CN101636104B - 实时检测分析物传感器灵敏度下降的方法及系统 - Google Patents

实时检测分析物传感器灵敏度下降的方法及系统 Download PDF

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CN101636104B
CN101636104B CN2007800482892A CN200780048289A CN101636104B CN 101636104 B CN101636104 B CN 101636104B CN 2007800482892 A CN2007800482892 A CN 2007800482892A CN 200780048289 A CN200780048289 A CN 200780048289A CN 101636104 B CN101636104 B CN 101636104B
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马克·D·布雷顿
鲍里斯·P·科瓦特舍维
埃尔温·S·布迪姆安
肯纳斯·J·多尼格
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Abbott Diabetes Care Inc
University of Virginia Patent Foundation
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B17/00Monitoring; Testing
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0015Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
    • A61B5/002Monitoring the patient using a local or closed circuit, e.g. in a room or building
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/145Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/14503Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue invasive, e.g. introduced into the body by a catheter or needle or using implanted sensors
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/145Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/14532Measuring 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
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/145Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/14546Measuring 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 analytes not otherwise provided for, e.g. ions, cytochromes
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2560/00Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
    • A61B2560/02Operational features
    • A61B2560/0223Operational features of calibration, e.g. protocols for calibrating sensors
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/145Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/1468Measuring 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 chemical or electrochemical methods, e.g. by polarographic means
    • A61B5/1473Measuring 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 chemical or electrochemical methods, e.g. by polarographic means invasive, e.g. introduced into the body by a catheter
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation

Abstract

本发明提供了用于提供被分析物传感器灵敏度下降的实时检测的方法、系统和计算机程序产品,其中该系统是连续葡萄糖监测系统。

Description

实时检测分析物传感器灵敏度下降的方法及系统
技术领域
本申请要求于2006年10月26日提交的标题为“用于实时检测连续葡萄糖传感器(CGS)灵敏度下降的方法、系统及计算机程序产品”的第60/854,566号美国临时申请的优先权,其公开内容通过引证结合于此用于所有用途。本申请涉及Marc D.Breton等人于2007年10月26日提交的第11/925,689号美国申请,其公开内容通过引证结合于此用于所有用途。 
背景技术
被分析物(例如,葡萄糖)监测系统(包括连续及离散监测系统)通常包括:小的、重量轻的电池供电且由微处理器控制的系统,其被配置为使用静电计检测与相应的被测葡萄糖水平成比例的信号,以及用于发送收集到的数据的RF信号。一些被分析物监测系统的一个方面包括经皮的或皮下的被分析物传感器配置,其(例如)被部分地设置在将被监测葡萄糖水平的监测对象的皮肤上。传感器单元可以使用由通过接触系统连接的控制电位(稳压器)模拟电路驱动的二或三电极(工作、基准和计数器电极)配置。 
被分析物传感器可以被配置为其一部分被放置到患者皮肤下,以检测该患者的被分析物水平,以及该被分析物传感器组成部分的另一部分与发射器单元进行通信。发射器单元被配置为通过诸如RF(射频)通信链路的无线通信链路将由传感器检测到的被分析物 水平发射至接收器/监视器单元。该接收器/监视器单元对接收到的被分析物水平进行数据分析,以产生与被监测的被分析物水平有关的信息。 
根据前述,期望对葡萄糖水平波动具有精确的评估,并且特别是,落到传感器灵敏度之外的称作早期信号衰减(ESA)的被分析物传感器信号的检测。 
发明内容
在一个实施例中,公开了一种方法、一种系统以及一种计算机程序产品,它们用于:接收多个被分析物传感器相关信号、确定与接收到的多个被分析物传感器相关信号相关联的信号衰减的概率、在确定的概率超过预定的阈值水平时验证信号衰减的存在、以及产生与信号衰减存在的验证相关联的第一输出信号。 
本公开的这些以及其他目的、特征以及优点将从下面实施例的详细描述、所附权利要求以及附图而变得充分明显。 
附图说明
图1示出了用于实施本公开的一个或多个实施例的数据监测及管理系统的框图; 
图2是根据本公开一个实施例的图1所示的数据监测及管理系统的发射器单元的框图; 
图3是根据本公开一个实施例的图1所示的数据监测及管理系统的接收器/监测器单元的框图; 
图4A-图4B分别示出了根据本公开一个实施例的被分析物传感器的透视图和截面图; 
图5是示出本公开中的一个实施例的实时早期信号衰减(ESA)的框图; 
图6是示出根据本公开一个实施例的全部ESA检测程序的流程图; 
图7是示出根据本公开一个实施例的结合图5所示的模块1描述的传感器电流异常的实时监测的流程图; 
图8是示出根据本公开一个实施例的图5中的模块2的验证程序以确认或拒绝模块1的输出的流程图; 
图9示出了根据本公开一个实施例的基于图5中的模块1的滑动窗口处理的实时电流信号特性评估方法; 
图10示出了根据本公开一个实施例的用于图5的模块1的系数的引导估计(bootstrap estimation); 
图11示出了根据本公开一个实施例的图5的模块2的标准化灵敏度密度的高斯内核估计(Gaussian kernel estimation);以及 
图12示出了根据本公开一个实施例的基于图7的预定测试数据集和检测阈值的与图5的组合的第一和第二模块的输出曲线相比较的第一模块的输出曲线。 
具体实施方式
如下面详细描述的,根据本公开的各个实施例,提供了用于在数据处理和控制系统(例如,包括被分析物监测系统)中的被分析物传感器灵敏度下降的实时监测的方法、系统和计算机程序产品。具体地,在本公开的范围内,提供了用于检测会引起临床显著传感器相关误差(包括,例如,由传感器灵敏度(定义为被分析物传感器电流电平与血糖水平之间的比率)下降表示的早期传感器衰减(ESA),该下降可以存在于传感器寿命的最初12-24小时期间,或使用被分析物传感器的夜间(“夜间下降”)期间)的低传感器灵敏度事件(episode)的方法、系统和计算机程序产品。 
图1示出了根据本公开一个实施例的数据监测和管理系统(诸如,被分析物(例如,葡萄糖)检测系统100)。为了方便,主要从与葡萄糖监测系统相关的方面进一步描述了本主题公开,且这种描述绝不用于限制本公开的范围。还应该理解,被分析物检测系统可以被配置为监测各种被分析物,例如,乳酸盐等。 
可以被监测的被分析物包括:例如,乙酰胆碱、淀粉酶、胆红素、胆固醇、绒毛膜促性腺素、肌酸激酶(例如,CK-MB)、肌氨酸、DNA、果糖胺、葡萄糖、谷氨酸盐、生长激素、荷尔蒙、酮、乳酸盐、过氧化物、前列腺特效抗原(prostate-specific antigen)、凝血素、RNA、促甲状腺激素、以及肌钙蛋白。还可以监测药物(例如,抗生素(例如,庆大霉素、万古霉素等)、洋地黄毒苷、地高辛、滥用的药物、茶碱、以及杀鼠灵)的浓度。 
被分析物监测系统100包括:传感器101、连接至传感器101的发射器单元102、和被配置为经由通信链路103与发射器单元102进行通信的主接收器单元104。主接收器单元104还可以被配置为将数据发射至数据处理终端105以对由主接收器单元104接收到的 数据进行评估。此外,一个实施例中的数据处理终端可以被配置为经由通信链路直接从发射器单元102接收数据,其中,通信链路可以可选地被配置用于进行双向通信。 
图1还示出了次接收器单元106,其可操作地连接至通信链路103,并被配置为接收由发射器单元102发射的数据。此外,如图所示,次接收器单元106被配置为与主接收器单元104以及与数据处理终端105进行通信。实际上,次接收器单元106可以被配置用于与主接收器单元104和数据处理终端105中的每一个都进行无线双向通信。如下进一步详细描述的,在本公开的一个实施例中,相比于主接收器单元104,次接收器单元106可以被配置为包括有限数量的功能和特征。如此,次接收器106可以基本被配置在更小的紧凑壳体中,或插入在诸如腕表的装置中。可替换地,次接收器单元106可以被配置为具有与主接收器单元104相同或基本相似的功能性,并且可以被配置为与连接支架(docking cradle)单元(用于临床更换、用于在夜间进行监测)和/或双向通信装置一起使用。 
在图1所示的被分析物监测系统100实施例中仅示出了一个传感器101、发射器单元102、通信链路103、和数据处理终端105。然而,本领域普通技术人员应该理解,被分析物监测系统100可以包括一个或多个传感器101、发射器单元102、通信链路103、和数据处理终端105。此外,在本公开的范围内,被分析物监测系统100可以是连续监测系统、或半连续、或离散监测系统。在多构件环境中,每个装置都被配置为由系统中每个其他装置唯一标识,从而轻易解决了被分析物监测系统100中各构件之间的通信冲突。 
在本公开的一个实施例中,传感器101物理上位于其被分析物水平正在被监测的用户身体中或身体上。传感器101可以被配置为对用户的被分析物水平进行连续采样,并将采样的被分析物水平转换成相应的数据信号,以由发射器单元102进行发射。在一个实施 例中,发射器单元102连接至传感器101,从而这两个装置都位于用户身体上,其中,被分析物传感器101的至少一部分经皮地位于用户皮层下。发射器单元102执行数据处理(诸如对数据信号进行滤波和编码,这些数据信号中的每一个均对应于用户的采样被分析物水平),以经由通信链路103发射至主接收器单元104。 
在一个实施例中,被分析物监测系统100被配置为从发射器单元102到主接收器单元104的单向RF通信路径。在这样的实施例中,在没有来自已经接收到了这些发射的采样数据信号的主接收器单元104的确认的情况下,发射器单元102对从传感器101接收到的采样数据信号进行发射。例如,发射器单元102可以被配置为在完成最初的接通电源程序后以固定速率(例如,以一分钟的间隔)对编码后的采样数据信号进行发射。同样,主接收器单元104可以被配置为以预定时间间隔检测这些发射的编码采样数据信号。可替换地,被分析物监测系统100可以被配置为具有发射器单元102和主接收器单元104之间的双向RF(或相反)通信。 
此外,在一个方面中,主接收器单元104可以包括两个部件。第一部件是模拟接口部件,其被配置为经由通信链路103与发射器单元102进行通信。在一个实施例中,该模拟接口部件可以包括用于接收来自发射器单元102的数据信号并对其进行放大的天线和RF接收器,随后,利用局部振荡器对这些信号进行解调并且通过带通滤波器对其进行滤波。主接收器单元104的第二部件是数据处理部件,其被配置为通过诸如执行数据解码、误差检测及校正、数据时钟产生、以及数据位恢复来对从发射器单元102接收到的数据信号进行处理。 
在操作过程中,一旦完成了接通电源程序,主接收器单元104就被配置为基于(例如)检测到的从发射器单元102接收到的数据信号的强度或预定的发射器标识信息来在其范围内检测发射器单 元102的存在。一旦成功与相应的发射器单元102进行了同步,主接收器单元104就被配置为开始从发射器单元102接收对应于用户的检测到的被分析物水平的数据信号。更具体地,一个实施例中的主接收器单元104被配置为经由通信链路103执行与相应的同步发射器单元102的同步跳时(time hopping),以得到用户的检测到的被分析物水平。 
再次参照图1,数据处理终端105可以包括个人计算机、诸如膝上电脑或手持设备(例如个人数据助理(PDA))等的便携式计算机等,其中的每一个都可以被配置用于经由有线或无线连接与接收器进行数据通信。此外,数据处理终端105还可以连接至数据网络(未示出)以存储、提取和更新对应于检测到的用户的被分析物水平的数据。 
在本公开的范围内,数据处理终端105可以包括注射设备(诸如胰岛素注射泵)等,其可以被配置向患者提供胰岛素,以及可以被配置为与接收器单元104进行通信以接收其中测量到的被分析物水平。可替换地,接收器单元104可以被配置为将注射设备集成于其中,从而接收器单元104被配置为向患者提供胰岛素治疗,例如,用于管理和修改基本档案,以及用于其中基于从发射器单元102接收到的检测到的被分析物水平来确定适当的推注以进行给药。 
此外,发射器单元102、主接收器单元104和数据处理终端105每个都可以被配置为进行双向无线通信,从而发射器单元102、主接收器单元104和数据处理终端105中的每一个都可以被配置为彼此经由无线通信链路103进行通信(即,彼此之间发送和接收数据)。更具体地,在一个实施例中,数据处理终端105可以被配置为经由通信链路106直接从发射器单元102接收数据,其中,通信链路106可以如上所述被配置为进行双向通信。 
在该实施例中,可以包括胰岛素泵的数据处理终端105可以被配置为接收来自发射器单元102的被分析物信号,从而,结合接收器103的功能,这些功能包括用于对患者的胰岛素治疗和被分析物监测进行管理的数据处理。在一个实施例中,通信链路103可以包括RF通信协议、红外通信协议、蓝牙使能通信协议、802.11x无线通信协议、或在避免潜在的数据冲突和干扰的同时将允许多个单元进行安全、无线通信(例如,按照HIPPA要求)的等效无线通信协议中的一个或多个。 
图2是根据本公开一个实施例的图1所示的数据监测和检测系统的发射器的框图。参照该图,一个实施例的发射器单元102包括被配置为与传感器101(图1)进行通信的模拟接口201、用户输入端202、以及温度测量部203,其中的每一个都可以可操作地连接至诸如中央处理单元(CPU)的发射器处理器204。 
图2还示出了发射器串行通信部205和RF发射器206,其中的每一个也都可操作地连接至发射器处理器204。此外,还在发射器单元102中设置有诸如电池的电源207,用于为发射器单元102提供所需的功率。此外,如从图中可以看到的,其中的时钟208被配置用于向发射器处理器204提供实时信息。 
如从图2可以看到的,传感器单元101(图1)配置有四个触点,其中的三个是电极-工作电极(W)210、保护触点(G)211、基准电极(R)212,以及计数器电极(C)213,其中的每一个都可操作地连接至发射器单元102的模拟接口201。在一个实施例中,工作电极(W)210、保护触点(G)211、基准电极(R)212、以及计数器电极(C)213中的每一个都可以使用利用激光或光刻法印刷或蚀刻的、或可替代的设置在衬底材料上的导电材料(例如,可以被印刷的碳、或可以被蚀刻的金属箔(例如,金))制成。 
在一个实施例中,建立从传感器101(图1)和/或制造和测试设备到发射器单元102的模拟接口201的单向输入路径,同时建立从用于进行能够发射的发射器单元102的RF发射器206到接收器单元104的单向输出。这样,图2示出了前述单向输入和输出之间经由专用链路209,而从模拟接口201到达串行通信部205、然后到达处理器204、再到RF发射器206的数据链路。这样,在一个实施例中,经由上述数据路径,发射器单元102被配置为将从传感器101(图1)接收到的经过处理和编码的数据信号经由通信链路103(图1)发射至主接收器单元104(图1)。此外,上述模拟接口201和RF发射器206之间的单向通信数据路径考虑了发射器单元102用于对完成制造工艺的操作以及用于为诊断和测试目的的直接通信的配置。 
如上所述,发射器处理器204被配置为在发射器单元102的操作期间向发射器单元102的各个部件发射控制信号。在一个实施例中,发射器处理器204还包括存储器(未示出),用于存储诸如用于发射器单元102的标识信息以及从传感器101接收到的数据信号。在发射器处理器204的控制下,所存储的信息可以被提取和处理,以发射至主接收器单元104。此外,电源207可以包括市场上可得到的电池。 
发射器单元102被配置为使电源部件207能够在以低功率(非操作)模式存储约十八个月后还能为发射器提供至少约三个月连续操作的电能。在一个实施例中,这可以通过发射器处理器204在非操作状态中以低功率模式操作来实现,例如,抽取不超过约1μA的电流。事实上,在一个实施例中,发射器单元102的制造工艺过程中的最后步骤可以将发射器单元102置于低功率、非操作状态(即,制造后睡眠模式)。在该方式中,发射器单元102的保存(shelf)寿命可以显著提高。此外,如图2所示,尽管电源单元207被示为 连接至处理器204,并且因此,处理器204被配置为为电源单元207提供控制,但是应该注意到,在本公开的范围内,电源单元207被配置为为图2所示的发射器单元102的每个构件都提供所需功率。 
回来参照图2,在一个实施例中,发射器单元102的电源部件207可以包括可再充电电池单元,其可以由独立的电源再充电单元(例如,设置在接收器单元104中)进行再充电,从而可以在更长的使用时间段中向发射器单元102供电。此外,在一个实施例中,发射器单元102可以被配置为在电源部件207中没有电池,在这种情况中,发射器单元102可以被配置为从外部电源(例如,电池)接收电能,这将在下面进一步详细进行描述。 
仍然再次参照图2,发射器单元102的温度检测部件203被配置用于监测传感器插入位置附近的皮肤温度。温度读数用于调整从模拟接口201得到的被分析物读数。发射器单元102的RF发射器206可以被配置为在315MHz到322MHz的频带内(例如,在美国)进行操作。此外,在一个实施例中,RF发射器206被配置为通过进行频移键控和曼彻斯特编码来对载波频率进行调制。在一个实施例中,数据传输率为19,200符号每秒,同时具有用于与主接收器单元104进行通信的最小传输范围。 
仍然再次参照图2,其还示出了连接至数据监测和管理系统100的发射器单元102中的保护电极(G)211和处理器204的泄漏检测电路214。根据本公开的一个实施例的泄漏检测电路214可以被配置用于检测传感器101中的漏电流(leakage current),以确定测量到的传感器数据是否是坏的,以及来自传感器101的测量数据是否正确。 
图3是根据本公开一个实施例的图1所示的数据监测和管理系统的接收器/监测器单元的框图。参照图3,主接收器单元104包括 血糖测试带接口301、RF接收器302、输入端303、温度检测部件304、以及时钟305,其中的每一个都可操作地连接至接收器处理器307。如从该图还可以看到的,主接收器单元104还包括可操作地连接至功率转换和监测部件308的电源306。此外,功率转换和监测部件308还连接至接收器处理器307。此外,还示出了接收器串行通信部件309、以及输出端310,其中的每一个都可操作地连接至接收器处理器307。 
在一个实施例中,测试带接口310包括用于接收葡萄糖测试带的人工插入的葡萄糖水平测试部,从而可以在主接收器单元104的输出端310上确定和显示测试带的葡萄糖水平。该葡萄糖的人工测试可以用于标定(calibrate)传感器101。RF接收器302被配置为经由通信链路103(图1)与发射器单元102的RF发射器206进行通信,以接收来自发射器单元102的编码数据信号,用于信号混频、解调以及其他的数据处理。主接收器单元104的输入端303被配置为允许用户根据需要将信息输入到主接收器单元104。在一个方面中,输入端303可以包括小键盘中的一个或多个键、触摸感应屏、或声控输入命令单元。温度检测部件304被配置为向接收器处理器307提供主接收器单元104的温度信息,同时,时钟305向接收器处理器307提供实时信息。 
图3所示的主接收器单元104的各构件中的每一个都由电源306供电,在一个实施例中,该电源包括电池。此外,功率转换和监测部件308被配置为监测由主接收器单元104中各构件所使用的功率,以进行有效的功率管理,以及在使主接收器单元104处于次最佳操作条件的功率使用的情况中对用户进行警告。这种次最佳操作条件的实例可以包括,例如,操作波动输出模式(如下所述)一段时间,从而在接通处理器307(从而,接通主接收器单元104)时使电源306基本耗尽。此外,功率转换和检测部件308还可以被 配置为包括诸如被配置为电池启动开关的场效应晶体管(FET)的反极性保护电路。 
主接收器单元104中的串行通信部件309被配置为提供从测试和/或制造设备开始的双向通信路径,用于主接收器单元104的初始化、测试、和配置。串行通信部件104还可以用于向计算机上载数据,诸如时间戳血糖数据。可以通过例如电缆、红外(IR)或RF链路建立与外部设备(未示出)的通信链路。主接收器单元104的输出端310被配置用于提供诸如显示信息的液晶显示器(LCD)的图形用户接口(GUI)。此外,输出端310还可以包括集成扬声器,用于输出可听得见的信号,以及提供手持电子装置(诸如当前可得到的移动电话)中共同发现的振动输出。在另一个实施例中,主接收器单元104还包括电子荧光灯,其被配置为向输出端310提供背光,以在黑暗的周围环境中输出看得见的显示。 
回来参照图3,在一个实施例中,主接收器单元104还可以包括存储部件,诸如作为处理器307的一部分的、或单独设置在主接收器单元104中的、可操作地连接至处理器307的可编程、非易失性存储设备。处理器307还被配置为执行曼彻斯特编码以及对经由通信链路103从发射器单元102接收到的编码数据信号进行误差检测和校正。 
在另一个实施例中,发射器单元102、主接收器单元104、次接收器单元105、或数据处理终端/注射部件105中的一个或多个可以被配置为通过通信链路无线地接收来自例如葡萄糖计的血糖值。在又一个实施例中,操控或使用被分析物监测系统100(图1)的用户或患者可以使用结合在发射器单元102、主接收器单元104、次接收器单元105、或数据处理终端/注射部件105中的一个或多个中的用户接口(例如,键盘、小键盘等)手动输入血糖值。 
在2001年1月16日发表的题为“Analyte Monitoring Device andMethod of Use”的第6,175,752号美国专利,以及在2003年12月26日提交的题为“Continuous Glucose Monitoring System and Methodof Use”的第10/745,878号申请中提供了连续被分析物监测系统、其各部件的其他详细描述(包括发射器的功能描述),上述专利及申请都已转让给加利福尼亚阿拉米达的Abbott Diabetes Care公司。 
图4A至图4B分别示出了根据本公开一个实施例的被分析物传感器的透视图和截面图。参照图4A,传感器400的透视图,其主要部分位于皮肤表面410上方,插入端430穿透皮肤并进入皮下空间420中,与诸如间质液的用户生物流体接触。工作电极401、基准电极402、以及计数器电极403的接触部分都可以在位于皮肤表面410上方的传感器400的部分上看到。工作电极401、基准电极402、以及计数器电极403可以在插入端403的端部看到。 
现在参照图4B,示出了一个实施例中的传感器400的截面图。具体地,可以看到传感器400的各电极,并以堆叠或分层配置或构造提供衬底和介电层。例如,如图4B所示,在一个方面中,传感器400(诸如图1的传感器单元101),包括衬底层404、和第一导电层401(诸如设置在衬底层404的至少一部分上的碳线路),该导电层还可以包括工作电极。还示出了设置在第一导电层401的至少一部分上的感测层408。 
回来参照图4B,诸如第一介电层405的第一绝缘层被设置或被堆叠在第一导电层401的至少一部分上,以及进一步的,诸如另一碳线路的第二导电层409可以被设置或堆叠在第一绝缘层(或介电层)405的至少一部分的顶部上。如图4B所示,第二导电层409可以包括基准电极,并且在一个方面中,可以包括银/氯化银(Ag/AgCl)层。 
再次参照图4B,在一个实施例中,诸如介电层的第二绝缘层406可以被设置或堆叠在第二导电层409的至少一部分上。此外,可以包括碳线路以及可以包括计数器电极403的第三导电层403在一个实施例中可以被设置或堆叠在第二绝缘层406的至少一部分上。最后,第三绝缘层407被设置或堆叠在第三导电层403的至少一部分上。以此方式,传感器400可以被配置在堆叠或分层结构和配置中,从而每个导电层的至少一部分都被相应的绝缘层(例如,介电层)分隔开。 
此外,在本公开的范围内,电极401、402、403中的一些或全部可以以上述堆叠形式被设置在衬底404的同一侧上,或可替换地,可以以共面形式设置,从而将每个电极都设置在衬底404上的同一平面上,然而,在各导电层/电极之间具有介电材料或绝缘材料。此外,在本公开的又一个方面中,一个或多个导电层(诸如电极401、402、403)可以被设置在衬底404的相反侧上。 
图5是示出本公开一个实施例中的实时早期信号衰减(ESA)的框图。参照图5,在一个实施例中,全部灵敏度下降检测器500包括第一模块510,被配置用于基于被分析物传感器测量结果窗口进行灵敏度下降概率的估计,以确定是否需要血糖水平的指棒(finger stick)测量。基于由第一模块510进行的灵敏度下降的估计概率,当确定了需要血糖水平的指棒测量时,如图5所示,在本公开的一个方面中,第二模块520使用测量到的血糖值来验证或确认或拒绝由第一模块510进行的灵敏度下降的估计概率。在一个方面中,第二模块520可以被配置用于基于统计测定来确认或拒绝第一模块510的结果(例如,灵敏度下降的估计概率)。 
即,在一个方面中,图5的灵敏度下降检测器500的第一模块510可以被配置为基于传感器值窗口的分析(例如,来自被分析物传感器的持续预定时间段的电流信号)来估计被分析物传感器灵敏 度下降的概率。更具体地,第一模块510可以被配置为基于以下估计传感器灵敏度下降的概率:基于传感器电流信号特性的滑动窗口提取器,基于确定的或提取的传感器电流信号特性的灵明度下降概率的基于模型的估计,和/或将估计的概率和预定的阈值τ进行比较。 
回来参照图5,第一模块510的逻辑估计器511在一个实施例中可以被配置为提取或抽取传感器电流信号特性的滑动窗口,以及基于传感器电流信号特性进行灵敏度下降概率的估计,以及将灵敏度下降的估计概率与预定阈值τ进行比较,来确定估计的灵敏度下降的验证是否是所期望的,或是否可以基于估计的灵敏度下降概率确认未检测到ESA或夜间下降。 
再次参照图5,如所示,当确定了估计的灵敏度下降概率的验证是所期望的时候(基于例如,当估计的概率超过在第一模块510中确定的预定阈值τ时),在一个实施例中的灵敏度下降检测器500中的第二模块520的假设分析模块521接收来自血糖测量装置(诸如包括 、Freestyle  
Figure G2007800482892D00152
、FreeStyle  
Figure G2007800482892D00153
或从美国加利福尼亚阿拉米达的Abbott Diabetes Care公司可得到的Precision XtraTM)的毛细血管血测量结果。在一个方面中,基于接收到的毛细血管血糖测量结果和被分析物传感器电流特性或值,估计的传感器灵明度下降概率可以被确认或被拒绝,从而确认存在ESA或夜间下降(在相应的数据点与夜间传感器电流值相关的事件),或可替换地,确认不存在ESA或夜间下降。 
在所述方法中,在本公开的一个方面中,提供了一种基于传感器电流信号特性的实时检测程序,其中,检测器500(图5)包括:第一模块510,在一个实施例中其被配置为执行传感器灵敏度下降概率的检测和估计;以及第二模块520,在一个方面中其被配置为基于由第一模块510确定的概率估计来验证ESA或夜间下降的存在 或不存在。因此,在本公开的一个方面中,ESA事件或夜间下降或丢失可能被准确地检测到,同时最小化误报警或错误否定的可能。 
再次参照图5,在一个实施例中的传感器灵敏度估计器500的第一模块510中使用滑动窗口处理来在实时决定处理的期望和对于传感器电流特性估计冗余的需要之间进行调和。图9中示出了根据本公开一个实施例的滑动窗口处理的一个实例。 
例如,在一个方面中,在由第一模块510进行处理期间,在决定处理的每次重复中,都选择一个时间窗口,并基于在所选时间窗口期间确定的传感器电流信号,确定一个或多个预定的传感器特定。利用非限定性实例,该一个或多个预定传感器特性可以包括平均电流信号电平、电流信号方差、电流信号的平均斜率、以及平均的传感器寿命(或自从对被分析物传感器进行插入或经皮定位所经历的时间)。 
此后,所选的时间窗口滑动固定的分钟数量以进行下一次重复。在一个方面中,时间窗口的宽度和持续时间以及递增步长可以预定或建立为60分钟,因此,产生非交叠时间窗口以最小化各决定之间的潜在相关性。在本公开的范围内,可以构想出其他的方法,例如,其中,滑动时间窗口可以包括约30分钟的持续时间以及一分钟的递增步长。 
在一个方面中,下面的表达式可以用于确定上述传感器特性估计,例如,传感器信号平均值、平均斜率以及方差值: 
其中,X是具有1s列和数据指数的矩阵,以及Y是电流值的列向量, 
其中,t是时间窗口中的第一可变数据点的指数。 
回来参照图5,在进行了上述的传感器特性的估计和确定之后,就产生了对应于传感器电流信号的时间窗口的四维特征向量。在一个方面中,所产生的特征向量和罗吉斯回归方法(logistic regressionapproach)可以用于估计传感器在每个预定时间窗口期间经历和体验的早期信号衰减(ESA)。在一个方面中,用于确定或估计ESA存在概率的Pr[ESA]的罗吉斯回归方法可以如下表示: 
Pr [ ESA | x n ] = exp < &beta; , x n > 1 + exp < &beta; , x n >
Figure G2007800482892D00173
在一个方面中,系数向量β在传感器信号衰减估计的效率中扮演重要角色。即,在一个实施例中,预定数量的传感器插入可以用于经验地确定或估计模型系数。更具体地,在一个方面中,可以进行引导估计程序来为模型系数增加鲁棒性。例如,可以应用广义线性模型适合方法(generalized linear model fit approach)到预定时间段来确定系数向量β。基于预定数量的重复,可以确定每个系数的经验概率分布函数,例如如图10所示,其中,每个所选系数都对应于相关分布的模式。 
在确定了一个或多个传感器电流特性或参数,以及在确定了对应的系数之后,在一个实施例中,ESA存在概率Pr[ESA]基于下式进行估计: 
应该注意,在本公开的范围内,由上述所示函数描述的ESA存在概率Pr[ESA]的估计可以根据设计或相关的潜在参数来修改,这些参数诸如,每日时间信息、或传感器电流信号的去趋势方差(detrended variance)。 
仍然再次参照图5,在基于上述估计确定了ESA存在的概率之后,将估计的概率与预先选择阈值水平进行比较,并基于该比较,可以提示对毛细血管血糖测量结果的请求。在一个方面中,预定的阈值水平可以包括用于与估计的ESA存在的概率进行比较的0.416。可替换地,在本公开的范围内,预定阈值水平可以在约0.3至0.6的范围内变化。 
如上所述,在本公开的一个方面中,传感器灵敏度估计器500的第一模块510(图5)被配置为基于与被分析物传感器相关的特性或参数以及传感器电流信号对ESA存在的概率进行估计。在一个实施例中,传感器灵敏度估计器500的第二模块520(图5)可以被配置为基于毛细血管血糖测量结果进行附加的处理以提供本分析物传感器的早期传感器衰减(ESA)的基本实时估计。即,由于ESA是通过灵敏度的下降或减少(即,传感器电流信号与血糖的比率)来限定的,因此,灵敏度在ESA发生期间的分布大体低于正常运行情况期间的分布。此外,基于传感器电流电平和血糖测量结果之间的非线性关系,利用血糖测量结果的ESA存在概率估计可以使 用仓(例如,分类)构建方法以及额定的灵敏度比率的经验分布函数来确定。 
更具体地,在本公开的一个方面中,可以将即时灵敏度(IS)定义为被分析物传感器的实际电流值和实际血糖值在给定时间点处的比(例如由下式(a)定义)。然而,由于信号中的噪声,例如,尤其是在独立的测量结果(诸如单个血糖测量结果)的情况中,可以通过确定指棒血糖确定时间附近的平均传感器电流信号电平来近似即时灵敏度(IS),例如,通过下式(b)来确定: 
Figure G2007800482892D00191
假设每个被分析物传感器都具有不同的灵敏度,因此即时灵敏度(IS)是高度依赖传感器的,即时灵敏度(IS)的绝对值可以不提供ESA存在的可靠指示。另一方面,在制造过程中,每个被分析物传感器都与标称(nominal)灵敏度值相关。因此,即时灵敏度与传感器标称灵敏度的比将造成更加依赖于传感器的、可靠的ESA检测机制。因此,时间t处的灵敏度比率Rs(t)可以如下在一个方面中进行定义: 
参照以上描述,一个实施例中的血糖仓/分类构建方法可以包括定义血糖测量结果比例的变换,其矫正测量到的和估计的血糖值之间的差异。即,在一个方面中,所定义的变换方法对应于或与血糖 水平的典型分布有关。例如,定义不同仓/分类的变换方法可以基于下式进行确定: 
r=1.509×e1.084×log(log(BG)-5.381)其中,BG单位是mg/dl  (5) 
其中,可以定义下列的成比例的葡萄糖仓(bin): 
1.r<-2,严重低血糖症 
2.-2≤r<-1,中度低血糖症 
3.-1≤r<0,低度血糖正常 
4.0≤r<1,高度血糖正常 
5.1≤r<2,中度高血糖症 
6.2≤r,严重高血糖症 
一旦确定了仓/分类以用于ESA存在概率的估计,在一个方面中,核密度估计(例如,使用高斯核,24)可以用于对每个上述的仓/分类中的灵敏度比率分布进行估计。在一个方面中,图11示出了灵敏度比率Rs中的分布估计,其中,对于每个仓/分类(包括,例如,严重低血糖症(仓1)、中度低血糖症(仓2)、低度血糖正常(仓3)、高度血糖正常(仓4)、低度高血糖症(仓5)、以及严重高血糖症(仓6)),每条曲线都示出了检测出ESA存在的相关分布。 
再次参照以上描述,基于对每个仓/分类中的灵敏度比率Rs的估计分布的概率密度函数进行的估计,在一个方面中,可以实现基 于贝叶斯法则(Bayes’law)的非参数假设检验方法。例如,在本公开的一个方面中,根据贝叶斯法则,已知灵敏度比率和血糖仓/分类的所估计的ESA存在概率可以基于下面的表达式进行分解: 
Figure G2007800482892D00211
其中,πa是等级a中各事件的比例, 
Figure G2007800482892D00212
是对于等级a的仓/分类i中的Rs的之前估计的概率密度函数。 
此外,为了最小化全部的误差概率,可以应用下面的决定规则:传感器是ESA,如果 
&DoubleRightArrow; f ^ ESA , i ( &rho; ) f ^ ESA &OverBar; , i ( &rho; ) > &pi; ESA &OverBar; &pi; ESA ,
假设πESA=πESA=0.5 
&DoubleRightArrow; f ^ ESA , i ( &rho; ) f ^ ESA &OverBar; , i ( &rho; ) > 1 &DoubleRightArrow; f ^ ESA , i ( &rho; ) > f ^ ESA &OverBar; , i ( &rho; )
因此,基于以上,在一个实施例中,当毛细血管血糖测量结果处于仓/分类i中时,当灵敏度比率Rs小于对应的所定义的阈值水平ti时,图5所示的第二模块520中的假设分析模块(521)可以被配置为基于毛细血管血糖水平测量结果读数对给定的被分析物传感器来验证/确认ESA的存在。例如,给定上述的六个血糖仓/分类 (仓1至仓6),各阈值水平ti为:t1=1.138,t2=0.853,t3=0.783,t4=0.784,t5=0.829,以及t6=0.797。 
在此方式中,在本公开的一个方面中,该方法、系统和计算机程序产品提供(但不限于)对连续血糖监测系统中的灵敏度下降的早期检测。灵敏度下降可以在例如传感器寿命的第一个24小时内发现,以及同时可以通过频率标定或传感器遮蔽(masking)来最小化潜在的不利影响,这种灵敏度下降在传感器数据精度方面具有临床显著效果,以及从而最小化使用传感器对患者的潜在危险。因此,在一个方面中,提供了用于估计或确定ESA存在的概率的方法、系统和计算机程序产品,它们基于传感器电流信号特性进行估计或确定,并且之后执行确认或验证程序以确定基于传感器电流信号特性进行估计的灵敏度下降概率是否对应于传感器中相应灵敏度下降的实时发生。 
因此,具体地,可以改善临界低血糖症范围中的传感器精度,在传感器寿命的早期阶段可以避免多重标定和/或传感器遮蔽,并且进一步地,可以避免会导致未检测到的低血糖症事件的灵敏度下降发生期间的传感器标定。 
图6是示出根据本公开一个实施例的整个ESA检测程序的流程图。参照图6,在本公开的一个实施例中,提取或收集预定数量的传感器数据(610),之后,确定对灵敏度下降确定的概率估计是否合适(620)。在一个方面中,下面的一个或多个参数可以用于确定灵敏度下降的概率估计的确定是否合适:足够的与被分析物传感器相关的数据点的存在或收集、相对于何时将被分析物传感器插入或被皮下定位的概率估计的时刻、从对灵敏度下降的概率估计的最近确定的时间段。 
如果确定对灵敏度下降确定的概率估计不适当(620),则图6所示的程序返回至收集附加传感器数据点。另一方面,如果确定对灵敏度下降确定的概率估计适当,则执行对灵敏度下降确定的概率估计(630)。之后,基于确定的灵敏度下降的概率估计,确定是否存在ESA(640)。 
即,基于例如由灵敏度下降检测器500的第一模块520(图5)进行的分析,如果未检测到ESA,则程序返回到收集和/或提取附加地传感器电流数据(640)。另一方面,如果基于上述分析检测到了ESA(640),则请求毛细血管血测量结果(例如,通过提示用户进行能够指棒血糖测试并输入血糖值)(650)。之后,图6所示的程序由假设分析模块521(图5)执行的ESA存在或不存在的确认的程序。 
再次参照图6,如果基于利用毛细血管血测量结果的分析确定不存在ESA,则程序再次返回至数据收集/提取模块(610)。另一方面,如果确定了存在ESA,则在一个方面中,会产生警告或通知并将其提供给用户(680)以警告用户。 
图7是根据本公开一个实施例的结合图5所示的模块1描述的传感器电流异常的实时检测的流程图。参照图7,在一个实施例中,用于定义的时间段的被分析物传感器数据被提取或选择。利用被分析物传感器数据,执行一个或多个数据处理以确定传感器信号特性,包括:例如,平均电流信号、最小二乘斜率(the least squaresslope)、标准偏差、自从被分析物传感器插入/定位以来平均经历的时间(或平均传感器寿命)、关于最小二乘斜率的方差(710)。 
参照图7,可以提取基于被分析物传感器数据的预定系数(720),并将其施加至被分析物传感器信号以确定或估计ESA存在概率(730)。此外,图7中还示出了预定阈值(740),在一个实施 例中,该阈值可以与确定的所估计的ESA存在概率进行比较(750)。在一个方面中,对于宣称的此条件,该预定阈值可以被确定为最小的ESA存在概率,并且该阈值可以在错误报警(假阳性,其中该阈值可以被容易地超过)与漏检测(假阴性,其中该阈值难以被超过)之间进行折衷。 
再次参照图7,如果确定了估计的ESA存在概率没有超过预定阈值(750),则确定不存在ESA-即,未检测到传感器电流信号衰减(760)。另一方面,如果确定了估计的ESA存在概率超过了预定阈值,则确定存在ESA-即,检测到了传感器电流信号衰减(770)。在确定ESA存在或不存在的任意一种情况中,该确定都被通信或提供给分析的后续阶段(780)用于进一步的处理。 
图8是示出根据本公开一个实施例的图5中的模块2的验证程序以确认或拒绝模块1的输出的流程图。参照图8,利用连续的葡萄糖数据(801)和毛细血管血糖测量结果(802),执行与血糖测量结果大约同一时间或近乎同时的一个或多个连续血糖传感器电流数据点的平均函数(803)。在传感器电流数据点是单个值的情况中,平均函数将得到该值本身-因此进行平均的程序是不必要的。 
可替换地,在传感器数据包括多于一个数据点的情况中,例如,在以血糖数据点的时间为中心的周围的11个数据点的情况中,执行平均函数得到了与这多个数据点都相关的一个平均值。此后,如图8所示,基于计算出来的上述传感器数据点和毛细血管血糖测量结果的平均值确定灵敏度值(S)(805)。例如,与传感器相关的灵敏度值(S)可以被确定为确定的平均传感器数据点值与血糖值的比。 
仍然参照图8,提取通常在传感器制造时确定的标准传感器灵敏度(807),并将其应用于所确定的传感器灵敏度值(S)以得到标准化的灵敏度比率Rs(808)。 
回来参照图8,基于(例如,在一个方面中,通过应用上面的等式(5)所述的函数)测量到或接收到的毛细血管血糖测量结果(802),确定或计算出上述相应的血糖仓(804)。此后,基于计算出来的或确定的血糖仓来确定相应的ESA测试阈值(806)。例如,如上所述,每个血糖仓(仓1至仓6)都与相应的阈值水平t相关,而在一个方面中,该阈值水平可以通过在先的分析或联系确定。 
仍然再次参照图8,利用标准化的灵敏度比率(808)和计算出来的仓(806),在标准化的灵敏度比率和确定或计算出来的仓之间进行比较(809)。例如,在该比较确定了标准化灵敏度比率(S)超过计算出来的仓t的情况中,确定不存在早期信号衰减(ESA)(811)。另一方面,当标准化灵敏度比率(S)被确定为小于计算出来的仓t时,则确定传感器信号中存在ESA。 
图9示出了根据本公开一个实施例的基于图5中的模块1的滑动窗口处理的实时电流信号特性评估方法。 
图10示出了根据本公开一个实施例的用于图5的模块1的系数的引导估计。参照这些附图,在一个方面中,所执行的用于为模型系数增加鲁棒性的引导估计程序可以包括:被应用至预定时间段以确定系数向量β的广义线性模型适合。基于预定数量的重复,可以确定每个系数的经验概率分布函数,例如,如图10所示,其中,每个所选系数都对应于相关分布的模式。 
图11示出了根据本公开一个实施例的图5的模块2的标准化灵敏度密度的高斯核估计。参照图11,如上所述与图5相结合,在 一个方面中,核密度估计(例如,使用高斯核,24)可以用于对上述每个仓/分类中的灵敏度比率Rs的分布进行估计。在一个方面中,图11示出了灵敏度比率Rs中的分布估计,其中,对于每个仓/分类(包括,例如,严重低血糖症(仓1)、中度低血糖症(仓2)、低度血糖正常(仓3)、高度血糖正常(仓4)、低度高血糖症(仓5)、以及高度高血糖症(仓6)),与未检测到ESA存在的分布相比,相应的曲线示出了检测到ESA存在的相关分布。 
图12示出了根据本公开一个实施例的错误报警(假阳性)比率和灵敏度下降检测率的比较。即,图12表示ESA检测率和错误报警率之间的关系。在一个方面中,曲线1210示出了灵敏度下降检测器500中的第一模块510(图5)基于罗吉斯回归分类器的输出结果,而曲线1220示出了灵敏度下降检测器500中的第一模块510和第二模块520(图5)的组合输出,该输出在ESA存在概率超过预定阈值水平的情况中,基于(例如)提示血糖测量结果的罗吉斯规则分类器。在一个实施例中,基于确定ESA存在概率的0.416的阈值水平,ESA检测率为约87.5%,而错误报警率为约6.5%。 
在上述方式中,根据本公开的各实施例,提供了ESA或被分析物传感器灵敏度夜间下降的实时检测。例如,具有低于普通灵敏度的灵敏度的被分析物传感器可以报告低于实际值的血糖值,从而潜在地低估高血糖症,从而触发错误的低血糖症报警。此外,由于使用了基准血糖值(例如,标定点)来估计传感器电流电平和血糖水平之间的关系,因此,如果在低灵敏度期间进行这种标定,一旦该期间结束,则所有的血糖测量结果都将正偏,从而潜在地掩盖了低血糖症事件。因此,可以实时监测和检测被分析物传感器的电流信号输出和相应的血糖水平之间关系出现的误差,从而可以为患者提供采取矫正行为的能力。 
实际上,使用监测装置(诸如提供血糖水平波动的时间因次(dimension)的被分析物监测装置)对病人的血糖水平变化进行实时检测提供了紧密控制多糖变化以控制糖尿病患者的状况的能力。更具体地,根据本公开的各个实施例,被分析物监测系统可以被配置为实时提供关于低葡萄糖水平的警告,尤其是在患者可能不被怀疑为低血糖症或迫近高血糖症的时候,从而在高血糖症发作期间提供了帮助患者避免危及生命的状况以及进行自救的能力。 
因此,在本公开的一个方面中,低传感器灵敏度事件的检测包括:第一模块,其可以被配置为基于被分析物传感器电流信号特性执行实时监测算法;以及第二模块,其可以被配置为基于单个的血糖测量结果进行统计分析以确认或拒绝由第一模块进行的传感器灵敏度下降的最初检测。在此方式中,在本公开的一个方面中,可以以最少的错误报警来提供ESA事件或传感器电流信号电平的夜间下降的精确检测。 
因此,一个方面中的计算机实现的方法包括:接收多个被分析物传感器相关信号;确定与接收到的多个被分析物传感器相关信号相关的信号衰减概率;当确定的概率超过预定阈值水平时验证信号衰减的存在;以及产生与信号衰减存在的验证相关的第一输出信号。 
此外,确定信号衰减概率可以包括:确定与接收到的多个被分析物传感器相关信号相关的一个或多个特性;以及将预定的系数应用于这多个被分析物传感器相关信号。 
另一方面,所确定的一个或多个特性可以包括:与被分析物传感器相关信号相关的一个或多个平均值、与被分析物传感器相关信号相关的最小二乘斜率、与被分析物传感器相关信号相关的标准偏 差、从将被分析物传感器定位开始平均所经历的时间、或关于与被分析物传感器相关信号相关的最小二乘斜率的变化。 
此外,在另一个方面中,预定阈值可以是用户定义的或由系统专家定义的。 
在再一个方面中,当确定的概率没有超过预定阈值水平时,该方法还可以包括:产生与不存在信号衰减条件相关的第二输出信号。 
此外,在再一个方面中,验证信号衰减的存在可以包括:选择信号衰减阈值水平;确定与被分析物传感器相关信号相关的灵敏度水平;以及至少部分地基于所确定的灵敏度水平与所选择的信号衰减阈值水平的比较确认信号衰减的存在,其中,信号衰减阈值水平可以与血糖测量结果相关。 
此外,在另一个方面中,血糖测量结果可以包括毛细血管血糖采样。 
在再一个方面中,与被分析物传感器相关信号相关的灵敏度水平可以包括:与被分析物传感器相关信号相关的额定灵敏度和与被分析物传感器相关信号相关的灵敏度值的比率,其中,灵敏度值可以被确定为与被分析物传感器相关信号的平均值和血糖测量结果的比率。 
此外,在另一个方面中,确认信号衰减的存在可以包括:确定灵敏度水平小于所选信号衰减阈值水平,而所选信号衰减阈值水平可以通过系统专家确定。 
根据本公开另一个方面的设备包括:数据存储单元;以及处理单元,其可操作地连接至数据存储单元,其被配置为接收多个被分析物传感器相关信号、确定与接收到的多个被分析物传感器相关信号相关的信号衰减概率、在确定的概率超过预定的阈值水平时验证信号衰减的存在、以及产生与信号衰减的验证相关的第一输出信号。 
可以被配置为确定信号衰减概率的处理单元可以被配置为确定与接收到的多个被分析物传感器相关信号相关的一个或多个特性,以及将预定系数应用于这多个被分析物传感器相关信号。 
所确定的一个或多个特性可以包括与被分析物传感器相关信号相关的一个或多个平均值、与被分析物传感器相关信号相关的最小二乘斜率、与被分析物传感器相关信号相关的标准偏差、从将被分析物传感器定位开始平均所经历的时间、或关于与被分析物传感器相关信号相关的最小二乘斜率的变化,其中,预定阈值水平可以是用户定义的或由系统专家定义的。 
当所确定的概率没有超过预定的阈值水平时,处理单元可以被进一步配置为产生与不存在信号衰减条件相关的第二输出信号。 
在再一个方面中,处理单元可以被进一步配置为选择信号衰减阈值水平、确定与被分析物传感器相关信号相关的灵敏度水平、以及至少部分地基于所确定的灵敏度水平和所选择的信号衰减阈值水平的比较来确认信号衰减的存在。 
信号衰减阈值水平可以与血糖测量结果相关。 
血糖测量结果可以包括毛细血管血糖采样。 
与被分析物传感器相关信号相关的灵敏度水平可以包括:与被分析物传感器相关信号相关的额定灵敏度和与被分析物传感器相关信号相关的灵敏度值的比率,其中,灵敏度值可以被确定为与被分析物传感器相关信号的平均值和血糖测量结果的比率。 
处理单元还可以被配置为确定灵敏度水平小于所选信号衰减阈值水平,而所选信号衰减阈值水平可以通过系统专家确定。 
在再一个方面中,该设备可以包括可操作地连接至处理单元以显示第一输出信号的用户输出单元。 
在本公开再一个方面中,用于检测葡萄糖传感器中的信号衰减的系统包括:被分析物传感器,用于通过对象皮层进行经皮定位;数据处理装置,可操作地连接至被分析物传感器以周期性地接收与被分析物传感器相关的信号,该数据处理装置被配置为确定早期信号衰减(ESA)的概率,以及基于一个或多个预定标准验证早期信号衰减的存在。 
数据处理装置可以包括:用户接口,用于输出与早期信号衰减相关的一个或多个信号,其中,早期信号衰减与被分析物传感器相关。 
在不背离本公开精神和范围的条件下,本公开的结构和操作方法中的各种其他改进和变化对本领域技术人员来说是显而易见的。尽管已经结合具体的优选实施例描述了本公开,但是应该理解,所要求保护的本公开不应被过度地限于这些具体实施例。旨在于由下列权利要求限定本公开的范围,以及从而覆盖这些权利要求及其等同物范围内的结构和方法。 

Claims (23)

1.一种用于检测被分析物传感器中的信号衰减的计算机实现的方法,包括:
在第一预定时间窗口期间,接收第一多个被分析物传感器相关信号;
确定与接收到的所述第一多个被分析物传感器相关信号相关联的信号衰减的概率;
当确定的所述概率没有超过预定阈值水平时,在第二预定时间窗口期间,接收第二多个被分析物传感器相关信号,其中所述第二预定时间窗口与所述第一预定时间窗口偏移预定的偏移时间;
当确定的所述概率超过所述预定阈值水平时,验证所述第一多个被分析物传感器相关信号中信号衰减的存在;以及
产生与所述信号衰减存在的验证相关联的第一输出信号。
2.根据权利要求1所述的方法,其中,确定所述信号衰减的概率包括:
确定与所述第一多个被分析物传感器相关信号相关联的一个或多个特性;以及
将预定的系数应用于所述第一多个被分析物传感器相关信号。
3.根据权利要求2所述的方法,其中,确定的所述一个或多个特性包括:与所述第一多个被分析物传感器相关信号相关联的一个或多个平均值、与所述第一多个被分析物传感器相关信号相关联的最小二乘斜率、与所述第一多个被分析物传感器相关信号相关联的标准偏差、从设置所述被分析物传感器开始的平均经历时间、或关于与所述第一多个被分析物传感器相关信号相关联的最小二乘斜率的方差。
4.根据权利要求1所述的方法,其中,所述预定阈值水平是用户定义的。
5.根据权利要求1所述的方法,其中,当确定的所述概率没有超过所述预定阈值水平时,产生与不存在信号衰减条件相关联的第二输出信号。
6.根据权利要求1所述的方法,其中,所述验证信号衰减的存在包括:
选择信号衰减阈值水平;
确定与所述第一多个被分析物传感器相关信号相关联的灵敏度水平;以及
至少部分地基于确定的所述灵敏度水平与选择的所述信号衰减阈值水平的比较来确认所述信号衰减的存在。
7.根据权利要求6所述的方法,其中,所述信号衰减阈值水平与血糖测量结果相关联。
8.根据权利要求6所述的方法,其中,与所述被分析物传感器相关信号相关联的灵敏度水平包括:与所述被分析物传感器相关信号相关联的标称灵敏度和与所述被分析物传感器相关信号相关联的灵敏度值的比率。
9.根据权利要求8所述的方法,其中,将所述灵敏度值确定为所述第一多个被分析物传感器相关信号的平均值与血糖测量结果的比率。
10.根据权利要求6所述的方法,其中,确认所述信号衰减的存在包括:确定所述灵敏度水平小于选择的所述信号衰减阈值水平。
11.一种用于检测被分析物传感器中的信号衰减的设备,包括:
被分析物传感器,用于通过对象的皮层进行经皮设置;
数据存储单元;以及
处理单元,可操作地连接至所述数据存储单元,所述处理单元被配置为:在第一预定时间窗口期间接收第一多个被分析物传感器相关信号;确定与接收到的所述第一多个被分析物传感器相关信号相关联的信号衰减的概率;当确定的所述概率没有超过预定阈值水平时,在第二预定时间窗口期间接收第二多个被分析物传感器相关信号,其中所述第二预定时间窗口与所述第一预定时间窗口偏移预定的偏移时间;在确定的所述概率超过所述预定阈值水平时验证所述第一多个被分析物传感器相关信号中信号衰减的存在;以及产生与信号衰减存在的所述验证相关联的第一输出信号。
12.根据权利要求11所述设备,其中,被配置为确定所述信号衰减的概率的处理单元用于确定与所述第一多个被分析物传感器相关信号相关联的一个或多个特性,以及将预定的系数应用于所述第一多个被分析物传感器相关信号。
13.根据权利要求12所述的设备,其中,确定的所述一个或多个特性包括:与所述第一多个被分析物传感器相关信号相关联的一个或多个平均值、与所述第一多个被分析物传感器相关信号相关联的最小二乘斜率、与所述第一多个被分析物传感器相关信号相关联的标准偏差、从设置所述被分析物传感器开始的平均经历时间、或关于与所述第一多个被分析物传感器相关信号相关联的最小二乘斜率的方差。
14.根据权利要求11所述的设备,其中,所述预定阈值水平是用户定义的。
15.根据权利要求11所述的设备,其中,当确定的所述概率没有超过所述预定阈值水平时,所述处理单元被进一步配置为产生与不存在信号衰减条件相关联的第二输出信号。
16.根据权利要求11所述的设备,其中,所述处理单元被进一步配置为选择信号衰减阈值水平、确定与所述第一多个被分析物传感器相关信号相关联的灵敏度水平、以及至少部分地基于确定的所述灵敏度水平与选择的所述信号衰减阈值水平的比较来确认所述信号衰减的存在。
17.根据权利要求16所述的设备,其中,所述信号衰减阈值水平与血糖测量结果相关联。
18.根据权利要求16所述的设备,其中,与所述被分析物传感器相关信号相关联的灵敏度水平包括:与所述被分析物传感器相关信号相关联的标称灵敏度和与所述被分析物传感器相关信号相关联的灵敏度值的比率。
19.根据权利要求18所述的设备,其中,将所述灵敏度值确定为所述第一多个被分析物传感器相关信号的平均值与血糖测量结果的比率。
20.根据权利要求16所述的设备,其中,所述处理单元被进一步配置为确定所述灵敏度水平小于选择的所述信号衰减阈值水平。
21.根据权利要求11所述的设备,包括用来连接至所述处理单元以显示所述第一输出信号的用户输出单元。
22.一种用于检测被分析物传感器中的信号衰减的系统,包括:
被分析物传感器,用于通过对象的皮层进行经皮设置;以及
数据处理装置,用来操作性地连接至所述被分析物传感器,被配置为:在第一预定时间窗口期间接收第一多个被分析物传感器相关信号;确定与接收到的所述第一多个被分析物传感器相关信号相关联的信号衰减的概率;当确定的所述概率没有超过预定阈值水平时,在第二预定时间窗口期间接收第二多个被分析物传感器相关信号,其中所述第二预定时间窗口与所述第一预定时间窗口偏移预定的偏移时间;在确定的所述概率超过所述预定阈值水平时验证所述第一多个被分析物传感器相关信号中信号衰减的存在;以及产生与信号衰减存在的所述验证相关联的第一输出信号。
23.根据权利要求22所述的系统,其中,所述数据处理装置包括:用户接口,用于输出与所述信号衰减的存在或不存在相关联的一个或多个信号,其中,所述信号衰减与所述被分析物传感器相关。
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