NICU Automatic Oxygen Control with Parameter and Disturbance Estimation
NICU 自动氧气控制,具有参数和干扰估计
基本信息
- 批准号:9979510
- 负责人:
- 金额:$ 22.66万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-06-01 至 2022-05-31
- 项目状态:已结题
- 来源:
- 关键词:AffectAlgorithmsBiological ModelsCaringCharacteristicsClinicalClinical DataClinical ResearchClinical TrialsDataDevelopmentDevicesEnsureEquipmentEventFinancial compensationFoundationsFundingGoalsLegal patentLiteratureLungManualsMeasurementMethodsModelingMonitorNursesOutcomeOxygenPatient CarePatientsPerformancePeripheralPhysiciansPhysiologicalPremature InfantProcessReaction TimeRecording of previous eventsResearchResearch PersonnelRespiratory SystemSamplingSiteStatistical MethodsSupport SystemSystemSystems AnalysisTechnologyTestingTimeTrainingUncertaintyVariantWorkbasedesigndiscrete timehuman subjectimprovedinnovationneonatenew technologynovelpatient variabilityresearch clinical testingrespiratoryresponsetime use
项目摘要
PROJECT SUMMARY/ABSTRACT
The goal of this research is to demonstrate a novel adaptive oxygen control system which will
improve the control of oxygen saturation in premature infants who are receiving respiratory
support due to underdeveloped lungs. In this work, a clinical study at two sites will be conducted
to demonstrate an oxygen control device which is able to continuously adjust oxygen
automatically. The study is an equivalence crossover to demonstrate that the device performs at
least as well as a trained NICU nurse in limiting fluctuations in FiO2 and maintaining SpO2 within
specific parameters prescribed by the treating physician. If there is sufficient evidence, superiority
will be investigated. In addition, the study will yield results that will characterize the performance
of manual and automatic control alternatives that can be compared.
This work is part of an ongoing effort to develop a new technology for controlling oxygen in
respiratory support systems for premature infants. The key outcome is clinical data that will show
how automatic control of oxygen in premature infants affects the accuracy of control of the oxygen
saturation level compared to manual control. The impact of this work is that a novel automatic
control system will be developed and tested which will improve the consistency of patient care by
automatically adapting the control algorithm to each patient and will lead to a better understanding
of the dynamics of the response of neonates to oxygen control.
The new patented oxygen control technology uses a parameter estimating extended Kalman
filter (PE-EKF) that uses the time history of measurements to estimate the dynamic model
parameters of the patients so that the oxygen control system can adapt to a wide range of
patient characteristics and conditions. A disturbance estimator also estimates the disturbance
level due to unmodeled inputs which cause adverse changes in oxygen saturation. Disturbance
estimation allows the control system to quickly quantify the disturbance level and respond by
manipulating inspired oxygen to cancel out disturbances that cause desaturation events. The
new developments in modeling the system, disturbance estimator, and PE-EKF allow real-time
adaptation of the oxygen control system to the changes in the patient during early development
and the onset physiological changes. In this work, researchers will be investigating an
advanced oxygen control system with a design based on the performance observed during
clinical testing and analysis of the system dynamics to further improve the performance.
项目总结/摘要
本研究的目的是展示一种新的自适应氧气控制系统,
改善正在接受呼吸机治疗的早产儿的氧饱和度控制
由于肺部发育不全而无法支撑。在这项工作中,将在两个地点进行临床研究
为了演示能够连续调节氧气的氧气控制装置,
的自动本研究是一项等同性交叉研究,旨在证明器械在以下条件下的性能:
在限制FiO 2波动和将SpO 2维持在
治疗医师规定的特定参数。如果有足够的证据,
将进行调查。此外,该研究将产生的结果,将表征性能
手动和自动控制的替代方案,可以比较。
这项工作是正在进行的努力的一部分,以开发一种新的技术,用于控制氧气在
早产儿呼吸支持系统。关键的结果是临床数据,
早产儿氧气自动控制如何影响氧气控制的准确性
与手动控制相比的饱和度。这项工作的影响是,
将开发和测试一个控制系统,该系统将通过以下措施提高病人护理的一致性
自动调整控制算法,以适应每个病人,并将导致更好的理解
新生儿对氧气控制的反应动力学。
新的专利氧气控制技术使用参数估计扩展卡尔曼
使用测量的时间历史来估计动态模型的滤波器(PE-EKF
因此,氧气控制系统可以适应患者的各种参数,
患者的特征和条件。扰动估计器也估计扰动
由于未建模的输入导致氧饱和度的不利变化。扰动
估计允许控制系统快速量化干扰水平,并通过
操纵吸入的氧气以抵消引起去饱和事件的干扰。的
在系统建模、干扰估计器和PE-EKF方面的新发展允许实时
氧气控制系统在早期发育期间适应患者的变化
以及生理变化的开始。在这项工作中,研究人员将调查一种
先进的氧气控制系统,其设计基于
临床测试和系统动力学分析,以进一步提高性能。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Ramak R Amjad其他文献
Ramak R Amjad的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
相似海外基金
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
- 批准号:
EP/Y029089/1 - 财政年份:2024
- 资助金额:
$ 22.66万 - 项目类别:
Research Grant
CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
- 批准号:
2337776 - 财政年份:2024
- 资助金额:
$ 22.66万 - 项目类别:
Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
- 批准号:
2338816 - 财政年份:2024
- 资助金额:
$ 22.66万 - 项目类别:
Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
- 批准号:
2338846 - 财政年份:2024
- 资助金额:
$ 22.66万 - 项目类别:
Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
- 批准号:
2348261 - 财政年份:2024
- 资助金额:
$ 22.66万 - 项目类别:
Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
- 批准号:
2348346 - 财政年份:2024
- 资助金额:
$ 22.66万 - 项目类别:
Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
- 批准号:
2348457 - 财政年份:2024
- 资助金额:
$ 22.66万 - 项目类别:
Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
- 批准号:
2404989 - 财政年份:2024
- 资助金额:
$ 22.66万 - 项目类别:
Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
- 批准号:
2339310 - 财政年份:2024
- 资助金额:
$ 22.66万 - 项目类别:
Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
- 批准号:
2339669 - 财政年份:2024
- 资助金额:
$ 22.66万 - 项目类别:
Continuing Grant