Abstract: Ambulatory or in-hospital monitoring of patients is provided with early warning and prioritization, enabling proactive intervention and amelioration of both costs and risks of health care. Multivariate physiological parameters are estimated by empirical model to remove normal variation. Residuals are tested using a multivariate probability density function to provide a multivariate health index for prioritizing medical effort.

Abstract: Ambulatory monitoring of human health is provided by a multi-component multi-sensor wireless wearable biosignal acquisition system comprising a torso device and a peripheral device communicating wirelessly, and a mobile phone for receiving collected data and uploading it over cellular network or WiFi to a remote computer for multivariate analysis. Biosignals include EKG and PPG, from which a determination of pulse transit time can be made.

Abstract: Ambulatory monitoring of human health is provided by a multi-component multi-sensor wireless wearable biosignal acquisition system comprising a torso device and a peripheral device communicating wirelessly, and a mobile phone for receiving collected data and uploading it over cellular network or WiFi to a remote computer for multivariate analysis. Biosignals include EKG and PPG, from which a determination of pulse transit time can be made.

Abstract: Efficient management of patient health of a population of patients, such as chronically ill patients living at home and monitored with remote continuous wearable or implantable physiology telemetry, is provided by means of a computer application for rendering a prioritized list of patients sortable according to a number of distinct criteria.

Abstract: Ambulatory monitoring of human health is provided by a multi-component multi-sensor wireless wearable biosignal acquisition system comprising a torso device and a peripheral device communicating wirelessly, and a mobile phone for receiving collected data and uploading it over cellular network or WiFi to a remote computer for multivariate analysis. Biosignals include EKG and PPG, from which a determination of pulse transit time can be made.

Abstract: Ambulatory or in-hospital monitoring of patients is provided with early warning and prioritization, enabling proactive intervention and amelioration of both costs and risks of health care. Multivariate physiological parameters are estimated by empirical model to remove normal variation. Residuals are tested using a multivariate probability density function to provide a multivariate health index for prioritizing medical effort.

Abstract: Ambulatory or in-hospital monitoring of patients is provided with early warning and prioritization, enabling proactive intervention and amelioration of both costs and risks of health care. Multivariate physiological parameters are estimated by empirical model to remove normal variation. Residuals are tested using a multivariate probability density function to provide a multivariate health index for prioritizing medical effort.

Abstract: A system, method and program product for monitoring a complex signal for ultrasensitive detection of state changes, or for signature recognition and classification is provided. A complex signal is decomposed periodically for empirical modeling. Wavelet analysis, frequency band filtering or other methods may be used to decompose the complex signal into components. A library of signature data may be referenced for selection of a recognized signature in the decomposed complex signal. The recognized signature may indicate data being carried in the complex signal. Estimated signal data may be generated for determination of an operational state of a monitored process or machine using a statistical hypothesis test with reference to the decomposed input signal.

Abstract: Ambulatory or in-hospital monitoring of patients is provided with early warning and prioritization, enabling proactive intervention and amelioration of both costs and risks of health care. Multivariate physiological parameters are estimated by empirical model to remove normal variation. Residuals are tested using a multivariate probability density function to provide a multivariate health index for prioritizing medical effort.

Abstract: A method and apparatus are provided for diagnosing faults in a monitored system. Estimates of parameter data are generated from the system with reference parameter data characteristic of known behavior of the system. The generated estimates of the parameter data are compared with measured parameter data. A residual is determined for each of the parameters based on a relationship between the estimates of the parameter data and the measured parameter data. A fuzzy classification rule is implemented to determine a likelihood that a predetermined fault exists by using residuals for parameters used to indicate the existence of the predetermined fault.

Abstract: System and method for selection of appropriate modeling data from a general data set to characterize a modeled process. The data is typically correlated sensor data, representing a multitude of snapshots of a sensed machine or process. The invention accommodates selection of greater amounts of general data for inclusion in the modeling data where that data exhibits greater dynamics, and selects less data from regions of little change. The system can comprise a computer running a software program, or a microprocessor.

Abstract: A method for systematically configuring and deploying an empirical model used for fault detection and equipment health monitoring. The method is driven by a set of data preprocessing and model performance metrics subsystems that when applied to a raw data set, produce an optimal empirical model.

Abstract: A system for empirically diagnosing a condition of a monitored system. Estimates of monitored parameters from a model of the system provide residual values that can be analyzed for failure mode signature recognition. Residual values can also be tested for alert (non-zero) conditions, and patterns of alerts thus generated are analyzed for failure mode signature patterns. The system employs a similarity operator for signature recognition and also for parameter estimation. Failure modes are empirically determined, and precursor data is automatically analyzed to determine differentiable signatures for failure modes.

Abstract: A method and apparatus are provided for diagnosing faults in a monitored system. Estimates of parameter data are generated from the system with reference parameter data characteristic of known behavior of the system. The generated estimates of the parameter data are compared with measured parameter data. A residual is determined for each of the parameters based on a relationship between the estimates of the parameter data and the measured parameter data. A fuzzy classification rule is implemented to determine a likelihood that a predetermined fault exists by using residuals for parameters used to indicate the existence of the predetermined fault.

Abstract: A system, method and program product for monitoring a complex signal for ultrasensitive detection of state changes, or for signature recognition and classification is provided. A complex signal is decomposed periodically for empirical modeling. Wavelet analysis, frequency band filtering or other methods may be used to decompose the complex signal into components. A library of signature data may be referenced for selection of a recognized signature in the decomposed complex signal. The recognized signature may indicate data being carried in the complex signal. Estimated signal data may be generated for determination of an operational state of a monitored process or machine using a statistical hypothesis test with reference to the decomposed input signal.

Abstract: A system, method and program product for monitoring a complex signal for ultrasensitive detection of state changes, or for signature recognition and classification is provided. A complex signal is decomposed periodically for empirical modeling. Wavelet analysis, frequency band filtering or other methods may be used to decompose the complex signal into components. A library of signature data may be referenced for selection of a recognized signature in the decomposed complex signal. The recognized signature may indicate data being carried in the complex signal. Estimated signal data may be generated for determination of an operational state of a monitored process or machine using a statistical hypothesis test with reference to the decomposed input signal.

Abstract: Reference data observations for given system are used to develop a reference set of such observations. Subsequent observations (comprising, in one embodiment, current observations) are then used to facilitate selection of portions of this reference set to yield a resultant set of observations that serves as a model. This model can then be used in comparison to actual system performance to detect, for example, a trend towards a faulty condition. Pursuant to a preferred approach, the model is recomputed from time to time and, pursuant to a particular embodiment, is recomputed with each new set of current observations.

Abstract: A method and apparatus for improved monitoring the operational state of instrumented systems is provided. An empirical model characterizes normal or desirable operation of the system, and real-time observations are provided to the model to generate estimates of expected sensor values. Comparison of the estimates with the real-time observations provides advanced warning of discrepancies in the operational state of the instrumented system. The invention provides for incipient failure detection, sensor failure detection and incipient process upset. An improved similarity operator provides for estimates that are not impaired by real-time observations at or beyond the limits of modeled data. The similarity operator comprises a difference function added to a constant, and the result is inverted.

Abstract: A system for empirically diagnosing a condition of a monitored system. Estimates of monitored parameters from a model of the system provide residual values that can be analyzed for failure mode signature recognition. Residual values can also be tested for alert (non-zero) conditions, and patterns of alerts thus generated are analyzed for failure mode signature patterns. The system employs a similarity operator for signature recognition and also for parameter estimation. Failure modes are empirically determined, and precursor data is automatically analyzed to determine differentiable signatures for failure modes.

Abstract: An improved empirical model-based surveillance or control system for monitoring or controlling a process or machine provides adaptation of the empirical model in response to new operational states that are deemed normal or non-exceptional for the process or machine. An adaptation decision module differentiates process or sensor upset requiring alerts from new operational states not yet modeled. A retraining module updates the empirical model to incorporate the new states, and a pruning technique optionally maintains the empirical model by removing older states in favor of the added new states recognized by the model.