是示出了根据本发明的又一个实施例为改善CGS的准确度所执行的步骤的流程图;
图12是图解说明了一种其中可以实施本发明的实施例的系统的示意图;以及
图13是示出了具有计算机可读指令的示例性计算装置的示意图,在该计算装置中可以实施本发明的实施例。
具体实施方式
通过改善CGS的校准或通过校正起因于BG与IG之间的生理时滞的误差或它们的组合,本发明提供了一种用于改善连续式葡萄糖传感器的准确度的方法和装置。考虑到在本发明的精神范围内的许多可能的变型,在下文将参照特定的实施例来讨论本发明。然而,本领域技术人员应当明了,以下讨论是用于说明目的,并且不应当解释为限制本发明。在不偏离本发明的精神的情况下,其它变型也是可用的。
大多数目前的CGS装置的不准确性(不准确度)主要归因于不良的CGS校准、生理时滞、以及随机误差。为了降低CGS的不准确性,提出了一种改善的校准程序。也在本发明中提出了,可以可替换地通过校正与生理时滞有关的误差来实现降低不准确性。事实上,改善的校准程序和时滞校正程序可以可替换地结合在一起以便获得更好的CGS性能。
参照图1,其中示意性地图解说明了根据本发明的一个实施例测量体内葡萄糖水平的方法。由于在血管或动脉(血液)102中直接测量葡萄糖水平非常困难,所以CGS 100通过测量与血管或动脉相互作用的间质液104中的葡萄糖水平来检测血糖水平;并且将CGS的输出值与血糖水平联系起来(该过程经常称作校准)。这种间接测量基于已证明的基础,即血糖水平与间质液中的葡萄糖水平共同变化。
为了改善CGS的准确度,如图2中所示,提供了准确度改善仪(校准仪,accuracy improver)106。通过经由最佳(优化)校准建议来改善CGS校准(其将在下文讨论)、或通过校正与生理时滞有关的误差(其将在下文讨论)、或通过其组合,准确度改善仪可以改善CGS的准确度。虽然图2示出了准确度改善仪包括在CGS(100)中作为功能构件,但并不需要如此。在其它实施例中,准确度改善仪可以是与CGS分开的独立模块或方法。具体地说,为了改善CGS的准确度,可以在与CGS输出相连接的装置中实施(实现或设置,implement)准确度改善仪。此外,可以以存储在计算机可读介质中的计算机可执行代码的序列的形式来实施准确度模块;或可以在装置的硬件中加以实施,其将随后详述。
作为本发明的一个实施例,图3示意性地图解说明了图2中的准确度改善仪(106)的分解图。在该特定的实施例中,准确度改善仪(106)包括CGS校准模块108和时滞校正模块110。通过执行最佳校准建议循环(optimal calibration recommendation cycle),CGS校准模块可指定用于改善CGS的校准,从而改善CGS的总准确度。时滞校正模块(110)被指定用来校正由血糖水平与间质葡萄糖水平之间的生理时滞引起的误差。取决于如上所讨论的准确度改善仪的具体功能,准确度改善仪可以仅具有CGS校准模块和时滞校正模块其中之一或两者均具有。
改善的CGS校准
在本领域已知的,CGS校准的准确度取决于血糖(下文称作BG)变化的速率和在校准时刻(t)的BG值(BG(t))。BG变化的速率可以数学上表示为BG(t)的时间导数:d(BG(t))/dt。鉴于下述事实:带有变量输入(variant input)的校准好于那些借助于单一或非变化的输入的校准,所以本发明的CGS校准使用变量输入。
作为一个实施例,图4a是示出了图3的示例性CGS校准的功能模块(118)的示意图。参照图4a,CGS校准仪118包括初始校准模块111、存储模块113、再赋值模块(reassigning module)115、以及决策环模块117,其中决策环模块117进一步包括监测模块119、内部请求模块121、自检启动模块(自检起始模块)123、决定模块125、以及传感器校准模块129。
初始校准模块进行最初校准以便从BG(0)的最初测量结果获得初始校准数据对SG(0)和BG(0)。存储模块113耦合于初始校准模块111的输出并存储初始校准数据对SG(0)和BG(0)。监测模块119连接于存储模块的输出并动态地监测CGS输出SG(t)和SG(t)变化的速率SG’(t)=dSG(t)/dt。内部请求模块121连接于监测模块的输出并管理用于校准的内部请求。自检启动模块123连接于内部请求模块的输出并被指定用于启动自检程序以用于最佳校准定时。与自检模块123的输出连接,决定模块125决定是否在特定时间通过传感器校准模块129进行另一次校准。在决定以后,来自校准的校准数据通过再赋值模块115被再赋值(再分配)到CGS。
应该注意,实际上一个或多个上述功能模块可以合并到其它功能模块中。尤其是,传感器校准模块129可以合并到初始校准模块111中,以进行CGS校准。可以将监测模块119、内部请求模块121、自检模块123、以及决定模块125中的任何一个合并到组合功能模块中。
在图4b中示意性地图解说明了图4a中的功能模块的示例性操作,以便完成本发明的所期望的最佳校准建议过程。参照图4b,通过在最初时间t测量血糖水平BG(0)来进行初始校准,以便获得最初CGS输出SG(0)(步骤112)。可以通过图4a中的初始校准模块111来执行该步骤。在步骤114记录最初数据对BG(0)和SG(0)。可以通过图4a中的存储模块113来执行此步骤。可以在由CGS制造商推荐的时间或在由用户确定的时间执行上述初始校准。然后校准程序进入校准决定形成环(calibration decision making loop)118,如图4a所示该校准决定形成环是通过决策环模块117来执行的。
校准决定形成环开始于步骤120,该步骤120为随时间监测传感器值SG(t)和变化速率SG’(t),其中SG’(t)定义为SG(t)的一阶时间导数,即SG’(t)=dSG(t)/dt。可以通过图4a中的监测模块119进行SG’(t)的动态监测和推导。在特定时间t1接受另一个校准请求(步骤122)以后,则在步骤124启动用于最佳校准定时的自检程序。此步骤触发(启动)一系列确定在此时是否应进行校准的操作,例如图4b中的126和128。接受校准请求并传送这种请求以启动自检程序可以通过图4a中的内部请求模块121来完成;并且可以通过图4a中的自检启动模块123来完成自检启动。应当注意到,可以在由制造商规定的时间、或可替换地在由用户(如医生以至适当的患者)规定的时间,启动对于另一次校准的内部请求。
在步骤124启动自检程序以后,则在步骤126确定是否|SG’(t1)|<1。可以通过在图4a中的决定模块125来执行此决定步骤。如果|SG’(t1)|≥1,则程序回到步骤120以继续监测SG(t)和SG’(t)值。否则,程序进行另一决定是否|SG(t1)-SG(t0)|大于d(mg/dl),其中d(mg/dl)是初始SG(0)与在时间t1时的CGS输出SG(t1)之间的预定差异阈值。例如,d(mg/dl)可以是10mg/dl或更高,如15mg/dl或更高,并且更优选30mg/dl或更高。在步骤126和128的一种或两种决定可以通过如图4a中所示的决定模块125来执行。在步骤128,如果|SG(t1)-SG(t0)|等于或小于d(mg/dl),则程序回到步骤120。否则,在步骤130,通过例如图4a中的传感器校准模块129进行另一校准。基于在步骤130的再校准,在步骤116再赋予CGS校准值,例如,通过分别用再校准值SG(t1)和BG(t1)代替校准值SG(t0)和BG(t0)。在步骤114存储再赋予的校准值。可以通过图4a中的再赋值模块115来完成上述再赋值。
在再赋值和记录以后,校准过程再次进入决定形成环118,并且重复上述步骤114、120、122、124、126、128、130、以及116。可以通过由CGS制造商建议的默认校准数目、或可替换地由用户来确定校准循环的数目。例如,在CGS运行(寿命,life)的最初24小时期间,可以进行多次校准循环,例如2至10次,或更通常3至4次校准循环。
利用如上所讨论的最佳校准方法的改善的CGS的准确度可以通过以下实验数据和计算机模拟来证实,如图5所示。
实验数据
为了测试体现如上所讨论的精确度改善方法的该CGS的准确度,对39位患有I型糖尿病(T1DM)的受治疗者进行了测量。39位参与者具有以下统计资料:平均年龄为42.5岁,其中标准差(SD)为12(SD=12),T1DM的平均持续时间为21.6年(SD=94),平均HbAlc=7.4%(SD=0.8),16位为男性。
该研究由弗吉尼亚大学IRB受治疗者(University of VirginiaIRB Subjects)认可。受治疗者在研究之前的晚上被允许进入一般临床研究中心(综合临床研究中心,general clinic research center,GCRC)。整夜将参与者的BG水平控制在100-150mg/dl(5.5-8.3mmol/l)的血糖正常范围内。将Minimed CGMSTM连接于每位受治疗者并在研究期间按照制造商的说明进行校准。所有CGMSTM被插入腹中。在早上实施高胰岛素钳夹(hyperinsulmemic clamp)。每个钳夹使用了1mU/kg/min的恒定胰岛素输注速率以及可变的葡萄糖输注速率以达到和维持BG水平在约110mg/dl(约6mmol/l)。其后,降低葡萄糖输注速率以允许BG的受控下降(约1mg/dl/min)直到BG达到50mg/dl(约2.8mmol/l)。然后重新开始葡萄糖输注以恢复到正常葡萄糖水平。钳夹研究的血糖正常部分以70至210分钟的时间长短变化;以及BG降低操作的持续时间在30至60分钟范围内变动。恢复时间在30至60分钟范围内变动。通过将手加热至50℃来获得动脉化血液(arterialized blood),然后每5分钟取样,用于参照BG水平。考虑到胰岛素要达到其稳定状态效应,在开始输注以后的最初15分钟的数据被忽略。使CGMSTM读数与参照BG同步。
传感器最佳校准的计算机模拟
利用在上述钳夹研究期间获得的两个参照BG值对传感器的再校准进行了计算机模拟,如图5所示,其描述了作为两个BG值之间的差异的函数的传感器误差。模拟的再校准使用了CGMSTM的标准线性校准函数。对来自再校准的结果与在实验期间显示的传感器本身的精确度以及与利用所有可获得的参照BG值的“完美”校准进行了比较。
参照图5,X轴表示以BG单位(mg/dl)的在两个模拟校准点之间的距离;而Y轴表示利用两点校准的传感器输出的平均绝对误差(MAE)。在该图中可以看到,如果两个校准BG的值接近,则MAE会较高。当差异接近20mg/dl时,MAE快速下降,并且其后缓慢下降。图中的上水平线表示传感器本身校准的MAE;而下水平线表示来自利用所有可获得的参照点的“完美”校准的MAE。
在图中还可以看到,数值大于30mg/dl但小于40mg/dl的BG校准差异d获得极好的结果;而数值大于40mg/dl的差异d获得“几乎完美的”结果。值得指出的是,上述在实验期间的传感器校准总是在稳定的BG(保持在血糖正常)期间完成的,因此BG变化的速率的影响为最小。
生理时滞的校正
除CGS的校准之外,BG与IG之间的生理时滞也引起CGS输出的不准确性。这来自以下事实:大多数目前的CGS装置并不直接测量血糖水平,而是以在间质液中的IG水平来代替。然后CGS装置将IG读数转换成BG的估计值。因此,从IG到BG的改善的转换方法将导致改善的CGS性能。本发明的一个目的是通过包括IG与BG水平之间的生理时滞来改善从IG到BG的转换。这样的改善是通过分析和结合IG与BG之间的时间相关性(timedependence)来完成的。具体地说,建立数学模型以用来描述BG与IG或CGS输出之间的时间相关性。基于已建立的模型,导出了数学方程以定量地表示CGS输出对BG的时间相关性,即CGS是BG的函数。然后转换此方程,以便表示作为CGS的函数的BG。因此,该反向方程(inverted equation)可以用来预测对于给出CGS输出值的BG水平。在应用中,将反向方程应用于原始CGS数据以产生准确的BG估计。
数学模型
鉴于葡萄糖是相对小分子的事实,所以认为,葡萄糖可以自由地扩散通过毛细管壁,如血管和脂肪组织。脂肪组织是高度血管化的;并且间质液占据细胞之间相对较0的层。该事实意味着,不存在远离细胞表面的体积元素(volume element),也不存在远离毛细管壁的体积元素。因此,葡萄糖在间质液中的摄取和扩散可以假设为在拓扑上是相对均匀的。
根据本发明的IG和BG的运输行为描述在图1中。再次参照图1,葡萄糖在间质液与血管(或脂肪组织)之间的运输行为可以作为扩散来建立模型。在间质液中的IG还经受消耗,其导致量和/或浓度降低。
为了导出数学扩散方程,假定所考虑的特定局部间隙环境并不显著地对BG/时间曲线的展开有贡献,因此,BG水平BG(t)的时间相关性独立地逐渐形成,并且可以处理为系统中的外源变量。这种假设是特别安全的,尤其在高胰岛素钳夹的情况下,其中BG水平受到葡萄糖IV输注的最大控制。进一步假设,葡萄糖的摄取按照IG独立途径、或按照由米-门氏(Michaelis-Menten)动力学描述的途径,如分别表示在方程1a和1b中:
方程1a
方程1b
在上述方程中,α是每单位体积每单位时间内葡萄糖的摄取。
应当注意到,方程1a和1b描述了明确的时间相关性。其它变量,如胰岛素水平、锻炼等(其可以直接影响葡萄糖摄取BG和IG)并没有从方程中排除。Km是方程1b中的常数;并且它并不引入另外的可拟合(fitable)参数。实际上,Km可以采用那些针对GLUI-4的活性所发表的值,如在Zierler K的“Whole body glucose metabolism(全身葡萄糖代谢)”,Am J Physiol.276:E409-E426,1999中所陈述的,将其主题的全部内容以引用方式结合于本文。通过参照菲克定律,由于从血液的扩散所引起的IG(t)变化可以通过方程2加以描述:
方程2
其中β是毛细管壁对葡萄糖的渗透性。因为在间质液中没有葡萄糖的其它明确的来源或渗透(sink),所以葡萄糖的净变化可以通过加入方程1a和1b来导出,其可以表示为以下方程3a和3b,其中方程3a对应于均匀摄取扩散模型,而方程3b对应于米-门氏动力学模型。
方程3a
方程3b
方程3a和3b的数学解
方程3a是具有解析解的常微分方程;而方程3b是需要数值模拟的第二型阿贝耳(Abel)方程的非线性微分方程。方程3a的解析解表示在以下方程4中:
方程4
通过假定α和β是随时间恒定的,利用如表示在以下方程5中的Δ-τ表示法,方程4可以被简化成以下方程6:
Δτ(f(t))≡f(t)-e-β(t-τ)f(τ)
方程5
方程6
通过在方程6中消除BG(t)的高阶导数项,于是方程6可以被简化成一种形式,而基于卡尔曼滤波/平滑技术的卡尔曼递归分析(Kalman recursion analysis)则可以应用于该形式。这样的技术的一个实例在“Optimal Control and Estimation(最佳控制和估计)”New York:Dover Publications,199437中陈述,将其主题的全部内容以引用方式结合于本文,其中使用了“状态空间模型”和递归方法。该递归方法附在本文的附录B中。利用“状态空间模型”并借助于CGS读数被小时间ξ均匀隔开的假设,在以下方程9中可以描述用于CGS输出和IG的相关性的状态空间模型。
CGS(ti)=校准×IG(ti)+R×w0 方程9,其中
IG(ti)=e-βτ×IG(ti-ξ)+f(ti)+Q×ws;以及其中
用于包括BG展开的CGS输出的状态空间模型可以表示为以下方程10。
方程10
用于包括BG展开和线性投影(linear projection)的CGS输出的状态空间模型可以表示为以下方程11。
方程11
给出消耗、渗透性、IG、以及IG的变化速率的估计值,则可以类似地进行方程3a和3b的反演。3a和3b的反向方程分别表示为以下方程12a和12b:
方程12a
方程12b
方程12a和12b表明,使用CGS对提供IG的变化速率的精确估计变得很重要。反向方程12a和12b的表达也是可能的,其表示为以下方程13和14。
方程13
方程14
应当注意到,在上述模型中的观测值是在方程7中定义的BG(t)的函数。如果接受多项式平滑/插值公式来描述BG(t)的进程,那么它也可以被线性反演,如所附的附录C中所示。
算法实现
因而,上述数学模型和方程可以应用于CGS读数,以便利用CGS输出通过预测BG水平来校正IG(t)与BG(t)之间的生理时滞。本发明的一个示例性过程呈现在图6的流程图中。
参照图6,在步骤132展开如上所讨论的数学模型。该模型描述了BG(t)、IG(t)、以及CGS输出SG(t)之间的动力学。基于在步骤132展开的模型,第一数学方程被简化,以便求出作为BG(t)的函数的SG(t)。该第一方程优选利用菲克扩散定律和米-门氏假设加以展开。然后反演该第一方程以便在步骤136估计作为CGS输出SG(t)的函数的BG(t)的动力学。然后反向方程可以应用于CGS原始数据以在步骤138改善CGS读数的精确度。在步骤138反向方程对于原始CGS数据的示例性应用图解说明于图7中。
参照图7,在步骤140获得原始数据集。数据集包括CGS输出SG(ti)、取样时间Tm(ti)、以及参数Par(α、β、校准),其中参数校准是在方程9中表示为校准的参数。在步骤142优选对原始CGS数据进行预处理(虽然不需要)。具体地说,通过初始回归和/或滤波对CGS原始数据进行处理以平滑CGS原始数据。该步骤是重要的,因为研究表明,原始CGS数据目前受到噪声和随机尖峰(信号)的影响,其中噪声和随机尖峰(信号)需要滤掉以便改善反向模型方程的精确度。存在许多方式来回归和/或过滤CGS数据。例如,可以基于临床观测的BG变化速率结合贝叶斯估计技术(Bayesian estimation technique)来对原始CGS数据进行过滤。在另一实施例中,可以用方程10和11的基于卡尔曼滤波器的方法来处理原始CGS输出,其借助于BG的展开满足其限制的假设来产生最佳估计。
在给出预处理的CGS数据(或直接的原始CGS数据而没有上述预处理)的情况下,在步骤144计算CGS输出的变化速率(时间导数)。CGS输出的独特特点是能够估计它们的输出的导数。然而,由于观测值和系统噪声以及漂移的(wandering)传感器灵敏度,观测到,这样的即时原始估计很少是最佳的。相反,利用指数权(exponential weight)的短区间多项式光滑技术可以产生更好的结果,如实验所指出的。然后,在步骤146将IG比例系数应用于CGS,接着在步骤148应用如上所述的反向模型来估计BG水平。
软件实现
如参照图7所描述的过程可以以许多方式加以实施,其中之一是以软件形式。例如,上述过程可以用实时版本加以实施,其尤其可直接应用于CGS输出以便将原始CGS输出转换成BG估计值,并且产生实时结果。本文附带了实时实施程序代码的集作为附录D。可替换地,可以作为追溯版本(retrospective version)来实施该过程,其中追溯版本尤其可用于进行CGS精确度的追溯改善,并且可应用于并不显示实时数据的CGS。本文附带了用于追溯实施的程序代码集作为附录E。应当注意到,为了便于参考,消除了每个行号的起始点。采用了参数的线性化版本,以有利于计算速度。本领域技术人员应当明了,在附录D和E中呈现的程序代码是基于说明的目的,并且不应当解释为限制本发明。在不偏离本发明的精神的情况下,还可以应用许多其它版本。
对方法进行试验
上述用于校正BG与CGS输出之间的生理时滞的过程已基于在一项研究期间获得的数据加以评估,其中所述研究是在弗吉尼亚大学一般临床研究中心(University of Virginia general Clinical ResearchCenter(GCRC))进行的,其是正在进行的NIH研究基金(RO1 DK51562,Principal Investigator Boris Kovatchev)的增加的项目。该增加的研究由Abbott Diabetes Care(P.I.William Clarke)资助以在两种CGS(Abbott NavigatorTM和Minimed CGMS)之间进行直接比较。模型的展开和试验属于增加的研究的目的。
用于研究的受治疗者
16位患有TIDM的受治疗者参与了研究,其中11位为男性,5位为女性,年龄为42岁(标准差(SD)为3年),糖尿病的持续时间为20年(SD为3年)。从每一位受治疗者获得知情同意。在体检以后,在研究之前的晚上受治疗者被许可进入一般临床研究中心。按照制造商的说明以及如建议的那样进行校准,在开始数据记录以前,将一种CGS系统,即Freestyle NavigatorTM应用于每位受治疗者约12小时。所有系统被插入腹中。直到第二天早上没有进行BG参考与CGS的比较。研究方案定义为,连续两天进行相同的高胰岛素钳夹。
每天高胰岛素钳夹使用了恒定的40mU/kg/min的胰岛素输注速率以及可变的葡萄糖输注速率以达到和维持BG水平在约110mg/dl。其后,降低葡萄糖输注速率以允许BG水平的受控下降(约1mg/dl/min)直到BG水平达到40mg/dl。该研究的血糖正常钳夹部分以70至210分钟的时间长短变化,而BG降低操作的持续时间在30至60分钟范围内变动。每5分钟采集动脉化血液并利用Beckman Glucose Analyzer(Beckman Instruments,Inc,Fullerton,CA)确定参考BG水平。每分钟记录Freestyle NavigatorTM葡萄糖读数并与参照BG同步,其中精确度为30秒。以5分钟的间隔计算参照和NavigatorTM速率以及BG变化的方向。这种程序为研究中的16位参与者产生29个钳夹数据集。
用于分析的软件
利用R 2.1.1进行数值分析,其是开放原始码的免费程序设计语言并且适合于统计分析(http//www.r-project.org)。除了基本程序包以外,还使用了“odesolve”、“fields”、和“dsel”程序包以及它们与CRAN储存库的相关性,另外Microsoft Excel用来产生图表。
结果
将方程12a应用于未过滤的NavigatorTM原始数据,其中参数是通过非线性最小二乘法而获得的。每个数据行程(数据运行,datarun)在下降到低血糖的起点处开始。表1示出了所有29个钳夹事件结果的总结。可以看到,NavigatorTM的平均RMS误差减少3倍(fold)以上,并且%RMS误差减少5倍以上。此外,通过该模型改善了参照与传感器BG之间的相关性:
表1
图8至图10描绘了这些结果。具体地说,图8示出了29个事件(以5分钟数据间隔在最低点比较)的平均值。实心菱形符号是由Beckman分析仪记录的参照BG。实心正方形是NavigatorTM的数据。空心正方形是通过本发明的方法校正的NavigatorTM的数据。在图中可以看到,模型校正数据比原始的NavigatorTM输出更接近参照BG。图9和图10示出了具有1分钟数据间隔的两个个体患者。在两种情况下,通过本发明的方法校正NavigatorTM数据(空心正方形)导致参照BG的改善的跟踪(tracing)。
组合的准确度改善方法和装置
如上所讨论的,通过本发明的一个实施例经由改善的校准方法可以改善CGS输出的准确度。可替换地,还可以通过校正BG与IG之间的生理时滞来改善CGS的准确度。在本发明的另一实施例中,可以结合上述两种校正方法以便进一步改善CGS准确度。图11图解说明了根据本发明的另一个实施例用于进行组合准确度改善过程的流程图。
参照图11,组合过程开始于校准CGS,其中利用如上文参照图4所描述的方法。可以消除或减小系统误差。在步骤152,通过滤波和/或平滑校准的CGS输出,对来自校准的CGS的CGS输出进行预处理。可以利用如在图7中的步骤142所描述的相同或不同的方法执行此步骤。应当注意到,虽然优选但并不需要此步骤。由于该预处理,在CGS输出中的传感器噪声和/或较小的随机波动以及一阶时间导数可以消除或简化。在步骤144计算CGS输出SG(ti)的一阶时间导数。然后,将IG比例系数应用于CGS输出,接着应用反向模型方程以估计BG水平,如参照图6所描述的,在这里将不重复。
表2示出了利用根据本发明的实施例的方法的准确度改善,其中通过比较由本发明的方法所获得的CGS输出和本领域中典型CGS(没有采用本发明的方法)的CGS输出。获自本领域典型CGS的CGS输出可参见“Evaluating the accuracy of continuous glucosemonitoring sensors:continuous glucose error grid analysis illustrated bytherasense freestyle navigator data,”来自B Kovatchev,L GonderFrederick,D Cox,和W Clarke,Diabetes Care,vol.27,pp 1922-1928,2004。
表2
表2的小组A提供了在上述钳夹研究期间Minimed CGMSTM的准确度的连续式葡萄糖误差删格分析(continuous glucose error-gridanalysis)(CG-EGA),分层为低血糖和血糖正常。临床上准确的传感器读数在低血糖期间为50.0%而在血糖正常期间为96.4%。这些百分比之间的较大差异主要起因于要求更高的临床准确度标准。对于低血糖事件:虽然对于稳定的血糖正常状态对于传感器误差存在较大的临床允许误差(clinical tolerance),但是在临床上危险的和快速发展的状态期间,如低血糖,则希望传感器满足更高的标准以便提供用于适当的和及时的治疗决定的准确反馈。CG-EGA反映了这种区别。另外,MAE和平均绝对百分误差(MAPE)包括在表1中并且也由BG范围分层。
在表2中的小组B提供了由两个参照BG再校准的传感器的CG-EGA、MAE以及MAPE,其中两个参照BG相隔是30mg/dl(例如,微分d是30mg/dl),其是在研究的BG范围内的临床上合理的微分。可以看到,CG-EGA准确读数的百分比从50%增加到86,7%,而在低血糖期间MAE从27.9mg/dl减小到10.9mg/dl。在血糖正常期间也观察到MAE和MAPE的改善。
表2的小组C提供了在传感器再校准以后估计的SIG与BG的CG-EGA、MAE以及MAPE。可以看到,在BG波动以后SIG的“准确度”较高,几乎100%,其意味着用于潜在的传感器准确度的极好的理论限度。
可以以许多方式来实施本发明的实施例。例如,它可以实施为连续式葡萄糖传感器的功能构件、或可以实施为与连续式葡萄糖传感器有关的独立的模块。在任何一种情况下,本发明的实施例可以实施为安装在计算装置中的软件的一组程序代码、或耦合于连续式葡萄糖传感器的硬件装置的一组计算机可执行代码。与实施介质无关,本发明的实施例可以与单个连续式葡萄糖传感器有关,用于改善单个葡萄糖传感器的准确度。可替换地,可以以这样的方式来实施本发明的实施例,即实时CGS数据、连同相关的误差和误差校正参数和数据可以被传送到误差处理中心。该传输可以或可以不带有葡萄糖数据。这样,可以建立通用化的连续式葡萄糖传感系统。
图12概略地图解说明一种示例性系统,在该系统中可以实施本发明的实施例。参照图12,临床机构(clinic setup)158提供了用于医生(例如164)诊断患有与葡萄糖有关的疾病的患者(例如160)的地方。连续式葡萄糖传感器(或结合有葡萄糖测试功能的传感装置)162可以用来监测和/或测试患者的葡萄糖水平。这样的监测和/或测试可以是短期的(例如门诊就诊)或长期的(例如病房停留或家庭)。该连续式葡萄糖传感器在其中结合有如上所讨论的准确度改善方法的实施例。具有改善的准确度的CGS输出可以由医生用于适当的操作(action),如对患者进行胰岛素注射或食物供给、或其它适当的操作。可替换地,具有改善的准确度的CGS输出可以被传送至计算机终端168,用于即时或未来分析。该传送可以通过电缆或无线的或任何其它适宜的方法。来自患者的具有改善的准确度的CGS输出还可以被传送至便携式装置,如PDA 166。具有改善的准确度的CGS输出可以被传送至葡萄糖监测中心172,用于处理和/或分析。这样的传送可以通过许多方式来完成,如网络连接170,其可以是有线的或无线的。
除具有改善的准确度的CGS输出以外,还可以将误差、用于准确度改善的参数、以及任何准确度相关的信息传送至如计算机168、和/或数据处理中心172,用于进行误差分析。这可以为葡萄糖监测中心提供集中化准确度监测和/或准确度增强,这是起因于葡萄糖传感器的重要性。
如早先所述,还可以在与目标连续式葡萄糖传感器有关的独立的计算装置中实施本发明的实施例。在图13中示意性地图解说明了一种示例性计算装置,在该计算装置中可以实施本发明的实施例。虽然这样的装置对于本领域技术人员来说是众所周知的,但为了方便其他读者,本文将提供简要说明。
参照图13,在其最基本的构造中,计算装置174通常包括至少一个处理单元180和存储器176。取决于计算装置的精确构造和类型,存储器176可以是易失的(如RAM)、非易失的(如ROM、闪存等)或两种的某种组合。
另外,装置174还可以具有其它特征和/或功能性。例如,该装置还可以包括另外的可移动式存储器和/或不可移动式存储器,其包括但不限于磁盘或光盘或磁带、以及可写电存储介质。这样的另外的存储器表示为可移动式存储器182和不可移动式存储器178。计算机存储介质包括易失性介质和非易失性介质、可移动式介质和不可移动式介质,这些介质是以任何方法或技术来实施的,用于存储信息如计算机可读指令、数据结构、程序模块或其它数据。存储器、可移动式存储器以及不可移动式存储器都是计算机存储介质的实例。计算机存储介质包括但不限于:RAM、ROM、EEPROM、闪存或其它存储技术、CDROM、数字多用途光碟(DVD)或其它光学存储器、盒式磁带、磁带、磁盘存储器或其它磁存储装置、或可以用来存储期望的信息并且可以由装置访问的任何其它介质。任何这样的计算机存储介质可以是装置的一部分、或连同装置一起使用。
该装置还可以包括一个或多个通信连接184,该通信连接使该装置可以与其它装置(例如其它计算装置)通信。通信连接携带在通信介质中的信息。通信介质通常体现为计算机可读指令、数据结构、程序模块或在调制的数据信号(如载波或其它运输机构)中的其它数据,并且包括任何信息传送介质。术语“调制的数据信号”是指这样的信号,该信号具有一个或多个其特征集合或以这样的方式加以变化使得编码在信号中的信息。作为实例(但并不作为限制),通信介质包括有线介质如有线网络或直接有线连接,以及无线介质如声音、RF、红外线以及其它无线介质。如上所述,如在本文中所使用的术语计算机可读介质包括存储介质和通信介质两者。
本领域技术人员应当明了,本文已讨论了用于改善连续式葡萄糖传感装置和利用该装置的系统的准确度的新的和有用的方法。然而,考虑到可以应用本发明的原理的许多可能的具体实施方式,应当认识到,本文参照附图所描述的具体实施方式仅是示例性的并且不应当视为限制本发明的范围。本领域的技术人员应当明了,在不偏离本发明的精神的情况下,可以在安排和细节上对示出的具体实施方式加以改进。因此,如本文描述的本发明设想所有这样的具体实施方式可以属于所附权利要求以及其等同替换的范围。
附录A
将以下出版物和美国临时申请的主题的全部内容以引用方式结合于本文。
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附录B-卡尔曼递归
附录B:卡尔曼递归(Kalman Recursion)
如果我们假设我们的数据遵循形式为A1.1的系统,其中x是隐系统状态向量(矢量)、y是观测向量、ws是具有协方差矩阵Q的系统噪声、wO是具有协方差矩阵R的观测噪声,以及F、G、H矩阵定义系统转变,那么该系统被称为状态空间模型。
xi+1=Fxi+Gzi+wS
A1.1)
yi=Hxi+wO
卡尔曼滤波器是两步过程。首先,进行一步向前预测。接着,基于新的测量结果对预测进行校正。让我们引入记号其是指“在测量结果达到和包括j的情况下在i处的x的估计值”。假设我们具有x的目前估计值以及该估计值的误差协方差P,那么我们可以将第一步骤写成A1.2。为了对估计值进行校正,然后我们在A1.3中计算了卡尔曼增益,并且在A1.4中利用新的测量结果y对估计值进行更新。
A1.2)
A1.4)
附录C-递归方法
可以如在A2.9中写对角算子,其将A2.8简化成A2.10。此外,通过再次并列(concatenate)第一q+1列向量,我们可以形成在A2.11中的所期望的矩阵A2.1。
A2.9)Cj≡{C[ii]=1,else C[ij]=0},
最后,通过将A2.11代入A2.3,我们到达A2.12,一种可以通过诸如QR分解的方法来求解的线性系统。
附录D-实时实施的程序代码
版本1:实时应用
1 GetNewBGPreds<-function(nav,times,par){
#需要几分钟的时间使参数工作正常
#消除漏测值
2.times[!is,.na(Nav)]->times;Nav[!is.na(Nav)l->Nav;
#第二,应用校准参数
3.Navipar[[31]->Nav;
#第三,产生阵列,以保持IG,和点(dot)IG的估计值
4.rep(O.,times=length(Nav))->Idot;Nav->IG;
#第四,在大量数据已被收集之前的时间内产生点IG的估计值
5.(IG[2]-IG[11)/(times[Zl-times ill)->Idot 121;
6.#线性内插法(线性插值)
7data frame(y=IG[1:3],x=times[1:31)->td;
8lm(y-x,data=td)->tx;t xScoefficients[2]->tx;attr(tx,″names″)<-NULL;tx->Idot[3];
9data frame(y=IG[1:4],x=times[1:4])->td;
10lm(y-x,data=td)->tx;tx$coefficients[2]->tx;attr(tx,″namesw)<-NULL;tx->Idot[4];
#第五,在CGS数据上使用样条来产生估计值
11for(i in 5:length(Nav))(
12times[(i-4):(i)]->tt;Nav[(i-4):(i)1->td;
13smooth spline(x=:tt,y=td,df=1)->temp;
14predict(temp,t imes[i],deriv=1)$y-2Idot[i];
15predict(temp,times[I])Sy->IG[il;
16}
#第六,应用主方程
17IG+Idotkpar[[2]]+par[[1]]->NewBG;
#第七,返回结果
18NewBG)
附录E-追溯实施的程序代码
版本2:追溯校正
除了实时实施外,可以通过延长将每一样条内插入12行(从“(i-4):(i)”“(i-4):(i+4)”)的时间来引入人工延迟。在完全的追溯分析中,可以使用下面给出的更简单的形式。
19GetOldBGPreds<-function(Nav,times,par){
#消除漏测值
20times[!is.na(Nav)]->times;
21Nav[!is.na(Nav)1->Nav;
#应用校准参数
22par[[3]1*Nav->Nav;
#在所有数据中产生平滑样条回归
23sreg(times,Nav)->Navfit;
#使用它来预测IG
24predict(Navfit,tirnes)->IG;
#使用它来预测点IG
25predict(Navfit,tirnes,deriv=1)->Idot;
#应用主方程
26(Idot)*par[[2]]+par[[I]1+IG->BGρreds;
#返回预测
27BGpreds)
最后,如果期望方程3的全部米-门氏形式,则在17和27行中可以简单地用“par[[1]]*IG/(126.+IG)”代替“par[[1]]”。R包含许多其他平滑和内插程序,并且使用sreg、平滑样条、以及lm可以用它们中的多种来代替,虽然输入/输出格式经常不同。
Claims (30)
1.一种传感器信号处理方法,包括:
从葡萄糖传感器接收葡萄糖传感器数据;
确定校准因子,所述校准因子使葡萄糖传感器数据与葡萄糖水平相关;
确定所述葡萄糖传感器数据的变化速率;
基于第一数学模型确定在间质液中的葡萄糖摄取或消耗参数;
基于第二数学模型确定在血液与间质液之间的葡萄糖扩散参数;以及
基于所述葡萄糖传感器数据、所述葡萄糖传感器数据的变化速率、所述校准因子、所述葡萄糖摄取或消耗参数、和所述葡萄糖扩散参数来确定葡萄糖水平。
2.根据权利要求1所述的方法,其中,所述葡萄糖传感器数据通过使所述葡萄糖传感器数据滤波而预处理。
3.根据权利要求2所述的方法,其中,所述滤波包括利用卡尔曼滤波器来滤波。
4.根据权利要求2所述的方法,其中,所述滤波包括采用贝叶斯估计技术。
5.根据权利要求1所述的方法,其中,所述葡萄糖传感器数据的变化速率通过使用利用指数权的短区间多项式光滑技术来确定。
6.根据权利要求1所述的方法,其中,所述第一数学模型基于米-门氏动力学。
7.根据权利要求6所述的方法,其中,所述第一数学模型使用用于葡萄糖转运子的米-门氏常数。
8.根据权利要求7所述的方法,其中,所述葡萄糖转运子是GLUT-4。
9.根据权利要求1所述的方法,其中,所述葡萄糖摄取或消耗参数取决于胰岛素水平和锻炼中的至少一种。
10.根据权利要求1所述的方法,其中,所述第二数学模型基于浓度梯度类扩散模型。
11.根据权利要求10所述的方法,其中,所述葡萄糖扩散参数基于毛细管壁对葡萄糖的渗透性。
12.根据权利要求1所述的方法,其中,所述葡萄糖传感器数据是实时数据。
13.根据权利要求1所述的方法,其中,所述葡萄糖传感器数据是追溯数据。
14.根据权利要求1所述的方法,其中,所述校准因子基于在第一时间的第一葡萄糖传感器数据测量和相应的第一参照血液葡萄糖测量来确定。
15.根据权利要求14所述的方法,其中,所述校准因子进一步基于在第二时间的第二葡萄糖传感器数据测量和相应的第二参照血液葡萄糖测量来确定,其中,所述第二葡萄糖传感器数据测量仅仅在所述第一葡萄糖传感器数据测量与所述第二葡萄糖传感器数据测量之间的差异大于预定差异阈值时使用。
16.一种传感器信号处理装置,包括:
处理单元;以及
可操作地耦接于至少一个处理单元的存储器,所述存储器用于存储指令,当所述处理单元执行所述指令时,使得所述处理单元从葡萄糖传感器接收葡萄糖传感器数据;确定校准因子,所述校准因子使葡萄糖传感器数据与葡萄糖水平相关;确定所述葡萄糖传感器数据的变化速率;基于第一数学模型确定在间质液中的葡萄糖摄取或消耗参数;基于第二数学模型确定在血液与间质液之间的葡萄糖扩散参数;以及基于所述葡萄糖传感器数据、所述葡萄糖传感器数据的变化速率、所述校准因子、所述葡萄糖摄取或消耗参数、和所述葡萄糖扩散参数来确定葡萄糖水平。
17.根据权利要求16所述的装置,其中,所述存储器用于存储指令,当所述处理单元执行所述指令时,使得所述处理单元对所述葡萄糖传感器数据滤波。
18.根据权利要求17所述的装置,其中,所述滤波包括利用卡尔曼滤波器来滤波。
19.根据权利要求17所述的装置,其中,所述滤波包括采用贝叶斯估计技术。
20.根据权利要求16所述的装置,其中,所述葡萄糖传感器数据的变化速率通过使用利用指数权的短区间多项式光滑技术来确定。
21.根据权利要求16所述的装置,其中,所述第一数学模型基于米-门氏动力学。
22.根据权利要求21所述的装置,其中,所述第一数学模型使用用于葡萄糖转运子的米-门氏常数。
23.根据权利要求22所述的装置,其中,所述葡萄糖转运子是GLUT-4。
24.根据权利要求16所述的装置,其中,所述葡萄糖摄取或消耗参数取决于胰岛素水平和锻炼中的至少一种。
25.根据权利要求16所述的装置,其中,所述第二数学模型基于浓度梯度类扩散模型。
26.根据权利要求25所述的装置,其中,所述葡萄糖扩散参数基于毛细管壁对葡萄糖的渗透性。
27.根据权利要求16所述的装置,其中,所述葡萄糖传感器数据是实时数据。
28.根据权利要求16所述的装置,其中,所述葡萄糖传感器数据是追溯数据。
29.根据权利要求16所述的装置,其中,所述校准因子基于在第一时间的第一葡萄糖传感器数据测量和相应的第一参照血液葡萄糖测量来确定。
30.根据权利要求29所述的装置,其中,所述校准因子进一步基于在第二时间的第二葡萄糖传感器数据测量和相应的第二参照血液葡萄糖测量来确定,其中,所述第二葡萄糖传感器数据测量仅仅在所述第一葡萄糖传感器数据测量与所述第二葡萄糖传感器数据测量之间的差异大于预定差异阈值时使用。
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CN110398196A (zh) * | 2019-07-19 | 2019-11-01 | 陕西威尔机电科技有限公司 | 一种用于精密测量的lvdt传感器信号处理方法及系统 |
CN114081484A (zh) * | 2021-11-24 | 2022-02-25 | 上海微创生命科技有限公司 | 连续血糖检测方法、系统及可读存储介质 |
WO2023093457A1 (zh) * | 2021-11-24 | 2023-06-01 | 上海微创生命科技有限公司 | 连续血糖检测方法、系统及可读存储介质 |
CN114081484B (zh) * | 2021-11-24 | 2024-02-27 | 上海微创生命科技有限公司 | 连续血糖检测方法、系统及可读存储介质 |
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JP2012210431A (ja) | 2012-11-01 |
CN101365374A (zh) | 2009-02-11 |
US20160007890A1 (en) | 2016-01-14 |
CA2620586A1 (en) | 2007-03-08 |
WO2007027691A1 (en) | 2007-03-08 |
CN101365374B (zh) | 2011-11-16 |
JP2009507224A (ja) | 2009-02-19 |
EP1921980A1 (en) | 2008-05-21 |
US10194850B2 (en) | 2019-02-05 |
EP1921980A4 (en) | 2010-03-10 |
US20190343433A1 (en) | 2019-11-14 |
US20080314395A1 (en) | 2008-12-25 |
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