CN107847154B - 不一致监测 - Google Patents
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Abstract
本文描述了用于心脏监测的系统、设备和方法。特别地,本文描述的系统、设备和方法可以用于方便地感测个体中的间歇性心律失常的存在。本文描述的系统、设备和方法可以被进一步配置成感测心电图。
Description
相关申请的交叉引用
本申请要求提交于2015年5月13日美国临时申请No.62/161,092的权益,该美国临时申请以引用方式并入本文。
背景技术
不规律心跳和心律失常与患者的显著发病率和死亡率有关。心律失常可能连续发生,或者可能间歇发生。心律失常的类型包括心房纤维性颤动和室上性心动过速。非侵入性心脏监测可用于诊断心律失常。
发明内容
本文描述了用于心脏监测的系统、设备和方法。本文描述的用于心脏监测的系统、设备和方法可以包括便携式计算设备,诸如智能电话、智能手表、膝上型电脑和平板电脑。使用本文描述的系统、设备和方法进行的心脏监测可用于预测或识别心律失常的发生。
心律失常可能连续发生,或者可能间歇发生。可以使用许多不同的技术来诊断持续发生的心律失常,这些技术包括例如触诊个体的桡动脉脉搏、听诊个体的心脏声音、记录个体的心率、以及记录个体的心电图。因为在患者中总是存在或基本上总是存在连续的或基本上连续的心律不齐,所以可以在任何时候应用任何前述的诊断技术以进行诊断。也可以使用上述诊断技术中的任何一种用于间歇性心律失常诊断,然而由于间歇性心律失常并不总是存在,诊断技术不能在任何时候应用,而必须在当个体正在经历心律失常时应用。因此,诊断间歇性心律失常可能是困难的,因为例如在个体经历间歇性心律失常的确切时间处,准备好应用上述诊断模式之一是不实际的。当个体未感知到他们正在经历间歇性心律失常时,这种特殊的难度也可能加重,使得例如他们不会在间歇性心律失常期间寻求健康护理提供者。
然而,可以方便地感测某些参数值,例如心率和活动水平,并分析它们以预测或确定心律失常的存在。可以分析一个或多个方便连续感测的参数值,例如心率和活动水平,以通过识别这两个参数值之间的不一致来确定心律失常的未来发作或存在。例如,两个感测值之间的不一致可以指示心律失常的未来发作或存在。响应于对心律失常的未来发作或存在的识别,可以使心电图被感测。
附加的感测参数也可以在分析中用作本文描述的心脏监测系统、设备和方法的一部分。例如,确定的心率变异性可以与感测的心率和活动水平进行比较,以确定例如心房纤维性颤动或室上性心动过速的存在。
本文描述了一种用于心脏监测的方法,包括:利用由个体佩戴的可佩戴设备的第一传感器来感测所述个体的活动水平值;利用所述可佩戴设备的第二传感器感测所述个体的心率值;利用所述可佩戴设备的处理器确定心率变异性值;利用所述处理器确定所述活动水平值、所述心率值和所述心率变异性值中的两个或多个之间是否存在不一致;以及利用所述可佩戴设备向所述个体指示当所述不一致被确定存在时记录心电图。在一些实施例中,所述第一传感器包括加速度计。在一些实施例中,所述第一传感器包括陀螺仪。在一些实施例中,所述第二传感器包括光电传感器。在一些实施例中,当所述活动水平值正常并且所述心率值升高时,所述不一致被确定为存在。在一些实施例中,当所述活动水平值正常、所述心率值升高、并且所述心率变异性值增大时,所述不一致被确定为存在。在一些实施例中,所述方法包括指示心房纤维性颤动的存在。在一些实施例中,当所述活动水平值正常、所述心率值升高、并且所述心率变异性值减小时,所述不一致被确定为存在。在一些实施例中,所述方法包括指示室上性心动过速的存在。在一些实施例中,基于所述活动水平值、所述心率值和所述心率变异性值来设置一个或多个阈值。在一些实施例中,使用机器学习算法来确定所述一个或多个阈值。
本文描述了用于心脏监测的可佩戴设备,包括:处理器;第一传感器,被配置为感测个体的活动水平值,其中所述第一传感器耦合到所述处理器;第二传感器,被配置为感测个体的心率值,其中所述第二传感器耦合到所述处理器;第一电极和第二电极,被配置成感测心电图;用计算机程序编码的非暂态计算机可读存储介质,计算机程序包括可由所述处理器执行的指令以使得所述处理器执行以下操作:确定所述个体的所述活动水平值与所述个体的所述心率值之间是否存在不一致;并且指示当所述不一致被确定为存在时记录所述心电图。在一些实施例中,所述第一传感器包括加速度计。在一些实施例中,所述第一传感器包括陀螺仪。在一些实施例中,所述第二传感器包括光电传感器。在一些实施例中,当所述活动水平值正常并且所述心率值升高时,所述不一致被确定为存在。在一些实施例中,所述计算机程序包括指令,该指令使得所述处理器确定心率变异性值。在一些实施例中,当所述活动水平值正常、所述心率值升高、并且所述心率变异性值增大时,所述不一致被确定为存在。在一些实施例中,所述计算机程序包括指令,该指令使得所述处理器指示心房纤维性颤动的存在。在一些实施例中,当所述活动水平值正常、所述心率值升高、并且所述心率变异性值升高时,所述不一致被确定为存在。在一些实施例中,所述计算机程序包括指令,该指令使得所述处理器指示室上性心动过速的存在。在一些实施例中,所述计算机程序包括指令,该指令使得所述处理器基于所述活动水平值和所述心率值来设置一个或多个阈值。
在一些实施例中,使用机器学习算法来确定所述一个或多个阈值。
本文描述了一种用于心脏监测的方法,包括:利用由所述个体佩戴的可佩戴设备的第一传感器来感测个体的活动水平值;利用所述可佩戴设备的第二传感器感测所述个体的心率值;利用所述可佩戴设备的处理器通过使用活动水平阈值和心率阈值来确定所述活动水平值和所述心率值中的两个或多个之间是否存在不一致;以及使用由所述处理器执行的机器学习算法来调整所述活动水平阈值和所述心率水平阈值。
附图说明
本文描述的个别内容的新颖特征在所附权利要求书中具体阐述。通过参考阐述说明性实施例的以下详细描述以及附图,将获得对本文描述的本发明个别内容的特征和优点的更好理解,其中说明性实施例中利用了本文描述的个别内容的原理,附图包括:
图1示出了在同一时期从同一个体感测到的心率跟踪和相应的心电图(ECG)跟踪。
图2示出了显示经过一段时间后在经历不同心律失常的个体中的心率和心律分析的图。
图3示出了在阵发性心房纤维性颤动期间感测到的心率跟踪的特写。
图4示出了用于连续感测心率或活动水平的可用技术。
图5示出了通常用于测量与智能手表结合的心率的光电传感器。
图6例示了被编程或以其它方式配置以感测个体的一个或多个生理参数的计算机系统。
图7示出用于不一致监测的算法的示意图。
具体实施方式
心脏监测
本文描述了用于心脏监测的系统、设备和方法。心脏监测通常包括监测个体的心脏功能的变化,例如,心率或心律。
心率可以在例如通常被定义为低于每分钟搏动60次的心率的心动过缓、通常被定义为每分钟搏动60-100次的心率的正常静息心率和通常被定义为大于每分钟搏动100次的心率的心动过速之间变化。心率在一段时间内的变化可以被称为心率变异性(HRV)。
心脏功能也根据律动的规律性来衡量。正常心律包括收缩期(射血期)和舒张期(灌流期)。在收缩期和舒张期期间,心脏的心室以规律的方式一致地作用,每一次心跳都会重复。当律动异常时,通常将该情况称为心律失常。心律失常的示例包括心房纤维性颤动、WPW综合征、延长的QT综合征和早搏心室收缩。
许多心律失常间歇地发生,并且相对不常出现。因此,为了监测和捕获间歇性心律失常,通常需要连续监测。可以使用动态心电图(holter)监测在走动患者中连续测量ECG,但是这种类型的监测对于患者来说是麻烦的,并且因此没有被广泛使用。配置成拍摄间断ECG的设备或系统对用户来说更为方便。此类设备或系统包括移动计算设备,其包括当与患者的皮肤表面接触时感测ECG的一个或多个电极。此类设备是轻的且便携的,并且不一定要求用户与一个或多个电极连续物理接触,就像使用holter型监测器那样。当用户被指示发生间歇性心律失常时,可以利用这些设备和系统记录间歇性心律失常。HRV感测与这些设备或系统组合使用以向用户指示何时接触一个或多个电极以感测ECG。
图1示出了在同一时期从同一个体感测到的心率跟踪100和相应的心电图(ECG)跟踪104。如ECG跟踪104所示,在感测到ECG的时期,个体经历了间歇性心房纤维性颤动106期间。也如心率跟踪100所示,在间歇性心房纤维性颤动期间个体的心率快速增加102。因此,随着个体的心率从静息心率增加到增加的心率102,个体的HRV在间歇性心房纤维性颤动期间增加。因此,HRV变化与心房纤维性颤动相关,其中在间歇性心房纤维性颤动期间发现HRV增加。
图2示出了显示经过一段时间后在经历不同心律失常的个体中的心率202和心律分析200的图。如图所示,在感测的心房纤维性颤动200期间,测得的心率202倾向于增加到每分钟搏动100次以上。因此,在心律失常期间,在该个体中出现高于静息心率的升高心率。
图3示出了在阵发性心房纤维性颤动期间感测到的心率跟踪的特写。如图所示,在心房纤维性颤动期间,从正常心脏每分钟搏动60-100次增加到每分钟搏动100次以上302有大幅增加。
图4示出了用于连续感测心率或活动水平的可用技术400。所示的是由制造商(例如,苹果)提供的智能手表。所示出的智能手表技术400中的一种的佩戴者可以方便且持续地佩戴一整天与手表耦合或集成的一个或多个传感器,因此,经由与智能手表耦合或集成的一个或多个传感器有效地连续地监测一个或多个参数值。因此,其中一种智能手表技术400是可佩戴形式的设备类型的示例,其方便地提供对用户的一个或多个参数的连续监视。可佩戴设备的非限制性示例包括手表(例如,智能手表)、眼镜、腕带、项链和衣服,可佩戴设备可具有耦合到它们或集成到它们的一个或多个传感器。然后,例如如图4所示的此种技术的用户的一个或多个连续感测的参数被用于指示用户使用设备或系统来感测ECG。例如,当用户的HRV增加时,佩戴具有心率传感器的智能手表的用户被智能手表提醒以记录ECG。
图5示出了通常用于测量与智能手表502结合的心率的光电传感器500。
在许多具有发生心律失常倾向的个体中活动水平与心律失常相关,其中增加的活动水平与心律失常的发作相关。在其他个体中,在存在增加的HRV的情况下,由一个或多个活动传感器检测到的增加的活动水平可能是正常的,并且与心律失常无关。因此,如本文所述,添加连续的心率监测以及连续的活动水平监测可以在心律失常监测方面实现与连续的心电图监测相同的结果。使用与本文描述的设备或系统相关联的一个或多个传感器,可以方便且准确地连续且同时感测心率和活动水平的两个参数值。
设备和系统
图6例示了被编程或以其它方式配置以感测个体的一个或多个生理参数的计算机系统601。生理参数的非限制性示例包括心率、血压、温度、氧饱和度、ECG、HRV和活动水平。计算机系统601包括用户的电子设备635,或者包括相对于电子设备635远程定位的计算机系统。适用于系统601的电子设备包括移动电子设备,例如,智能手机、智能手表、平板电脑和膝上型电脑。电子设备601包括被配置成感测生理参数的一个或多个传感器。已知许多传感器用于测量心率。合适的传感器的非限制性示例包括基于光的传感器(例如,红外传感器/发射器)、超声波传感器和触觉传感器。用于测量律动的传感器包括用于测量心电图(ECG)的电极和用于测量光体积描记图的基于光的传感器。
计算机系统601包括中央处理单元(CPU,在本文也称为“处理器”和“计算机处理器”)605,中央处理单元可以是单核或多核处理器,或者用于并行处理的多个处理器。计算机系统601还包括存储器或存储器位置610(例如,随机存取存储器、只读存储器、闪存)、电子存储单元615(例如,硬盘)、用于与一个或多个其它系统通信的通信接口602(例如网络适配器)以及外围设备625(诸如,高速缓存、其它存储器、数据存储和/或电子显示适配器)。存储器610、存储单元615、接口602和外围设备625通过诸如主板的通信总线(实线)与CPU 605通信。存储单元615可以是用于存储数据的数据存储单元(或数据储存库)。计算机系统601可以借助于通信接口602可操作地耦合到计算机网络(“网络”)603。网络603可以是互联网,内联网和/或外联网,或者与互联网通信的内联网和/或外联网。在一些情况下,网络603是电信和/或数据网络。网络603可以包括一个或多个计算机服务器,其可以实现分布式计算,诸如云计算。在一些情况下,借助于计算机系统601,网络603可以实现对等网络,这可以使得耦合到计算机系统601的设备能够充当客户端或者服务器。
CPU 605可以执行一系列机器可读指令,这些指令可以用程序或软件来实现。指令可以被存储在例如存储器610的存储器位置中。指令可以指向CPU 605,其随后可以编程或以其它方式配置CPU 605以实现本公开的方法。由CPU 605执行的操作的示例可以包括获取、解码、执行和写回。
CPU 605可以是例如集成电路的电路的一部分。系统601的一个或多个其它组件可以被包括在电路中。在一些情况下,电路是专用集成电路(ASIC)。
存储单元615可以存储文件,例如,驱动程序、库和保存的程序。存储单元615可以存储用户数据,例如用户偏好和用户程序。在一些情况下,计算机系统601可以包括一个或多个附加数据存储单元,附加数据存储单元位于计算机系统601外部,诸如位于通过内联网或互联网与计算机系统601通信的远程服务器上。
计算机系统601可以通过网络603与一个或多个远程计算机系统进行通信。例如,计算机系统601可以与用户的远程计算机系统(例如,移动设备、服务器等)通信。远程计算机系统的示例包括个人计算机(例如,便携式PC)、平板或平板电脑(例如,iPad,Galaxy Tab)、电话、智能电话(例如,iPhone,支持Android的设备,)或个人数字助理。用户可以通过网络603访问计算机系统601。
可以通过存储在计算机系统601的电子存储位置(例如,存储器610或电子存储单元615)上的机器(例如,计算机处理器)可执行代码来实现在本文中描述的方法。机器可执行代码或机器可读代码可以以软件的形式提供。在使用期间,代码可以由处理器605执行。在一些情况下,可以从存储单元615检索代码并将其存储在存储器610上以供处理器605随时访问。在一些情况下,电子存储单元615可以被排除,并且机器可执行指令被存储在存储器610上。
代码可以预编译并配置成与机器一起使用,该机器具有适用于执行代码的处理器,或者可以在运行时编译代码。代码可以用编程语言提供,可以选择编程语言以使得代码以预编译或编译的方式执行。
本文提供的系统和方法(诸如计算机系统601)的各方面可以在编程中实现。技术的各个方面可以被认为是“产品”或“制造品”,其典型形式为机器(或处理器)可执行代码和/或以一种机器可读介质承载或体现的关联数据。机器可执行代码可以被存储在电子存储单元、此类存储器(例如,只读存储器、随机存取存储器、闪存)、或硬盘上。“存储”型介质可以包括计算机、处理器等的有形存储器或其相关模块(诸如各种半导体存储器、磁带驱动器、磁盘驱动器等)中的任何一个或全部,其可以随时为软件编程提供非暂态存储。所有或部分软件有时可以通过互联网或各种其它电信网络进行通信。例如,此种通信可以使软件从一个计算机或处理器加载到另一个,例如从管理服务器或主计算机到应用服务器的计算机平台。因此,可以承载软件元件的另一种类型的介质包括光学波、电波和电磁波,诸如通过有线和光学陆线网络以及各种空中链路在本地设备之间的物理接口上使用的。承载此种波的物理单元,诸如有线或无线链路、光链路等也可以被认为是承载软件的介质。如本文所使用的,除非限制于非暂时态的、有形的“存储”介质,诸如计算机或机器“可读介质”的术语是指参与向处理器提供指令以供执行的任何介质。
因此,诸如计算机可执行代码的机器可读介质可以采取许多形式,包括但不限于有形存储介质、载波介质或物理传输介质。非易失性存储介质包括例如光盘或磁盘,诸如任何计算机等中的任何存储设备,其诸如可用于实现附图中所示的数据库等。易失性存储介质包括动态存储器,诸如此类计算机平台的主存储器。有形的传输介质包括同轴电缆;铜线和光纤,包括在计算机系统内构成总线的线。载波传输介质可以采取电信号或电磁信号的形式,或者声波或光波的形式,诸如在射频(RF)和红外(ER)数据通信期间产生的那些。因此,计算机可读介质的常见形式包括例如:软盘、软磁盘、硬盘、磁带、任何其它磁介质、CD-ROM、DVD或DVD-ROM、任何其它光介质、穿孔卡纸磁带、具有孔图案的任何其它物理存储介质、RAM、ROM、PROM和EPROM、FLASH-EPROM、任何其它存储芯片或盒、传输数据或指令的载波、传输此种载波的电缆或链路、或计算机可以从其读取编程代码和/或数据的任何其它介质。在将一个或多个指令的一个或多个序列携带到处理器以供执行时可能涉及到这些形式的计算机可读介质中的许多。
计算机系统601可以包括电子显示器535或者与其通信,电子显示器包括用户界面(UI)640,其用于提供例如磁场的分布、电流的分布、局部心肌活动的分布等。UF的示例包括但不限于图形用户界面(GUI)和基于Web的用户界面。
本公开的方法和系统可以通过一个或多个算法来实现。算法可以在中央处理单元605执行时通过软件来实现。例如,算法被用于分析感测的生理参数。
在一些实施例中,本文所述的设备被配置成感测两个或多个生理参数。例如,在一些实施例中,配置成测量如本文所述的个体的心率的设备也被配置成感测所述个体的心电图。在这些实施例中,如本文所述的设备包括被配置成感测个体的心电图的一个或多个电极。在一些实施例中,如本文所述的设备包括两个电极。在一些实施例中,如本文所述的设备包括三个电极。在一些实施例中,如本文所述的设备包括四个电极。在一些实施例中,如本文所述的设备包括五个电极。在一些实施例中,如本文所述的设备包括六个电极。在一些实施例中,如本文所述的设备包括七个电极。在一些实施例中,如本文所述的设备包括八个电极。在一些实施例中,如本文所述的设备包括九个电极。在一些实施例中,如本文所述的设备包括十个电极。本文描述的设备的电极被配置成感测个体的心电图并且将感测到的心电图数据发送到与本文所述的设备或系统的一部分集成的处理器。在一些实施例中,处理器被配置成在本文描述的设备的显示器上显示心电图。在一些实施例中,设备被配置成感测和/或显示单个导联心电图。在一些实施例中,单个导联包括导联I、导联II、导联aVR、导联aVL、导联aVF、导联V1、导联V2、导联V3、导联V4、导联V5和导联V6中的任一个。在一些实施例中,设备被配置成感测和/或显示两个导联,其包括导联I、导联II、导联aVR、导联aVL、导联aVF、导联V1、导联V2、导联V3、导联V4、导联V5和导联V6中的任意两个。在一些实施例中,设备被配置成感测和/或显示两个导联,其包括导联I、导联II、导联aVR、导联aVL、导联aVF、导联V1、导联V2、导联V3、导联V4、导联V5和导联V6中的任意三个。在一些实施例中,设备被配置成感测和/或显示三个导联,其包括导联I、导联II、导联aVR、导联aVL、导联aVF、导联V1、导联V2、导联V3、导联V4、导联V5和导联V6中的任意三个。在一些实施例中,设备被配置成感测和/或显示四个导联,其包括导联I、导联II、导联aVR、导联aVL、导联aVF、导联V1、导联V2、导联V3、导联V4、导联V5和导联V6中的任意四个。在一些实施例中,设备被配置成感测和/或显示五个导联,其包括导联I、导联II、导联aVR、导联aVL、导联aVF、导联V1、导联V2、导联V3、导联V4、导联V5和导联V6中的任意五个。在一些实施例中,设备或设备被配置成感测和/或显示六个导联,其包括导联I、导联II、导联aVR、导联aVL、导联aVF、导联V1、导联V2、导联V3、导联V4、导联V5和导联V6中的任意六个。在一些实施例中,设备被配置成感测和/或显示七个导联,其包括导联I、导联II、导联aVR、导联aVL、导联aVF、导联V1、导联V2、导联V3、导联V4、导联V5和导联V6中的任意七个。在一些实施例中,设备被配置成感测和/或显示八个导联,其包括导联I、导联II、导联aVR、导联aVL、导联aVF、导联V1、导联V2、导联V3、导联V4、导联V5和导联V6中的任意八个。在一些实施例中,设备被配置成感测和/或显示九个导联,其包括导联I、导联II、导联aVR、导联aVL、导联aVF、导联V1、导联V2、导联V3、导联V4、导联V5和导联V6中的任意九个。在一些实施例中,设备被配置成感测和/或显示十个导联,其包括导联I、导联II、导联aVR、导联aVL、导联aVF、导联V1、导联V2、导联V3、导联V4、导联V5和导联V6中的任意十个。在一些实施例中,设备被配置成感测和/或显示十一个导联,其包括导联I、导联II、导联aVR、导联aVL、导联aVF、导联V1、导联V2、导联V3、导联V4、导联V5和导联V6中的任意十一个。在一些实施例中,设备被配置成感测和/或显示十二个导联,其包括导联I、导联II、导联aVR、导联aVL、导联aVF、导联V1、导联V2、导联V3、导联V4、导联V5和导联V6中的任意十二个。在一些实施例中,设备包括被配置成使所述设备的处理器分析感测的心电图的软件。由设备的处理器执行的感测的心电图的分析识别了异常心脏状况的存在。例如,在一些实施例中,由设备的处理器执行的分析通过例如分析PQRST波形和/或比较心电图内的多个PQRST波形来识别心律失常。在一些实施例中,处理器通过比较个体的一个或多个PQRST波形与来自包含其他个体的心电图的数据库的其他个体的一个或多个PQRST波形来执行心电图的分析。在本文描述的设备的一些实施例中,当设备感测到一个或多个生理参数时,提醒个体通过例如接合一个或多个电极来感测心电图。例如,在一些实施例中,如本文所述的设备被配置成感测个体的血压,并且在这些实施例中的一些实施例中,该设备被配置成感测个体的第二生理参数,例如心率。除了例如由设备同时感测到的个体的低血压之外,由设备感测的个体的加速心率触发设备的处理器指示个体与设备的电极接合以感测心电图。
感测的加速心率和低血压的组合通常指示异常,然而,其它生理状况也可能产生伴随着低血压的升高心率,包括例如脱水。因此,在一些实施例中,当将生理参数(诸如心率、血压、氧饱和度和温度)与个体的基线值或来自包含其他个体的生理参数的数据库的数据进行比较时,准确性增强。例如,一些精英运动员具有在另一个体中异常的生理参数值,例如非常低的心率或增加的心率变异性(例如,在锻炼期间)。
在一些实施例中,本文所述的设备被配置成感测个体的光体积描记图。例如,光体积描记图提供心脏周期信息,并且可以例如由本文描述的设备的处理器进行分析以确定早搏心室收缩的存在。
在一些实施例中,如本文所述的设备被配置成感测个体的脉搏氧合。在一些实施例中,如本文所述的设备被配置成感测个体的脉搏氧合。
分析
在一些实施例中,如本文所述的设备被配置成感测和/或分析多个附加的生理参数。由本文描述的设备和系统感测和/或分析的参数值的非限制性示例包括心率、活动水平、血压、温度、脉搏氧和心率变异性。在一些实施例中,分析包括第一感测生理参数与第二感测生理参数的比较,并确定第一感测参数值与第二感测参数值之间是否存在不一致。
在一些实施例中,如本文所述的设备被配置成监测个体中的心律失常,其中监测可以包括心律失常发作的识别。在一些实施例中,由本文描述的设备执行的心脏监视包括例如监测先前未被识别有心律失常的个体中的心律失常的存在或发作。在一些实施例中,由本文描述的设备执行的心脏监测包括识别已知或怀疑的间歇性心律失常的发作。在一些实施例中,本文描述的设备被配置成预测个体的心律失常的发作。例如,心律失常的发作是由于所感测的生理参数(诸如心率)的值的突然和显著的移位而预测的。当两个或多个生理参数被同时检测和相对于彼此分析时,心律失常的预测更精确。例如,相对于感测的活动水平的心率变化的感测提供了感测心率的上下文信息。
心律失常的子集有时被称为快速性心律失常。快速性心律失常通常包括心动过速心率,其可以包括每分钟搏动100次以上的心率。快速性心律失常可以包括例如某些类型的心房纤维性颤动和室上性心动过速。在一些实施例中,如本文所述的设备被配置成识别快速性心律失常的存在或发作,例如心房纤维性颤动或室上性心动过速。在一些实施例中,如本文所述的设备被配置成识别快速性心律失常的存在或发作。在一些实施例中,如本文所述的设备被配置成预测快速性心律失常的发作。
在一些实施例中,如本文所述的设备被配置成提供连续的心脏监测。在一些实施例中,如本文所述的设备被配置成提供长达一年的持续的心脏监测。在一些实施例中,如本文所述的设备被配置成提供长达12个月的持续的心脏监测。在一些实施例中,本文所述的设备被配置成提供长达6个月的持续的心脏监测。在一些实施例中,本文所述的设备被配置成提供长达3个月的持续的心脏监测。在一些实施例中,本文所述的设备被配置成提供长达1个月的持续的心脏监测。在一些实施例中,本文所述的设备被配置成提供长达2周的持续的心脏监测。在一些实施例中,本文所述的设备被配置成提供长达1周的持续的心脏监测。在一些实施例中,本文所述的设备被配置成提供长达72小时的持续的心脏监测。在一些实施例中,本文所述的设备被配置成提供长达48小时的持续的心脏监测。在一些实施例中,本文所述的设备被配置成提供长达24小时的持续的心脏监测。在一些实施例中,本文所述的设备被配置成提供长达12小时的持续的心脏监测。在一些实施例中,本文所述的设备被配置成提供长达8小时的持续的心脏监测。在一些实施例中,本文所述的设备被配置成提供长达4小时的持续的心脏监测。在一些实施例中,本文所述的设备被配置成提供长达2个月的持续的心脏监测。
在一些实施例中,本文描述的设备被配置为提供间歇性心脏监测。在一些实施例中,响应于一个或多个感测的参数值而开始间歇性心脏监测。可以引起间歇性心脏监测开始的一个或多个感测参数值的非限制性示例可以包括例如个体的心率、个体的血压、个体的活动水平、个体的温度、个体的脉搏血氧测定、或个体的任何其它感测的生物测定参数。在一些实施例中,可以响应于一个或多个感测参数来感测个体的心电图。例如,可以使心电图响应于心率值而被感测。
在一些实施例中,一个或多个连续传感器可以感测一个或多个参数,其通过一个或多个传感器引起间歇性心脏监测开始。在一些实施例中,连续感测个体的心率。在一些实施例中,连续感测个体的活动水平。在一些实施例中,连续感测个体的心率变异性。在一些实施例中,间歇地感测个体的心电图。在一些实施例中,响应于个体的连续测量的心率,使间歇感测的心电图被感测。在一些实施例中,响应于个体的活动水平,使间歇感测的心电图被感测。在一些实施例中,响应于连续测量的心率和连续测量的活动水平,使间歇感测的心电图被感测。在一些实施例中,响应于连续感测的心率、连续感测的活动水平以及连续感测的心率变化性,使间歇感测的心电图被感测。
在一些实施例中,如本文所述的设备或系统包含一个或多个传感器,其配置成用于连续心脏监测。在一些实施例中,如本文所述的设备或系统包含一个或多个传感器,其配置成用于间歇性心脏监测。在一些实施例中,如本文所述的设备或系统包括一个或多个心率传感器,其可以例如包括光电传感器。在一些实施例中,如本文所述的设备或系统包括一个或多个活动水平传感器,其可以例如包括加速度计或陀螺仪。在一些实施例中,如本文所述的设备或系统包括一个或多个心电图传感器,其可以例如包括一个或多个电极。适用于本文描述的设备、系统和方法的其它传感器的非限制性示例还包括血压传感器、温度传感器和脉搏血氧传感器。
在一些实施例中,如本文所述的设备或系统包括处理器。在一些实施例中,处理器与被配置成连续感测的一个或多个传感器以及被配置成间歇感测的一个或多个传感器耦合。在一些实施例中,处理器被配置成从一个或多个传感器接收参数值。在一些实施例中,处理器被配置成激活一个或多个传感器或启动对参数值的感测。在一些实施例中,处理器被配置成分析参数值。在一些实施例中,处理器被配置成将第一参数值与第二参数值进行比较。在一些实施例中,要被比较的第一参数值和第二参数值同时或基本上同时被感测。
在一些实施例中,如本文所述的设备或系统还包括程序或应用程序形式的软件。在一些实施例中,程序或应用程序可以被配置成使处理器执行一个或多个功能。在一些实施例中,程序或应用程序可以被配置成使处理器从一个或多个传感器接收参数值。在一些实施例中,程序或应用程序可以被配置成使处理器激活一个或多个传感器或启动对参数值的感测。在一些实施例中,程序或应用程序可以被配置成使处理器分析参数值。在一些实施例中,程序或应用程序可以被配置成使处理器将第一参数值与第二参数值进行比较。在一些实施例中,要被比较的第一参数值和第二参数值同时或基本上同时被感测。
在一些实施例中,本文描述的设备被配置成执行分析,其中分析由处理器执行。在一些实施例中,由本文描述的设备执行的一个或多个参数值的分析包括感测的参数值与阈值或范围的比较。例如,分析可以包括确定感测的心率值是否落入一个或多个范围内。例如,在一些实施例中,感测的心率可以被确定为在包括每分钟搏动60-100次的范围的心率范围内。例如,在一些实施例中,感测的心率可以被确定为在包括小于每分钟搏动60次的范围的心率范围内。例如,在一些实施例中,感测的心率可以被确定为在包括大于每分钟搏动100次的范围的心率范围内。
在一些实施例中,由本文描述的设备执行的一个或多个参数值的分析包括第一感测参数与第二感测参数的比较。例如,在一些实施例中,将心率值与个体的感测活动水平进行比较。
在一些实施例中,将第一感测值与第二感测值进行比较,并且确定两个值之间是否存在不一致。例如,在一些实施方式中,预期在升高的活动期间存在升高的心率值,因此同时感测的升高的心率和升高的活动水平将不会被发现彼此不一致。
当第一感测参数值不期望与第二感测参数值一致时,可以识别不一致。例如,升高的心率值预计不会出现在正常或静息的活动水平上,并且因此这两个值彼此不一致。例如,在一些实施例中,当心率传感器感测到每分钟搏动100次以上的心率并且同时感测的活动水平被确定为静息活动水平时,两个感测值的分析确定它们彼此不一致。
在一些实施例中,由本文描述的设备和系统执行的分析包括心率变异性的增加的确定。在一些实施例中,由本文描述的设备和系统执行的分析包括将心率变异性与一个或多个感测的参数值进行比较。例如,在一些实施例中,将心率变异性与同时或基本上同时感测的心率值和活动水平值进行比较。
在一些实施例中,由本文描述的设备和系统执行的分析包括使用如本文所述的识别的不一致来预测或识别心律失常的发生。在一些实施例中,确定包括同时或基本上同时感测的升高的心率和静息或正常活动水平的不一致以指示心律失常即将发生或心律失常的存在。特别地,由于心率升高,具有这种类型的不一致的心律失常典型地包括快速性心律失常。
在一些实施例中,确定心率变异性、升高的心率以及静息或正常活动率上同时感测的增加以指示心房纤维性颤动的未来发作或存在。在一些实施例中,还可以确定感测的增加的心率变异性、正常静息心率和静息或正常活动率以指示心房纤维性颤动的未来发作或存在。在一些实施例中,确定心率变异性、升高的心率以及静息或正常活动率上同时感测的降低以指示室上性心动过速的未来发作或存在。在一些实施例中,当心律失常被确定为即将发生或者存在时,记录心电图。在一些实施例中,通过本文描述的心脏监测设备或系统指示或通知个体接合一个或多个电极以感测心电图。在一些实施例中,一个或多个电极可以定位在心脏监测设备的表面上,以使得个体例如可以舒适地使第一电极与第一末端的皮肤表面接合,而同时使第二电极与第二末端的皮肤表面接合。在一些实施例中,当心律失常被确定为即将发作或存在于个体中时,一个或多个电极可以被附接到个体的身体并且被自动地接合以通过心脏监测设备或系统感测心电图。例如,第一电极可以定位在由个体的第一末端佩戴的智能手表上,并且第二电极可以定位在由个体的第二末端佩戴的腕饰上。在这个示例中,智能手表上的第一电极和腕饰上的第二电极都与心脏监测设备通信并受其控制。
在一些实施例中,本文描述的设备被配置成执行机器学习。在一些实施例中,本文描述的设备、系统和方法包括机器学习算法,其分析在一段时间内从个体感测的参数值。在一些实施例中,本文描述的设备、系统和方法包括机器学习算法,其分析从多个个体感测的参数值。在一些实施例中,机器学习算法使得本文描述的设备、系统和方法更准确地识别或预测给定个体中心律失常的存在。例如,在一些实施例中,感测的心电图数据可以被往回比较参数值,例如,感测的心率和活动水平,其触发所述心电图的感测。例如,当感测的心电图确认心律失常的存在,其存在由例如其它参数值之间的不一致所指示时,机器算法使得本文描述的设备或系统从该数据中学习。类似地,例如,当感测的心电图不能确认心律失常的存在,其存在由例如其它参数值之间的不一致所指示时,机器算法使得本文描述的设备或系统也从该数据学习。也就是说,在一些实施例中,机器学习算法将感测到的心电图与使得其(即,心电图)被感测的参数值之间的不一致相关联。心电图上的心律失常的存在或不存在分别强化了心律失常与使得心电图被感测的不一致的相关性,或者与心律失常与不一致的相关性的存在相矛盾。例如,当感测到110的心率并且同时感测到静息活动时,使得心电图被感测,并且当感测的心电图不指示心律失常的存在时,机器学习算法使得本文描述的设备或系统学习到对于该个体而言,静息时110的心率不一定表示心律失常的存在。在一些实施例中,机器学习算法继续引起参数值数据(例如,心率、活动水平和心率变异性)的存储,并且随时间将参数值与相关联的心电图数据进行比较。因此,在一些实施例中,利用随时间感测的多个参数值并且与相关联的心电图数据进行比较,心脏监视设备或系统基于特定个体的参数值之间的不一致提高了其预测或识别心律失常发作的能力。在一些实施例中,因为由机器学习算法确定的基于参数值的心律失常的存在或不存在的可能性非常高,当在特定个体的参数值之间存在特定的不一致时,机器学习算法可以避免感测心电图的需要。
本文描述的用于心脏监测的任何设备、系统和方法可以包括智能手机、膝上型电脑或台式电脑、智能手表或平板电脑中的一个或多个。
不一致监测
图7示出用于不一致监测的算法的示意图。在步骤700中,心率和活动水平由例如本文描述的设备或系统感测。在一些实施例中,使用陀螺仪或加速度计感测活动水平。使用基于光的或其它常用的心率传感器感测心率。测量心率和活动水平的设备可以是相同的设备或多于一个的设备。例如,智能手表或其它可佩戴设备可以被配置成包括心率传感器以及活动水平传感器。
如步骤702所示,如果感测到增加的心率以及正常或静息活动水平,通过设备或系统处理器确定两个值为不一致。也就是说,升高的心率与感测的稳定活动水平不匹配。通过如本文描述的设备或系统的处理器来确定不一致的存在。所识别的不一致可以表明心律失常的存在。如此,在步骤712A中,使得ECG被感测。例如,步骤712A可以包括通过感测到心率和活动水平的设备或系统向用户指示接触ECG感测设备的一个或多个电极并因此感测ECG。ECG感测设备可以是用于感测心率和活动水平的设备或系统的一部分,或者可以是单独的设备。例如,佩戴具有心率和活动水平监测的智能手表的用户接收来自智能手表的听觉和/或视觉指示,以在感测的心率值与感测的活动水平值之间存在不一致时感测ECG。在一些实施例中,智能手表包括一个或多个电极,并且当因为不一致的存在从而感测ECG而从智能手表接收如此做的指示时,用户将一个电极与其身体的左侧接触,以及将一个电极与其身体的右侧接触。在一些实施例中,智能手机包括一个或多个电极,并且当因为不一致的存在从而感测ECG而从智能手表接收如此做的指示时,用户将一个电极与其身体的左侧接触,以及将一个电极与其身体的右侧接触。
如步骤704所示,如果感测到增加的心率以及增加的心率变异性,并且感测到正常或静息的活动水平。增加的心率和HRV与正常或静息活动水平不一致,并且通过设备或系统处理器确定不一致的存在。一旦确定不一致,例如,如本文相对于步骤712A所描述的,在步骤712B中使得ECG被感测。如图所示,在步骤716中,该特定不一致可以指示心房纤维性颤动的存在,并且应该用712B的ECG来确定。
如步骤706所示,如果感测到增加的心率以及降低的心率变异性,并且感测到正常或静息的活动水平。增加的心率、降低的心率变异性以及正常或静息活动水平彼此不一致,并且通过设备或系统处理器确定不一致的存在。一旦确定不一致,例如,如本文相对于步骤712A所描述的,在步骤712C中使得ECG被感测。如图所示,在步骤718中,可能存在室上性心动过速,并且应该用712C的ECG来确定。
如步骤708所示,如果感测到增加的心率以及增加的活动水平,则设备或系统处理器确定不存在不一致,并且在个体可能正在锻炼714时,不记录ECG。
如步骤710所示,如果感测到规律的心率(例如,每分钟60-100次),并且感测到心率变异性增加以及正常或静息活动水平。正常的心率、增加的心率变异性以及正常或静息活动水平彼此不一致,并且通过设备或系统处理器确定不一致的存在。一旦确定不一致,例如,如本文相对于步骤712A所描述的,在步骤712D中使得ECG被感测。如图所示,在步骤718中,可能存在心房纤维性颤动,并且应该用712D的ECG来确定。
在一些实施例中,不一致的存在的确定基于两个或多个感测的生理参数彼此的比较。也就是说,例如,将110的升高的心率与由加速度计感测的静息活动水平进行比较,加速度计测量到个体以0英里/小时的速度行进。110的心率升高,而0英里/小时的活动水平是静息水平,这表明感测的心率与活动水平之间的不一致。在一些实施例中,处理器确定感测的生理参数的值高于或低于阈值或值的范围。在一些实施例中,阈值或值的范围被认为是群体中的正常值或静息值。在一些实施例中,阈值对于用户的生物数据是特定的,使得用户例如与来自普通人群的适当阈值年龄匹配或性别匹配。例如,70岁的用户的活动水平被确定为增加,而7岁的用户的活动水平不会增加。因此,如果感测的生理参数升高、降低或正常(或静息),则通过限定来确定不一致,并且然后将该限定值与另一感测的生理参数的限定值进行比较。也就是说,例如,限定为增加、减少或正常(或静息)的值与也被限定为增加、减少或正常(或静息)的值相比较。
在一些实施例中,本文描述的设备和系统的附加步骤(图7中未示出)运行机器学习算法,使得用于确定感测的生理参数是否是增加、减少、正常(或静息)的阈值和范围被调整以更精确地适合用户。也就是说,例如,通过ECG确定具有心率为80的心律失常的用户将降低他们的心率阈值,使得85的心脏(其在一些情况下是正常的)将被确定为增加的速率。机器学习算法随时间更精确地设置阈值,从而更准确地确定不一致,从而响应于确定不一致的存在而导致ECG的更精确(和有效)的记录。
下面的表1以表格的形式呈现了图7中发现的一些信息。
表1
虽然本文描述的本发明的个别内容的优选实施例已经在本文中显示和描述,但是对于本领域技术人员来说显而易见的是此类实施例仅仅是通过示例方式提供的。在不脱离本文描述的个别内容的情况下,本领域技术人员现在将想到许多变化、改变和替换。应该理解,本文描述的个别内容的实施例的各种替代物可以用于实践本文描述的个别内容。下面的权利要求书旨在限定本文描述的个别内容的范围,并且由此覆盖这些权利要求及其等同物范围内的方法和结构。
Claims (22)
1.一种用于心脏监测的方法,包括:
利用由个体佩戴的包括腕饰或智能手表的可佩戴设备的第一传感器来连续地感测所述个体的活动水平值;
利用所述可佩戴设备的第二传感器来在所述活动水平值为静息时连续地感测所述个体的心率值;
利用所述可佩戴设备的处理器来确定所述活动水平值和所述心率值之间是否存在不一致;以及
基于所述不一致的存在,从所述可佩戴设备上的心电图传感器即ECG传感器接收所述个体的电信号,其中,所述ECG传感器包括用于感测所述电信号的第一电极和第二电极。
2.根据权利要求1所述的方法,其中,所述第一传感器包括加速度计。
3.根据权利要求1所述的方法,其中,所述第一传感器包括陀螺仪。
4.根据权利要求1所述的方法,其中,所述第二传感器包括光电传感器。
5.根据权利要求1所述的方法,其中,当所述活动水平值正常并且所述心率值升高时,所述不一致被确定为存在。
6.根据权利要求1所述的方法,包括基于所述心率值来确定心率变异性值。
7.根据权利要求6所述的方法,其中,当所述活动水平值正常、所述心率值升高、并且所述心率变异性值增大时,所述不一致被确定为存在。
8.根据权利要求6所述的方法,其中,当所述活动水平值正常、所述心率值升高、并且所述心率变异性值减小时,所述不一致被确定为存在。
9.根据权利要求6所述的方法,包括基于所述活动水平值、所述心率值和所述心率变异性值来设置一个或多个阈值。
10.根据权利要求9所述的方法,其中,使用机器学习算法来确定所述一个或多个阈值。
11.一种包括腕饰或智能手表的可佩戴设备,包括:
处理器;
第一传感器,被配置为连续地感测个体的活动水平值,其中所述第一传感器被耦合到所述处理器;
第二传感器,被配置为在所述活动水平值为静息时连续地感测个体的心率值,其中所述第二传感器被耦合到所述处理器;
第一电极和第二电极,被配置为感测心电图;
用计算机程序编码的非暂态计算机可读存储介质,所述计算机程序包括可由所述处理器执行的指令以使所述处理器执行以下操作:
确定所述个体的所述活动水平值与所述个体的所述心率值之间是否存在不一致;
基于所述不一致的存在,利用所述可佩戴设备向所述个体指示健康异常的可能性;以及
从所述可佩戴设备上的心电图传感器即ECG传感器接收所述个体的电信号,以确认所述健康异常的存在,其中,所述ECG传感器包括所述第一电极和所述第二电极。
12.根据权利要求11所述的设备,其中,所述第一传感器包括加速度计。
13.根据权利要求11所述的设备,其中,所述第一传感器包括陀螺仪。
14.根据权利要求11所述的设备,其中,所述第二传感器包括光电传感器。
15.根据权利要求11所述的设备,其中,当所述活动水平值正常并且所述心率值升高时,所述不一致被确定为存在。
16.根据权利要求11所述的设备,其中,所述计算机程序包括指令,该指令使得所述处理器确定心率变异性值。
17.根据权利要求16所述的设备,其中,当所述活动水平值正常、所述心率值升高、并且所述心率变异性值增大时,所述不一致被确定为存在。
18.根据权利要求16所述的设备,其中,所述计算机程序包括指令,该指令使得所述处理器指示心房纤维性颤动的存在。
19.根据权利要求16所述的设备,其中,当所述活动水平值正常、所述心率值升高、并且所述心率变异性值减小时,所述不一致被确定为存在。
20.根据权利要求19所述的设备,其中,所述计算机程序包括指令,该指令使得所述处理器指示室上性心动过速的存在。
21.根据权利要求11所述的设备,其中,所述计算机程序包括指令,该指令使得所述处理器基于所述活动水平值和所述心率值来设置一个或多个阈值。
22.根据权利要求21所述的设备,其中,使用机器学习算法来确定所述一个或多个阈值。
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ES2820923T3 (es) | 2021-04-22 |
EP3282933A4 (en) | 2018-04-25 |
JP6918038B2 (ja) | 2021-08-11 |
CN107847154A (zh) | 2018-03-27 |
US10638941B2 (en) | 2020-05-05 |
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