The University of British Columbia

Honeywell G.m.b.h.

Bionova Technologies Inc.

Lionsgate Technologies, Inc.

The Royal Institution for the Advancement of Learning / Mcgill University

Macmillan Bloedel Limited

Pulsar Informatics, Inc.

Guy A Dumont

Johan Backstrom

Michael Forbes

Philip D Loewen

Mihai Huzmezan

Qiugang Lu

Craig Robert Ries

Tatjana Zikov

Ernest Puil

Mark Ansermino

Stéphane Bibian

R Bhushan Gopaluni

Bernard Ansell Macleod

Christian Leth Petersen

Nicholas West

Hossain Cyrus Ahmadi

Sara Khosravi

John Mark Ansermino

Daniel Joseph Mollicone

Christopher Grey Mott

Bruce James Allison

Diane Boivin

Klaske Van Heusden

Bhusban Gopaluni

Lee D Rippon

Hosaain Cyrus Ahmadi

Patrick J Tessier

Joe E Ciarniello

A control system for administration of a drug comprises a drug administration actuator for administering the drug to a patient at a controllable dosage; a monitor for measuring an effect of the drug on the patient; and a controller configured to determine a control signal to the drug administration actuator; wherein the controller is configured to implement a closed loop model-predictive control scheme comprising, for each of a series of time steps, minimizing a cost function subject to one or more constraints to determine the control signal, the cost function based at least in part on a reference level and the monitor measurement; wherein the control signal is used as an input to an auxiliary model to estimate at least one concentration level of the drug in the patient; and wherein the constraints comprise at least one constraint on the estimate of the concentration level of the drug.

Methods and systems for closed-loop control of drug administration

A method includes obtaining operating data associated with operation of a cross-directional industrial process controlled by at least one model-based process controller. The method also includes, during a training period, performing closed-loop model identification with a first portion of the operating data to identify multiple sets of first spatial and temporal models. The method further includes identifying clusters associated with parameter values of the first spatial and temporal models. The method also includes, during a testing period, performing closed-loop model identification with a second portion of the operating data to identify second spatial and temporal models. The method further includes determining whether at least one parameter value of at least one of the second spatial and temporal models falls outside at least one of the clusters. In addition, the method includes, in response to such a determination, detecting that a mismatch exists between actual and modeled behaviors of the industrial process.

Model-plant mismatch detection with support vector machine for cross-directional process behavior monitoring

The invention relates to administration of clinical anesthesia. Particular embodiments provide systems and methods for controlled delivery of a combination of an analgesic agent and a hypnotic agent. More specifically, the invention relates to closed-loop control systems/methods for automatically controlling the administration of a combination of a hypnotic agent and an analgesic agent in a clinical anesthesia setting which incorporate feedback based on one or more indirect measures/indicia of analgesia. The invention further relates to such control systems/methods that account for limitations of such indirect measures/indicia of analgesia.

Systems and methods for controlled delivery of analgesic and hypnotic agents

A method includes repeatedly identifying one or more values for one or more model parameters of at least one model associated with a process. The one or more values for the one or more model parameters are identified using data associated with the process. The method also includes clustering the values of the one or more model parameters into one or more clusters. The method further includes identifying one or more additional values for the one or more model parameters using additional data associated with the process. In addition, the method includes detecting a mismatch between the at least one model and the process in response to determining that at least some of the one or more additional values fall outside of the one or more clusters. The values could be clustered using a support vector machine.

Model-plant mismatch detection using model parameter data clustering for paper machines or other systems

A method includes obtaining closed-loop data associated with operation of an industrial process controller, where the industrial process controller is configured to control at least part of an industrial process using at least one model. The method also includes generating at least one noise model associated with the industrial process controller using at least some of the closed-loop data. The method further includes filtering the closed-loop data based on the at least one noise model. In addition, the method includes generating one or more model parameters for the industrial process controller using the filtered closed-loop data.

Closed-loop model parameter identification techniques for industrial model-based process controllers

Sheetmaking cross-directional (CD) control requires a sophisticated model-based controller whose operation requires an accurate model of process behavior, but due to the complexity of the process, identifying these process models is challenging. Current techniques rely on open-loop process experimentation. Using non-causal scalar transfer functions for the steady-state CD process model and controller model avoid the problem of large dimensions associated with the CD process. These non-causal transfer functions can be represented by causal transfer functions that are equivalent to the non-causal ones in the sense of the output spectrum. A closed-loop optimal input design framework is proposed based on these causal equivalent models. CD actuators have responses in both sides along the cross direction which can be viewed as a non-causal behavior. Techniques to perform the non-causal modeling are demonstrated and developed in a closed-loop optimal input design framework based on non-causal modeling of the closed-loop CD process.

Optimal closed-loop input design for identification of flat-sheet process models

Methods, systems and related apparatus are provided to enable an electronic device to operate an external sensor comprising one or more emitters for emitting electromagnetic radiation of two different wavelengths and a detector for generating a response signal based on received electromagnetic radiation of the two different wavelengths connectable to an audio interface by applying a harmonic driving signal to a first contact and a second contact of the audio interface for driving the emitters of the external sensor, receiving the response signal at a third contact of the audio interface, demodulating and demultiplexing the response signal into a first wavelength response signal and a second wavelength response signal, analyzing the first and second wavelength response signals to determine one or more vital signs, and outputting the determined one or more vital signs.

Systems and methods for determining physiological characteristics of a patient using pulse oximetry

A method and apparatus to monitor the neurologic state of a patient undergoing general anesthesia is provided. Previous automated systems to monitor the neurologic state of a patient undergoing general anesthesia involve a significant time delay between the patient's true hypnotic state and the computed indices. The present invention reduces this time delay by using a different analysis technique applied to spontaneous EEG. A wavelet decomposition and statistical analysis of the observed EEG is conducted and compared to reference data to provide a numerical indicator. In addition, this indicator is more consistent with the patient's loss of consciousness indicated by the loss of count event than previous systems.

Method and apparatus for the estimation of anesthetic depth using wavelet analysis of the electroencephalogram

Systems and methods are provided for predicting a circadian state of an individual. The methods comprise: providing a model representative of the response of the circadian state to light stimulus, the model comprising at least one model variable representative of a probability distribution function (PDF) of a phase offset of the circadian state of the individual; and using the model to estimate an updated PDF of the phase offset, wherein using the model to estimate the updated PDF of the phase offset comprises performing a Bayesian estimation process commencing with an initial PDF of the phase offset and iterating toward the updated PDF of the phase offset.

Methods and systems for circadian physiology predictions

The present invention relates to a method of signal processing of electrograms for use in medical devices, preferably by time-frequency transforms. The present invention additionally relates to a system for receiving and analyzing such signals. The present invention preferably is a method utilizing the time-frequency transforms, such as wavelet transforms, for the purpose of artifact removal from EGs. These transforms decompose a signal in both time and frequency domains, and therefore, are well suited for non-stationary signal analysis. As a result, dissimilar signal features are well localized both in time and frequency, which potentially provides a good separation between the signal of interest and artifacts. This particularly applies to large-amplitude artifacts corrupting EGs.

Method and system for the denoising of large-amplitude artifacts in electrograms using time-frequency transforms

A method and apparatus to monitor and control the anesthesia state of a patient undergoing general anesthesia is provided. Previous automated systems to monitor the anesthesia state of a patient undergoing general anesthesia involve a significant time delay between the patient's true anesthesia state and the computed indices. The present invention reduces this time delay by using a novel analysis technique applied to spontaneous electrophysiological activity. A transformation and statistical analysis of the observed electrophysiological activity is conducted and compared to reference data to provide numerical indicators. In addition, these indicators are consistent with the levels of depression of patients CNS and ANS states. This method is illustrated in detail by CNS monitoring of electroencephalogram signals.

Method and apparatus for the estimation of anesthesia depth

The load on a refiner motor is controlled by adjusting the motor load setpoint by monitoring load applied by the refiner and the plate gap between the refiner plates to define the curve of the load versus plate gap relationship. This slope of load versus plate gap relationship is estimated and when the sign of the slope of the curve changes sign indicating operation in an unstable zone of pad collapse and based on the tendency for the refiner to be in the unstable operation zone over a selected period of time exceeding a preset limit adjusting the setpoint lower to approach more closely the maximum load applied over the selected time.

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Vancouver, Canada

Guy A Dumont