Method and apparatus for determining critical care parameters

A physiological parameter and critical technology, applied in the field of physiological measurement system, can solve the problems such as OD that no one raises

Inactive Publication Date: 2011-12-14
BODYMEDIA +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0019]However, to the best of our knowledge, no one has proposed the use of continuous or semi-continuous lactate sampling to produce high-fidelity, high-precision measures of OD that could be used to replace methods such as indirect Typical measures of OD by calorimetry and indirect Fick method
Nor has anyone suggested the use of OD determination with this method as a guide for treatment and resource allocation or as a method for triage or medical / surgical management of diseases that result in an imbalance between oxygen delivery and utilization

Method used

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  • Method and apparatus for determining critical care parameters
  • Method and apparatus for determining critical care parameters
  • Method and apparatus for determining critical care parameters

Examples

Experimental program
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example 1

[0322] The following data, shown in FIGS. 40A-40H , illustrate how the severity of an LBNP (lower body negative pressure as described above) protocol (or exercise protocol) affects armband sensor values. For each graph, the X-axis represents stages of severity: stage 0 is the baseline stage, and the remaining stages gradually increase in severity. The Y-axis in these graphs represents the units for the particular sensor described in the graph. (For example, in the first graph of COVER (ambient temperature), the units are degrees Celsius).

[0323] Each point in the graph is the average of all minutes below that particular phase averaged across all subjects (there were a total of 28 subjects undergoing the LBNP protocol and there were a total of 14 subjects participating in the exercise protocol). Figure 40A is a measurement of ambient temperature (COVER); Figure 40B is a measure of galvanic skin response (GSR); Figure 40C is a measure of heat flux (HF); Figure 40D is a measu...

example 2

[0325] The following data, shown in Figures 41A and 41B, represent typical characteristics of armband signals for the LBNP protocol. Each grid consists of 6 columns: each column represents armband signal (from left to right - HR (heart rate), ECGMAD (mean absolute difference of raw ECG signal collected by armband), HF (heat flux), SKIN (skin ) temperature; HR (heart rate variability); and GSR

[0326] (galvanic skin response). Each row of the grid represents a specific object. The first row has all diagrams for object 180, the second row has all diagrams for object 181 and so on. The X-axis in each graph represents a protocol duration of approximately 40 minutes (each phase is approximately 5 minutes long, and subjects progress on average to Phase 6—resulting in 30 minutes on the X-axis + 5 minutes at baseline + recovered 5 minutes). The Y-axis represents the value in the corresponding unit of the armband variable in question (for example, for SKIN - the Y-axis represents ...

example 3

[0328] The classifier for detecting hemorrhagic shock was designed in two layers. The first layer makes a distinction between LBNP and exercise. Once this distinction is made, the second layer of the classifier decides the severity of LBNP. Detection of severe LBNP levels is similar to detection of hemorrhagic shock.

[0329] For the first layer of the classifier: energy expenditure, heart rate and GSR were progressively increased in both LBNP and exercise protocols as there was an increase in severity. However, accelerometer values ​​behave differently for the two protocols. Even for supine and other low-motion-related exercises such as supine cycling, increased exercise volume was observed in the accelerometer variables, whereas during LBNP the accelerometer variables remained static throughout the duration. This is a clear indication that EE, GSR etc. are increasing despite no movement.

[0330] Tables 6 and 7 illustrate the results of the classifiers. These tables rep...

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Abstract

A physiological measuring system is disclosed that monitors certain physiological parameters of an individual through the use of a body-mounted sensing apparatus. The apparatus is particularly adapted for continuous wear. The system is also adaptable or applicable to calculating derivations of such parameters. A oxygen debt measuring embodiment is directed predicting an outcome in response to injury and illness. The technique allows for closed-loop resuscitation, early identification of illness and early corrective action.

Description

[0001] Cross References to Related Applications [0002] This application is a continuation-in-part of U.S. Application Serial No. 11 / 928,302, filed October 30, 2007, which is a continuation-in-part of U.S. Application Serial No. 10 940,889, filed September 13, 2004, which issued as U.S. Patent No. 7,502,643 Continuing, U.S. Patent No. 7,502,643 requires U.S. Provisional Application Serial No. 60 / 502,764, filed September 12, 2003; U.S. Provisional Application Serial No. 60 / 510,013, filed October 9, 2003; and U.S. Provisional Application Benefit of Application Serial No. 60 / 555,280. This application is a continuation-in-part of U.S. Patent Application Serial No. 10 / 940,214 filed September 13, 2004, which is a continuation-in-part of U.S. Patent Application Serial No. 10 / 638,588 filed August 11, 2003, U.S. Patent Serial No. 10 / 638,588 is a continuation of U.S. Application Serial No. 09 / 602,537, filed June 23, 2000, issued as U.S. Patent No. 6,605,038, which is a continuation of ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/02A61B5/0205A61B5/00
CPCA61B5/002A61B5/4519A61B5/7207A61B5/413A61B5/721A61N1/37252A61B5/0205A61B5/7267A61B5/0002A61B5/412A61B5/0022G16H40/67
Inventor K.沃德D.安德里S.K.贝姆克J.费林顿J.加斯巴罗C.卡萨巴赫C.帕焦内R.佩勒捷K.罗斯S.萨菲耶J.M.斯蒂沃里克E.特勒S.维什努巴特拉N.维亚斯G.科瓦奇
Owner BODYMEDIA
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