CN108697323A - 应激水平的非侵入式生理量化 - Google Patents
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- CN108697323A CN108697323A CN201680065047.3A CN201680065047A CN108697323A CN 108697323 A CN108697323 A CN 108697323A CN 201680065047 A CN201680065047 A CN 201680065047A CN 108697323 A CN108697323 A CN 108697323A
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Abstract
一种数据获取设备包括测量仪器以生成生理数据流和/或心理数据流。所述获取设备内的微处理器将生成的数据流处理成度量,所述度量被馈送到应激函数算法内。算法处理可以发生在所述设备上,或度量可以经由无线通信传送以用于在移动设备和/或基于云的平台上进行外部处理。计算的应激函数告知基于云的计算系统生物学导出模型,该生物学导出模型描述响应于应激刺激而在身体内释放的激素和神经递质的动态。使用这些模型来量化应激激素水平,并组合使用所述应激激素水平以用作个体正在经历的急性应激和慢性应激的生物激发度量。
Description
相关专利申请的交叉引用
本发明要求享有2015年11月6日提交的美国临时专利申请No.62/251,996的优先权,该美国临时专利申请通过引用的方式纳入本文。
技术领域
本发明涉及非侵入式数字健康监测和信号处理领域。具体地,本发明涉及用于非侵入式量化人类对象的应激水平的系统和方法。
背景技术
应激是当今社会的一个基本问题。虽然年轻且健康的个体可能能够应对急性应激发作,但是持续经受急性应激和慢性应激可能对健康有长期有害的影响。存在监测应激水平的需要,以允许更好地管理应激,从而减少与应激有关的消极健康影响。
应激是一种复杂的生理和心理现象。尽管存在许多生理和心理应激测量,但是不存在用于急性应激和/或慢性应激的量化的单个黄金标准度量(metric)。现有的应激测量被认为过于简化和/或侵入式和/或难以测量,因此,现有的应激测量不提供对应激水平的可靠和/或连续监测。因此,对能够快速地、连续地、准确地和非侵入式地监测个体的应激水平以识别应激源(stressor)从而能够更好地管理和改善整体应激水平和总体幸福感,仍然存在挑战。
应激的定义
生物应激或生理应激被定义为生物体对应激源(诸如环境条件或刺激)的反应。应激可以是积极的(良性应激),例如但不限于运动,或是消极的(非良性应激),例如但不限于工作应激。无论是哪种应激,身体都会相应地作出回应以克服每种相应的挑战或情形。急性应激被定义为由特定情形驱动的短期应激,该特定情形可以被解释为令人兴奋的和/或激励的(例如坐过山车)或可怕的(例如附近的交通事故)。应激可以被进一步细分为心理应激和物理应激或运动应激。慢性应激发生在反复经受应激情形时,这导致慢性激活应激反应。
生物水平上的应激控制
应激反应受大脑边缘区域(诸如海马体、杏仁核和下丘脑,大脑中经由脑垂体腺将神经系统连接至内分泌系统的部分)的控制。当环境感官刺激被大脑的边缘前额皮质感知为应激源时,神经冲动从海马体传播并且激活下丘脑后部,下丘脑后部进而触发自主神经系统的交感分支。此神经应激轴是第一个对应激源回应反应,且它直接使目标器官(诸如心脏)受神经支配以增加心率和心输出量,以及骨骼肌激活。去甲肾上腺素是由使这些器官受神经支配的突触神经元释放的主要激素。自主神经系统的副交感分支起平衡快速应激反应的作用,且通过分泌神经递质(诸如乙酰胆碱)来维持体内平衡,乙酰胆碱将导致心率和心输出量降低。
除了经由交感神经系统直接使目标器官受神经支配之外,还存在两种主要的应激反应途径:交感肾上腺髓质(SAM)轴和下丘脑脑垂体肾上腺(HPA)轴。SAM轴的激活是一种快速作用的生理反应,并导致血液血浆和血清中的儿茶酚胺激素的水平增加,儿茶酚胺激素包括“战斗或逃跑”激素肾上腺素和去甲肾上腺素。当应激源已经由大脑的边缘前额皮质界面处理时,海马体被刺激,随后下丘脑被刺激,接着肾上腺髓质被刺激。肾上腺髓质被刺激导致肾上腺素和去甲肾上腺素释放到血流中,这激活“战斗或逃跑”反应。肾上腺素是在此阶段释放的主要激素,包括总分泌物的80%。此生理反应是快速的,并导致心率、血压、心输出量、出汗和血糖水平增加。因为皮质醇还可以刺激肾上腺髓质产生肾上腺素和去甲肾上腺素,所以SAM轴和HPA轴之间存在串扰。一旦应激得到处理,副交感神经系统就被激活以抑制交感神经系统的影响,并恢复体内平衡。
HPA轴的激活是一种相对缓慢的生理反应,并导致类固醇激素(包括皮质醇和醛固酮)水平的增加。HPA轴的激活开始于从下丘脑释放促肾上腺皮质激素释放激素(CRH)。CRH作用于脑垂体腺的前叶,前叶在CRH激活时释放促肾上腺皮质激素(ACTH)。ACTH通过血流传播以刺激肾上腺的皮质,这随后导致释放皮质醇和醛固酮。皮质醇是应激反应的关键调节剂且对身体具有广泛影响,其主要作用包括经由糖质新生促进葡萄糖形成和将能量重新分配至紧要器官(诸如心脏和大脑),此外还抑制不必要的功能,诸如免疫反应和再生。皮质醇水平在应激事件之后十到三十分钟达到峰值,且水平可以在该事件之后保持升高达大约一小时。醛固酮是从肾上腺皮质分泌的另一种激素,且是肾素血管紧张素系统的一部分,其通过在肾脏中保留钠和水来调节血压。虽然身体具有适当的负反馈系统以确保身体在急性应激刺激之后回到体内平衡,但是经受慢性应激导致此系统被过度刺激,这可能有严重的健康影响。
其他因素也影响皮质醇的水平,包括年龄、性别、病毒感染、睡眠不足、咖啡因消耗和强烈的身体运动。皮质醇水平以昼夜节律方式被调节,其中皮质醇水平遵循昼夜循环。早上皮质醇水平最高,水平在整个白天内降低,直到在晚上、在睡眠开始之后的三至五小时之间达到其最低水平。皮质醇的昼夜调节可能在异常生理条件和心理条件(例如但不限于应激)下改变。
当前的应激测量方法
虽然存在许多心理和生理应激测量,但是不存在用于测量应激的单个黄金标准度量。心率变异性(HRV)是学术文献以及一些商业应用中最常用的急性应激量度。心率变异性由于自主神经系统的交感分支和副交感分支的相反活动而发生,自主神经系统形成SAM应激反应通路的一部分。然而,已经表明,HRV测量不是交感神经系统和副交感神经系统的完美表示,且这两个系统在所有条件下都不相关。研究已经表明,单独使用HRV作为应激的测量是复杂生理过程的过度简化。给出SAM轴激活的指示的其他应激测量包括心率、血压监测、皮肤电活动测量、呼吸率测量和唾液α-淀粉酶水平。经由使用心理问卷的评估也可以实现应激水平的监测和量化。所有这些测量都被认为过于简化或侵入式和/或难以测量,因此不提供对应激水平的可靠和/或连续监测。
研究已经表明,皮质醇水平可以被可靠地从唾液中采样,且在诱导应激之后十到三十分钟内达到峰值。激素皮质醇是应激反应的一种关键调节剂,且由肾上腺中的胆固醇合成。血液皮质醇水平在应激事件之后十到三十分钟达到峰值。在事件之后水平保持升高达大约一小时。升高的血液皮质醇水平激活负反馈回路系统,这导致皮质醇产生减少,从而导致血液皮质醇水平回到基线。此负反馈机制确保身体在应激刺激之后回到体内平衡。因此,血液皮质醇水平用作应激的生物标记。因此,唾液皮质醇已经成为应激研究中最受欢迎的生物标记物之一,且是用于激活HPA轴的黄金标准度量。唾液皮质醇测量的一个警告是唾液皮质醇测量不完全比得上血液皮质醇水平,原因是一些唾液皮质醇水平是由于口腔中的皮质酮活动造成的,且需要实验室分析样品。因此,难以获得快速结果或难以实现连续监测。
美国专利8,622,901(Jain等人)描述了一种连续监测患者的应激的方法,该方法使用加速度计数据连同许多其他传感器,包括(但不限于)心率监测器、血压监测器、脉搏血氧计和情绪传感器。为了能够使用此方法连续监测应激水平,根据肾多普勒超声数据为每个个体患者创建个性化应激曲线,其中患者的阻力指数(R/1)被用来计算应激。患者的R/1和自我报告的应激水平之间存在强相关性。R/1和自我报告的应激水平之间的关系被用来生成用于计算应激指数的算法。该应激指数与从上文提及的传感器收集的生理数据流和心理数据流相关,然后确定用于根据生理、心理、行为和环境数据来计算应激指数的应激模型。美国专利申请公开2010/0022852(Westerink等人)描述了一种用于处理皮肤电反应(GSR)信号以估计用户的唤醒水平的方法。GSR传感器测量皮肤的电阻。自主神经系统的交感分支的唤醒导致汗腺活动的增加,这导致皮肤电导增加。皮肤电导因此可以是应激反应的量度。具体实施方案描述了用于处理GSR信号的计算机程序产品,该计算机程序产品在运行时控制计算机以估计唤醒水平。欧洲专利申请2586365(Sanchez Avila等人)描述了一种用于量化用户的应激的方法,其中该方法允许建立应激用户和放松用户之间的区别。该发明使用心率和GSR信号作为数据输入,并利用基于sigmoid传递函数的应激图案来允许在更大数目的情形下量化应激。美国专利申请公开2013/0281798(Rau等人)公开了用于周期性监测对象的情绪状态的方法。对象在会话(session)期间经受多种刺激,其中通过多个生理监测传感器和心理监测传感器获取数据。数据被传递到数据库,随后进行数据处理以提取关于对象的情绪状态的目标信息,具体地涉及情绪状态,包括但不限于焦虑性障碍、抑郁症、情感障碍、注意力缺陷多动障碍、自闭症谱系障碍和双相障碍。
对于可以准确地、快速地、连续地和非侵入式地量化和监测个体的应激水平的生物激发的系统和方法仍然存在大量的需要。如本文描述的,公开了用于准确地、连续地和非侵入式地量化生物应激水平的系统和方法。
发明内容
本发明是一种生理和心理量化系统,该生理和心理量化系统包括数据获取设备,该数据获取设备包括测量仪器以生成生理数据流和/或心理数据流。该数据获取设备内的微处理器将生成的数据流处理成馈送到应激函数算法内的生物度量。算法处理可以在所述数据获取设备上发生,或所述生物度量可以经由无线通信链路而被传送以供在移动设备和/或基于云的计算平台上进行外部处理。所述基于云的计算平台计算应激强度,并使用计算的应激强度和生物导出模型来描述响应于应激刺激而在身体内释放的激素和神经递质的动态。使用所述生物导出模型来量化应激激素水平,并组合使用所述应激激素水平以用作个体正在经历的急性应激和慢性应激的生物激发的度量。
例如,通过但不限于手腕带、胸带、胸部贴片、头带、上臂带、上臂贴片、植入物、可摄取的或纳米技术将所述数据获取设备附接到人类对象的身体。所述测量仪器捕获信号,例如但不限于生理信号和心理信号。不需要外部数据或数据流或预定数据或数据流,例如但不限于多普勒超声数据、心理评估数据和在监测会话期间捕获的患者数据。由所述测量仪器生成的数据流通过包含在所述数据获取设备内的微处理器处理成数字测量,且所述数字测量被进一步处理成生物度量。所述生物度量被馈送到应激函数算法,所述应激函数算法提供对良性应激水平和/或非良性应激水平的粗级预测,预测可以以应激强度(0-100%)给出。所述生物度量也可以从数据库获得以馈送到所述应激函数算法内。所述应激函数算法处理发生在所述数据获取设备上,或所述生物度量通过无线通信链路被发送到具有互联网连接的移动设备,或被发送到基于云的计算平台,以用于应激函数算法处理。粗级的应激预测告知了描述在应激时身体内释放的激素和神经递质的动态的生物数学应激模型。经由应激函数算法处理从这些生物数学应激模型估计特定应激激素水平,并计算的应激激素水平可以用作人类对象的读出。可以组合使用应激激素和神经递质估计来计算急性应激和慢性应激的生物激发的可量化的度量。通过所述无线通信链路,量化的应激度量从所述基于云的计算平台转发回到所述移动设备和/或所述数据获取设备,以用于向用户显示和/或通知他的或她的生物应激水平。量化的应激度量和/或建模的应激激素和神经递质估计也可以经由互联网通信链路而被转发到第三方数据库和/或移动设备。第三方的实例包括但不限于临床方、保险方和零售方。基于云的生物数学应激模型的压缩版本也可以被发送到所述数据获取设备,以使得能够在所述数据获取设备自身上估计应激激素和神经递质和计算可量化的应激度量。
结合附图和所附权利要求考虑以下对本发明的详细描述,其他目的、特征和优点将变得显而易见。
附图说明
图1A和图1B一起是例示了根据本发明的应激量化系统的示意性表示。
图2是SAM应激轴的示意性表示,例示了根据本发明的在基于云的计算平台上通过常微分方程建模的生物控制过程。
图3是HPA应激轴的示意性表示,例示了根据本发明的在基于云的计算平台上通过常微分方程建模的生物控制过程。
图4是根据本发明的非侵入式应激量化系统的示意性表示。
图5是例示了在特里尔社会应激测试(Trier Social Stress Test,TSST)期间获得的两个不同测试对象的测量的唾液皮质醇水平和模型预测的皮质醇水平的比较的图表。
具体实施方式
术语缩写
参考图1A和图1B,生理和心理应激量化系统100测量和预测人类对象的应激。生理和心理应激量化系统100包括数据获取设备101,该数据获取设备101通过例如但不限于手腕带、胸带,胸部贴片、头带、上臂带、上臂贴片或经由植入物、可摄取的或纳米技术连接到人类对象的身体。数据获取设备101包括能够捕获信号的测量仪器102,所述信号例如但不限于是生理信号和心理信号。生理信号可以包括但不限于心脏信号、运动信号、音频信号、肺信号、热信号、皮肤电信号和脑信号。心理信号可以包括但不限于用户反馈、主观反馈和环境反馈。参考图1A,测量仪器102捕获生理数据,包括心脏信号120(诸如光体积描记术PPG122、心电图ECG123和血压BP124)、运动信号130(诸如加速度计ACC132、磁力计MAG133以及陀螺仪GYRO 134)、肺信号140(诸如呼吸率BR 142)、热信号150(诸如温度152和热通量153)、EDA信号160(诸如皮肤电反应GSR 162)和大脑信号170(诸如脑电图EEG 172)。
进一步参考图1A,测量仪器102还捕获心理数据,包括用户反馈180(活动追踪182和API反馈183)、主观反馈190(TSST 192和TRAIT 193)和环境反馈195(人196、天气197和活动198)。
从测量仪器102获得的数据流通过微控制器103的数字信号处理DSP模块112或通过在测量仪器102内的处理而被转换为数字信号,以给出测量113。通过微控制器103的基于生物的处理模块114,测量113经历基于生物的处理,以从微控制器103生成生物度量104。用于基于生物的处理的其他数据流也可以从数据库获得,例如但不限于医学数据库和遗传数据库。在具体实施方案中,生物度量104由数据获取设备101内的微控制器103处理。在其他实施方案中,生物度量104经由无线通信链路105被发送到具有用于与基于云的计算平台106通信的互联网连接的移动设备110,例如但不限于智能手机、平板计算机或膝上型计算机。
生物度量104用作基于云的计算平台106上的应激函数算法模块107的输入。在其他实施方案中,生物度量104还可以用作数据获取设备101上的应激函数算法处理的输入。在具体实施方案中,使用生物度量104作为数据获取设备101上或基于云的计算平台106上的应激算法处理(模块107)的输入,描述了一种预测对象是否处于急性精神应激状态的广义模型。在其它实施方案中,描述了一种可以量化对象正在经历的应激水平的模型,其中考虑使用嵌入在应激函数算法模块107内的统计方法、根据从数据获取设备101获取的生物度量104和数据来提供应激强度(0-100%)的粗级预测。在优选实施方案中,从该粗级预测获取的数据被用在生物数学模型模块108中,生物数学模型模块108通过估计应激激素水平和神经递质水平来量化对象在生理学上正在经历的急性应激和慢性应激的水平,所述应激激素水平和神经递质水平包括但不限于肾上腺素、去甲肾上腺素、乙酰胆碱、CRH、ACTH、皮质醇和醛固酮。
在具体实施方案中,组合使用应激激素和神经递质估计来用作急性应激和慢性应激的生物激发的可量化的度量。量化的应激度量(诸如应激强度115)从基于云的计算平台106通过无线通信链路105被转发回到移动设备110(通信链路105的一部分)和/或数据获取设备101,以用于向用户显示和/或通知他的或她的生物急性应激水平和/或慢性应激水平。在具体实施方案中,量化的应激度量(诸如应激强度115和/或建模的应激激素和神经递质估计116)也可以经由互联网通信而被转发到第三方数据库和/或移动设备。第三方的实施例包括但不限于临床方、保险方和零售方。基于云的生物数学导出应激模型108的压缩版本也可以被发送到数据获取设备101或移动设备110,以能够在数据获取设备101上进行应激激素和神经递质估计116和计算可量化的应激度量。压缩或简化的个人应激模型也可以被传输到数据获取设备101或移动设备110。
应激函数算法
来自包含在数据获取设备101内的微控制器103的生物度量104经由无线通信链路105而被发送到具有用于与基于云的计算平台106通信的互联网连接的移动设备110,以用于通过应激函数算法模块107进行处理。在其他实施方案中,生物度量104的应激函数算法处理(模块107)发生在数据获取设备101上。在具体实施方案中,应激函数算法模块107中的应激函数算法分析从微处理器103导出的生物度量104。特别地,通过聚类分析来分析生物度量104。聚类分析是将一组对象进行分组的行为,分组的方式是使得同一组中的对象(称为聚类)彼此之间的相似程度大于与其他聚类中的对象的类似程度。聚类分析的一个实施例是k均值聚类117,其被用来将从数据获取设备101导出的测量(生物度量104)分类成不同的应激和活动水平状态。聚类分析的另一个实施例是由模糊聚类分析模块1111实施的模糊聚类。模糊逻辑是计算机逻辑的一种形式,其输出是0到1之间的值(其也可以被表示为0-100%)的连续统。该系统开始于为每个输入指派一组隶属函数并且为每个输出指派一组隶属函数。然后应用用于该隶属函数的一组规则。在具体实施方案中,该算法允许k-均值聚类(模块117)告知隶属函数的形状。模糊聚类(模块111)提供数据中的哪些特征属于一个特定聚类或状态的百分比的指示。因此,可以确定对象正在经历的应激水平(从0-100%)。模糊聚类的输出是随着应激水平上升和下降而随时间波动的应激函数。高斯混合模型(GMM)模块118是聚类模型的另一个实施例。GMM模型假设所有数据点都是由有限数目的具有未知参数的高斯分布生成的。在具体实施方案中,GMM模型通过将聚类过程和应激函数计算组合在一个模型中提供了优势。在具体实施方案中,应激旋转模型模块119被用于急性应激和慢性应激和运动事件的分类。参数绘图上的数据点的矢量方向性可以被视为环路或“旋转”。旋转测量可能对应于应激事件或运动事件。为了捕获和量化由旋转生成的信息,一种算法计算应激和运动的旋转面积。该面积被输出作为指示应激事件或运动事件的持续时间和严重程度的应激函数。此模型提供了一种相对于聚类技术的优点,该优点在于它能够同时预测急性精神应激和运动。在其他实施方案中,通过使用K均值聚类、模糊聚类GMM和应激旋转算法的组合来计算应激强度。上文提到的理论和方法被用来创建系综(ensemble)121以将应激事件分类。随后使用例如但不限于逻辑回归函数来计算应激强度,所述逻辑回归函数使用最小的一个生物度量作为输入来导引该强度。
SAM轴模型(图2)
在具体实施方案中,如从上文所描述的算法(模块107,图1B)确定的应激函数告知了图2中例示的SAM应激路径201的基于常微分方程(ODE)的模型。这些计算系统生物学导出模型(模块108,图1B)描述了响应于应激刺激而释放的激素和神经递质的动态,从而提供了对在个体的血流中循环的激素的可能水平的洞察,所述激素包括但不限于肾上腺素、去甲肾上腺素和乙酰胆碱。在文献中找到的参数输入,诸如激素受体的结合效率和激素的半衰期,连同来自如上文所描述的应激函数算法(模块107)的组合的输出,被用作描述SAM轴的ODE模型的主要输入。在具体实施方案中,此模型提供了对身体内的肾上腺素水平和去甲肾上腺素水平的估计。
HPA轴模型(图3)
在具体实施方案中,如从上文描述的应激函数算法(模块107)确定的应激函数告知了图3中例示的HPA应激路径301的基于常微分方程(ODE)的模型。这些计算系统生物学导出模型(模块108)描述了响应于应激刺激(昼夜节律+应激刺激)而释放的激素和神经递质的动态,从而提供了对在个体的血流中循环的激素的可能水平的洞察,所述激素包括但不限于促肾上腺皮质激素释放激素(CRH)、促肾上腺皮质激素(ACTH)、皮质醇和醛固酮。在文献中找到的参数输入,诸如激素受体的结合效率和激素的半衰期,连同来自如上文所描述的应激函数算法的组合的输出,被用作描述HPA轴模型的ODE模型的主要输入。在具体实施方案中,此模型提供了对身体内的CRH、ACTH、皮质醇和醛固酮水平的估计。
急性应激和慢性应激的生物激发的可量化的度量(图4)
参考图4,基于云的生物数学模型(108,201,301,402)使用估计的应激激素水平和神经递质水平作为输入来计算组合应激度量,例如但不限于个体正在经历的慢性应激和急性应激的量、应激源的持续时间、应激强度和平均应激水平。在其他实施方案中,基于云的生物数学模型的压缩版本被发送到数据获取设备405或移动设备406,以使得能够在数据获取设备405上对可量化的应激度量402进行计算和显示以及估计应激激素和神经递质。生物数学模型的输入包括但不限于HPA轴输入301、SAM轴输入201以及交感和副交感神经轴输入。在具体实施方案中,量化的度量402作为读出从基于云的计算平台401经由无线通信链路403被发送到数据获取设备405或移动设备406。在其它实施方案中,量化的度量402可以从基于云的平台401通过无线通信链路403而被转发到移动设备406,以用于向用户显示和/或通知他的或她的生物应激水平。激素和神经递质估计408包括例如急性应激度量422、慢性应激度量424、应激强度426、应激源的持续时间428和平均应激水平430。激素和神经递质估计408也可以从基于云的计算平台401和/或移动设备406经由互联网通信链路403而被转发到第三方数据库和/或第三方移动设备407。第三方407的实施例包括但不限于临床方、保险方和零售方。可以由第三方407从量化的应激度量402推断出情境信息,以获得对与患者/客户有关的生理和环境条件/刺激的洞察。
图5示出了在特里尔社会应激测试(TSST)期间获得的两个不同测试对象的测量的唾液皮质醇水平和HPA模型预测的皮质醇水平的比较的实施例。
用户实施例1
在本发明的一个实施方案中,数据获取设备405收集来自穿戴数据获取设备405的人类对象的生理信号。该对象经由与数据获取设备405或移动设备406的接口的简短交互来提供应激事件404(图4)的情境。情境的实施例包括:与患者交互的人、会议、社会环境或可能与应激状态的发生相关的任何其他信息。该对象每天、每周和每月接收对他的或她的应激水平的统计。应激水平的实施例可以包括但不限于平均应激水平和强度以及应激源的持续时间,以及可量化的应激度量402。用户提供的情境越多,提供给用户的相关信息可以越多。临床第三方407可以访问第三方数据库和/或移动设备406上的情境数据以做出生理和环境推断432(图4)从而帮助开具相关药物的处方,改善当前处方药物,诊断新的和/或未知的应激源,获得对用户的整体生活方式和健康的额外洞察以便改善预后。图5示出了在特里尔社会应激测试(TSST)期间获得的两个不同测试对象的测量的唾液皮质醇水平412和模型预测的皮质醇水平414的比较。图5中的区域416指示使用应激旋转算法检测到应激的位置。区域418指示经历的高应激强度。线420指示心率,实心点412示出了测量的皮质醇水平,且线414表示用HPA轴模型预测的皮质醇水平414。
用户实施例2
在本发明的一个实施方案中,数据获取设备405收集精神病学或心理学患者的生理信号并且推断可能的生物应激的周期。代替数据获取设备405和生理量化系统400,典型地将这样的应激体验作为精神病学/心理学的会话的一部分进行分析,其中患者在事实之后几周(如果某事真会发生的话)回忆围绕事件的情境以帮助临床医生朝向最佳治疗方案。用本发明的生理应激量化系统400,应激状态可以由用户验证或质疑,且可以在同一天晚些时候在如由数据获取设备405(图4)识别的应激较少环境下使用与数据获取设备405有联系的移动设备406的简短交互或直接通过数据获取设备405的接口收集围绕应激状态的情境。这具有以一种不可能代替本发明的方式来捕获围绕应激事件的发生和情境404的双重优点。情境的实施例包括:与患者互动的人、会议、社会环境或可能与应激状态的发生相关的任何其他信息。
虽然已经参考本发明的优选实施方案描述了本发明,但是应理解,可以在本文描述的和所附权利要求中描述的本发明的精神和范围内做出变化和修改。
Claims (20)
1.一种用于非侵入式生理和心理量化人类对象的应激水平的系统,包括以下:
a.一个数据获取设备,包括:
i.一个非侵入式仪器,用于从所述人类对象获取生理和心理数据;
ii.一个微控制器,用于将获取的生理和心理数据处理成生物度量;
b.一个通信链路;以及
c.一个计算平台,其被连接到所述通信链路,用于:
i.从所述数据获取设备接收所述生物度量;以及
ii.通过应激函数算法模块将所述生物度量处理成可量化的应激度量,以确定所述人类对象经历的应激水平。
2.根据权利要求1所述的系统,其中所述测量仪器选自一组数据获取设备,该组数据获取设备包括植入设备、可摄取设备、纳米技术设备、胸带、胸部贴片、头带、上臂带、上臂贴片、手腕带、手指带、手指贴片、臂套和腿套。
3.根据权利要求1所述的系统,其中所述算法模块选自包括K均值聚类分析模块、模糊聚类模块、高斯混合模型模块、应激旋转模型模块、以及所述K-均值聚类分析模块、所述模糊聚类模块、所述高斯混合模型模块和所述应激旋转模型模块的系综的一组。
4.根据权利要求1所述的系统,其中所述计算平台还基于所述生物度量来计算所述人类对象释放的激素或神经递质的估计,且其中所述对象所释放的激素或神经递质的所述估计与所述人类对象经历的应激水平相关。
5.根据权利要求4所述的系统,其中所述计算平台使用所述生物度量来估计在激活SAM应激轴期间所述人类对象释放的激素和/或神经递质。
6.根据权利要求4所述的系统,其中所述计算平台使用所述生物度量来估计在激活HPA应激轴期间所述人类对象释放的激素和/或神经递质。
7.根据权利要求4所述的系统,其中所述人类对象经历的应激水平包括平均应激水平、急性应激水平、慢性应激水平、应激强度、应激源持续时间、非良性应激水平和良性应激水平。
8.根据权利要求1所述的系统,其中所述计算平台是基于云的计算平台,所述基于云的计算平台通过所述通信链路从所述获取设备的所述微控制器接收所述生物度量。
9.根据权利要求1所述的系统,其中所述系统包括第三方数据库,且量化的应激度量通过所述通信链路发送到所述第三方数据库。
10.根据权利要求1所述的系统,用于从所述生物度量确定可量化的应激度量的所述计算平台存在于通过所述通信链路连接到所述数据获取设备的一个移动设备上。
11.根据权利要求1所述的系统,用于从所述生物度量确定可量化的应激度量的所述计算平台存在于所述数据获取设备上。
12.一种用于非侵入式生理和心理量化人类对象的应激水平的方法,包括以下:
a.通过数据获取设备的非侵入式仪器从所述人类对象获取生理和心理数据;
b.经由通信链路将所获取的生理和心理数据发送到计算平台;
c.从所述数据获取设备接收所获取的生理和心理数据;以及
d.通过应激函数算法模块将所获取的生理和心理数据处理成可量化的应激度量,以确定所述人类对象经历的应激水平。
13.根据权利要求1所述的方法,其中所获取的生理和心理数据:
a.通过所述数据获取设备的微处理器转换成生物度量;
b.从所述数据获取设备接收所述生物度量;以及
c.通过应激函数算法将所述生物度量处理成可量化的应激度量,以确定所述人类对象经历的应激水平。
14.根据权利要求12所述的方法,其中所述测量仪器选自一组数据获取设备,该组数据获取设备包括植入设备、可摄取设备、纳米技术设备、胸带、胸部贴片、头带、上臂带、上臂贴片、手腕带、手指带、手指贴片、臂套和腿套。
15.根据权利要求12所述的方法,其中所述算法选自包括K均值聚类分析、模糊聚类、高斯混合模型、应激旋转模型、以及所述K-均值聚类分析、所述模糊聚类、所述高斯混合模型和所述应激旋转模型的系综的一组。
16.根据权利要求12所述的方法,其中处理步骤还基于所述生物度量来计算所述人类对象释放的激素或神经递质的估计,且其中所述人类对象释放的激素或神经递质的估计与所述人类对象经历的应激水平有关。
17.根据权利要求16所述的方法,其中处理步骤使用所述生物度量来估计在激活SAM应激轴期间所述人类对象释放的激素和/或神经递质。
18.根据权利要求16所述的方法,其中处理步骤使用所述生物度量来估计在激活HPA应激轴期间所述人类对象释放的激素和/或神经递质。
19.根据权利要求16所述的方法,其中所述人类对象经历的应激水平包括平均应激水平、急性应激水平、慢性应激水平、应激强度、应激源持续时间、非良性应激水平和良性应激水平。
20.根据权利要求12所述的方法,其中应激水平通过所述通信链路传送到第三方数据库。
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CN111714143A (zh) * | 2020-06-12 | 2020-09-29 | 京东数字科技控股有限公司 | 心理状态测评方法、装置、设备和计算机可读存储介质 |
CN112244792A (zh) * | 2020-10-30 | 2021-01-22 | 北京应激魔方数字科技有限公司 | 多维应激管理系统及方法 |
CN113712559A (zh) * | 2021-09-06 | 2021-11-30 | 中国人民解放军军事科学院军事医学研究院 | 一种人类应激负荷测量方法及其应用 |
CN113712559B (zh) * | 2021-09-06 | 2024-04-12 | 中国人民解放军军事科学院军事医学研究院 | 一种人类应激负荷测量方法及其应用 |
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EP3370601B1 (en) | 2022-08-10 |
WO2017078759A1 (en) | 2017-05-11 |
EP3370601A1 (en) | 2018-09-12 |
EP3370601A4 (en) | 2019-06-19 |
US20170127993A1 (en) | 2017-05-11 |
US11129568B2 (en) | 2021-09-28 |
US20170181700A1 (en) | 2017-06-29 |
IL259102A (en) | 2018-06-28 |
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