APPARATUS AND METHODS FOR COMPUTING CARDIAC OUTPUT OF A LIVING SUBJECT VIA APPLANATION TONOMETRY
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    发明申请
    APPARATUS AND METHODS FOR COMPUTING CARDIAC OUTPUT OF A LIVING SUBJECT VIA APPLANATION TONOMETRY 有权
    用于计算生活物质的心脏输出的装置和方法

    公开(公告)号:US20140275937A1

    公开(公告)日:2014-09-18

    申请号:US13829061

    申请日:2013-03-14

    IPC分类号: A61B5/00 A61B5/029 A61B3/16

    摘要: Apparatus and methods for calculating cardiac output (CO) of a living subject using applanation tonometry measurements. In one embodiment, the apparatus and methods build a nonlinear mathematical model to correlate physiologic source data vectors to target CO values. The source data vectors include one or more measurable or derivable parameters such as: systolic and diastolic pressure, pulse pressure, beat-to-beat interval, mean arterial pressure, maximal slope of the pressure rise during systole, the area under systolic part of the pulse pressure wave, gender (male or female), age, height and weight. The target CO values are acquired using various methods, across a plurality of individuals. Multidimensional nonlinear optimization is then used to find a mathematical model which transforms the source data to the target CO data. The model is then applied to an individual by acquiring physiologic data for the individual and applying the model to the collected data.

    摘要翻译: 使用压平眼压测量法计算活体的心输出量(CO)的装置和方法。 在一个实施例中,装置和方法构建非线性数学模型以将生理源数据向量与目标CO值相关联。 源数据向量包括一个或多个可测量或可推定的参数,例如:收缩压和舒张压,脉搏压,搏动间隔,平均动脉压,心脏收缩压升高的最大斜率, 脉压波,性别(男性或女性),年龄,身高和体重。 使用各种方法在多个个体中获取目标CO值。 然后使用多维非线性优化来找到将源数据转换为目标CO数据的数学模型。 然后通过获取个体的生理数据并将模型应用于收集的数据,将该模型应用于个体。

    APPARATUS AND METHODS FOR COMPUTING CARDIAC OUTPUT OF A LIVING SUBJECT VIA APPLANATION TONOMETRY

    公开(公告)号:US20190326017A1

    公开(公告)日:2019-10-24

    申请号:US15960379

    申请日:2018-04-23

    摘要: Apparatus and methods for calculating cardiac output (CO) of a living subject using applanation tonometry measurements. In one embodiment, the apparatus and methods build a nonlinear mathematical model to correlate physiologic source data vectors to target CO values. The source data vectors include one or more measurable or derivable parameters such as: systolic and diastolic pressure, pulse pressure, beat-to-beat interval, mean arterial pressure, maximal slope of the pressure rise during systole, the area under systolic part of the pulse pressure wave, gender (male or female), age, height and weight. The target CO values are acquired using various methods, across a plurality of individuals. Multidimensional nonlinear optimization is then used to find a mathematical model which transforms the source data to the target CO data. The model is then applied to an individual by acquiring physiologic data for the individual and applying the model to the collected data.