Eric J. Manders

The SIMON project: model-based signal acquisition, analysis, and interpretation in intelligent patient monitoring

The authors describe SIMON (signal interpretation and monitoring), an approach which combines static domain-specific information, which relates variables and alarm events, with dynamic information provided by a model. It is currently being tested for the monitoring of neonates in the intensive care unit. The model component is responsible for estimating the state of the monitored system, predicting the evolution of the systems variables and parameters, and establishing a monitoring contest. This information is then used by the DA (data abstraction) and the data acquisition modules to plan a monitoring strategy to filter, rank, and abstract incoming data. Faults and artifact models included in the DA permit the low-level detection of noise-contaminated episodes. The adaptation of the monitoring strategy to these changes in the environment effectively shields the model from untrustworthy information and thus increases the reliability and robustness of the system. The scheduling mechanism included in the DA permits a continuous evaluation of the system load as well as an ability to process all its tasks.<<ETX>>

SIMON: A distributed computer architecture for intelligent patient monitoring

Abstract Intelligent real-time patient monitoring encompasses data acquisition and reduction, sensor validation, diagnosis, therapy advice, and selective display of information. This paper describes the architecture and the functionality of a prototype intelligent patient monitoring system, named SIMON, designed to meet these requirements. In SIMON, the various aspects of a monitoring task are performed by three semi-independent modules running asynchronously: the feature extraction, the patient model, and the display modules. Central to SIMON is the notion of context sensitivity which permits (a) the adaptation of the monitoring strategy in response to changes either in the patient state or in the monitoring equipment and (b) the contextual interpretation of incoming data. SIMON is currently applied to the task of monitoring newborn infants with respiratory distress syndrome (RDS) and undergoing assisted ventilation.

Model-based software tools for integrated vehicle health management

Present day IVHM systems are often constructed using hand-written code that is hard to produce and difficult to verify and maintain. In this paper we introduce a suite of model-based tools that allow for the construction of embeddable IVHM applications using a model-based approach. Reusable (and potentially validated) reasoners are used in conjunction with executable code that is generated from models, thus allowing the integration of the reasoner as a component into a larger on-board system. This paper describes the toolsuite, the modeling approach used, the runtime environment, and some of the applications where the tools were used

Data acquisition for an intelligent bedside monitoring system

The authors have developed a software architecture for intelligent monitoring tasks at the bedside in critical care environments (SIMON). This architecture includes a data acquisition module. The authors have modeled their design of this component on the concepts proposed by the IEEE P1073 standardization committee. It should be seen as a bridge towards an environment based on that standard. The authors have designed their system to be able to acquire all data available from all bedside instruments to allow signal analysis at the lowest level. The authors are using SIMON on-line in the Cardiac Care Unit at Vanderbilt University Medical Center.

Design of a dynamically reconfigurable critical care monitor

A methodology and implementation are presented for a critical care monitor which is capable of dynamic reconfiguration of data abstraction strategies using contextual information. The goal is to provide the clinician with abstractions of the data that closely match the objectives of data interpretation in relation to the current clinical questions. A prototype with basic functionality is being tested in the coronary care unit at Vanderbilt University Medical Center. The monitoring system is integrated with the patient care information system to obtain clinical contextual information on the monitored patient.

INTEGRATED SYSTEMS HEALTH MANAGEMENT TO ACHIEVE AUTONOMY IN COMPLEX SYSTEMS

Abstract Integrated Systems Health Management (ISHM) provides the ability to maintain system health and performance over the life of a system. For safety-critical systems, ISHM must maintain safe operations while increasing availability by preserving functionality and minimizing downtime. This paper discusses a model-based approach to ISHM that combines fault detection, isolation and identification, fault-adaptive control, and prognosis into a common framework. At the core of this framework are a set of component oriented physical system models. By incorporating physics of failure models into component models the dynamic behavior of a failing or degrading system can be derived by simulation. Current state information predicts future behavior and performance of the system to guide decision making on system operation and maintenance. We demonstrate our approach on a pump that is part of a Water Recovery System used for NASA applications.

Online Hierarchical Fault-Adaptive Control for Advanced Life Support Systems

This paper discusses a hierarchical online fault-adaptive control approach for Advanced Life Support (ALS) Systems. ALS systems contain a number of complex interacting subsystems. To avoid complexity in the models and online analysis, diagnosis and fault-adaptive control is achieved by local units. To maintain overall performance, the problem of resource management for contending concurrent subsystems has to be addressed. We implement a control structure, where predefined setpoint specifications for system operation are used to derive optimizing utility functions for the subsystem controllers. We apply this approach in situations where a fault occurs in a system, and once the fault is isolated and identified, the controllers use the updated system model to derive new set point specifications and utility functions for the faulty system.

Hierarchical Online Control Design for Autonomous Resource Management in Advanced Life Support Systems

This paper presents a distributed, hierarchical control scheme for autonomous resource management in complex embedded systems that can handle dynamic changes in resource constraints and operational requirements. The developed hierarchical control structure handles the interactions between subsystem and system-level controllers. A global coordinator at the root of the hierarchy ensures resource requirements for the duration of the mission are not violated. We have applied this approach to design a three-tier hierarchical controller for the operation of a lunar habitat that includes a number of interacting life support components.

Model-based signal analysis and interpretation in the intensive care unit

This paper describes the architecture and functionality of a distributed intelligent signal analysis and interpretation system developed for the monitoring of neonates in the intensive care unit. In this system, named SIMON, various aspects of a monitoring task are performed by three independent modules running asynchronously: the feature extraction, the patient model, and the display modules. Central to SIMON is the notion of context sensitivity which permits the adaptation of the monitoring strategy in response to changes either in the patient state or in the monitoring equipment as well as the contextual interpretation of incoming data.

Integrated Systems Health Management to Achieve Autonomy in Complex Systems

: Integrated Systems Health Management (ISHM) provides the ability to maintain system health and performance over the life of a system. For safety-critical systems, ISHM must maintain safe operations while increasing availability by preserving functionality and minimizing downtime. This paper discusses a model-based approach to ISHM that combines fault detection, isolation and identification, fault-adaptive control, and prognosis into a common framework. At the core of this framework are a set of component oriented physical system models. By incorporating physics of failure models into component models the dynamic behavior of a failing or degrading system can be derived by simulation. Current state information predicts future behavior and performance of the system to guide decision making on system operation and maintenance. We demonstrate our approach on a pump that is part of a Water Recovery System used for NASA applications.