Estimation of Blood Oxygen Content Using Context-Aware Filtering

Ivanov, Radoslav; Atanasov, Nikolay; Weimer, James; Pajić, Miroslav; Simpao, Allan F.; Rehman, Mohamed A.; Pappas, George J.; Lee, Insup

doi:10.1109/iccps.2016.7479102

Cited by 4 publications

(13 citation statements)

References 21 publications

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“…Consider O 2 content estimation in arteries using only the noninvasive binary pulmonary measurements, where the framework of blood dynamic in the lung is shown in Figure . According to the diffusion process described in this figure, the dynamic physiological model for the arterial O 2 content can be derived from the oxygen content equation and the alveolar gas equation as:

x (t + 1) = f x (t) + w (t) + U (t),

where

U (t) = (1 - f) (1.34 H b + 0.003 (c_{1} u (t) + c_{2} (t) e (t))) - f μ

, and c 2 ( t )=[1− u ( t )(1− RQ )]/ RQ . The state x ( t ) is the arterial O 2 content, while u ( t ) is the percent of O 2 in inhaled air (as input by clinicians).…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Consider O 2 content estimation in arteries using only the noninvasive binary pulmonary measurements, where the framework of blood dynamic in the lung 9 is shown in Figure 1. According to the diffusion process described in this figure, the dynamic physiological model for the arterial O 2 content can be derived from the oxygen content equation and the alveolar gas equation as 8 :…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Remark The estimation problems addressed in the work of Koutsoukos et al and Ivanov et al assumed that the probability of outputs at a given state is known for binary sensors. However, this assumption is not reasonable in practical applications.…”

Section: Problem Statementmentioning

confidence: 99%

“…For example, the estimation of hybrid systems was addressed under binary sensors in the work of Koutsoukos . Ivanov et al studied the context‐aware filter for systems with binary sensors to improve estimation accuracy. Apparently, the Gaussian distribution approximation of posterior distribution need to be satisfied for Bayesian approach, which is a strict condition for estimators.…”

Section: Introductionmentioning

confidence: 99%

“…Ivanov et al studied the context‐aware filter for systems with binary sensors to improve estimation accuracy. Apparently, the Gaussian distribution approximation of posterior distribution need to be satisfied for Bayesian approach, which is a strict condition for estimators. In most of the above‐mentioned research works, the raw measurements from multiple sensors are augmented to design estimators in a centralized fusion structure.…”

Section: Introductionmentioning

confidence: 99%

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Distributed fusion Kalman filtering under binary sensors

Zhang

Chen

2020

Intl J Robust & Nonlinear

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Summary Binary sensors are special sensors that only transmit one‐bit information at each time and have been widely applied to environmental awareness and medical monitoring. This paper is concerned with the distributed fusion Kalman filtering problem for a class of binary sensor systems. A novel uncertainty approach is proposed to better extract valid information from binary sensors at switching instant. By minimizing a local estimation error covariance, the local robust Kalman estimates are firstly obtained. Then, the distributed fusion Kalman filter is designed by resorting to the covariance intersection fusion criterion. Finally, a blood oxygen content model is employed to show the effectiveness of the proposed methods.

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x (t + 1) = f x (t) + w (t) + U (t),

where

U (t) = (1 - f) (1.34 H b + 0.003 (c_{1} u (t) + c_{2} (t) e (t))) - f μ

, and c 2 ( t )=[1− u ( t )(1− RQ )]/ RQ . The state x ( t ) is the arterial O 2 content, while u ( t ) is the percent of O 2 in inhaled air (as input by clinicians).…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Distributed fusion Kalman filtering under binary sensors

Zhang

Chen

2020

Intl J Robust & Nonlinear

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Context-Aware Detection in Medical Cyber-Physical Systems

Ivanov

Weimer

Lee

2018

2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)

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This paper considers the problem of incorporating context in medical cyber-physical systems (MCPS) applications for the purpose of improving the performance of MCPS detectors. In particular, in many applications additional data could be used to conclude that actual measurements might be noisy or wrong (e.g., machine settings might indicate that the machine is improperly attached to the patient); we call such data context. The first contribution of this work is the formal definition of context, namely additional information whose presence is associated with a change in the measurement model (e.g., higher variance). Given this formulation, we developed the context-aware parameter-invariant (CA-PAIN) detector; the CA-PAIN detector improves upon the original PAIN detector by recognizing events with noisy measurements and not raising unnecessary false alarms. We evaluate the CA-PAIN detector both in simulation and on realpatient data; in both cases, the CA-PAIN detector achieves roughly a 20-percent reduction of false alarm rates over the PAIN detector, thus indicating that formalizing context and using it in a rigorous way is a promising direction for future work.

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Continuous Estimation Using Context-Dependent Discrete Measurements

Ivanov

Atanasov

Pajić

et al. 2019

IEEE Trans. Automat. Contr.

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This paper considers the problem of continuous state estimation from discrete context-based measurements. Context measurements provide binary information as obtained from the system's environment, e.g., a medical alarm indicating that a vital sign is above a certain threshold. Since they provide state information, these measurements can be used for estimation purposes, similar to standard continuous measurements, especially when standard sensors are biased or attacked. Context measurements are assumed to have a known probability of occurring given the state; in particular, we focus on the probit function to model threshold-based measurements, such as the medical-alarm scenario. We develop a recursive context-aware filter by approximating the posterior distribution with a Gaussian distribution with the same first two moments as the true posterior. We show that the filter's expected uncertainty is bounded when the probability of receiving context measurements is lower bounded by some positive number for all system states. Furthermore, we provide an observability-like result-all eigenvalues of the filter's covariance matrix converge to 0 after repeated updates if and only if a persistence of excitation condition holds for the context measurements. Finally, in addition to simulation evaluations, we applied the filter to the problem of estimating a patient's blood oxygen content during surgery using real-patient data.

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Estimation of Blood Oxygen Content Using Context-Aware Filtering

Cited by 4 publications

References 21 publications

Distributed fusion Kalman filtering under binary sensors

Distributed fusion Kalman filtering under binary sensors

Context-Aware Detection in Medical Cyber-Physical Systems

Continuous Estimation Using Context-Dependent Discrete Measurements

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