Mihai Huzmezan

Quantifying cortical activity during general anesthesia using wavelet analysis

This paper reports on a novel method for quantifying the cortical activity of a patient during general anesthesia as a surrogate measure of the patients level of consciousness. The proposed technique is based on the analysis of a single-channel (frontal) electroencephalogram (EEG) signal using stationary wavelet transform (SWT). The wavelet coefficients calculated from the EEG are pooled into a statistical representation, which is then compared to two well-defined states: the awake state with normal EEG activity, and the isoelectric state with maximal cortical depression. The resulting index, referred to as the wavelet-based anesthetic value for central nervous system monitoring (WAV/sub CNS/), quantifies the depth of consciousness between these two extremes. To validate the proposed technique, we present a clinical study which explores the advantages of the WAV/sub CNS/ in comparison with the BIS monitor (Aspect Medical Systems, MA), currently a reference in consciousness monitoring. Results show that the WAV/sub CNS/ and BIS are well correlated (r=0.969) during periods of steady-state despite fundamental algorithmic differences. However, in terms of dynamic behavior, the WAV/sub CNS/ offers faster tracking of transitory changes at induction and emergence, with an average lead of 15-30 s. Furthermore, and conversely to the BIS, the WAV/sub CNS/ regains its preinduction baseline value when patients are responding to verbal command after emergence from anesthesia. We conclude that the proposed analysis technique is an attractive alternative to BIS monitoring. In addition, we show that the WAV/sub CNS/ dynamics can be modeled as a linear time invariant transfer function. This index is, therefore, well suited for use as a feedback sensor in advisory systems, closed-loop control schemes, and for the identification of the pharmacodynamic models of anesthetic drugs.

A wavelet based de-noising technique for ocular artifact correction of the electroencephalogram

This paper investigates a wavelet based denoising of the electroencephalogram (EEG) signal to correct for the presence of the ocular artifact (OA). The. proposed technique is based on an over-complete wavelet expansion of the EEG as follows: i) a stationary wavelet transform (SWT) is applied to the corrupted EEG; ii) the thresholding of the coefficients in the lower frequency bands is performed; iii) the de-noised signal is reconstructed. This paper demonstrates the potential of the proposed technique for successful OA correction. The advantage over conventional methods is that there is no need for the recording of the electrooculogram (EOG) signal itself. The approach works both for eye blinks and eye movements. Hence, there is no need to discriminate between different artifacts. To allow for a proper comparison, the contaminated EEG signals as well as the corrected signals are presented together with their corresponding power spectra.

Introduction to Automated Drug Delivery in Clinical Anesthesia

Control technology has been applied to a wide variety of industrial and domestic applications to improve performance, safety and efficiency. Anesthesia, a critical aspect of clinical and emergency medicine, has not yet benefited from such technological advances. The lack of dedicated feedback sensors, and the large inter- and intra-patient variability in terms of patients’ response to drug administration, have seriously limited the effectiveness and reliability of closed-loop controllers in clinical settings. However, recent advances in sensing devices, along with robust nonlinear control theories, have generated new hopes that the gap between manual and automated control of anesthesia can finally be bridged. This paper addresses the pharmacological principles of clinical anesthesia in a context appropriate for control engineers. Concepts and terminology, monitoring issues, as well as drug dose vs. response relationships, are covered.

Time delay integrating systems: a challenge for process control industries. A practical solution

Abstract Temperature control of processes that involve the heating and cooling of a closed batch reactor can be a real problem for conventional proportional-integral-derivative (PID) based loop controllers. This paper describes the application of a new industrial advanced process controller. This controller is designed to handle integrating type processes with long dead times and long time constants. The results demonstrate that reactors that could previously only be operated manually can be easily automated using an adaptive model predictive control technology. The barrier to automation of the reactor batch controls can be now removed resulting in tremendous improvements in batch consistency, batch cycle times, and productivity.

Reconfiguration and scheduling in flight using quasi-LPV high-fidelity models and MBPC control

Describes advances towards reconfiguration and scheduling in flight control systems using high fidelity models expressed in a quasi-LPV form, fault detection and isolation (FDI), model approximation/simplification and constrained model based predictive control (MBPC). The strategy is applied to a well known missile example.

Reconfigurable Flight Control During Actuator Failures Using Predictive Control

Abstract This paper describes a scheme for fault-tolerant control of an aircraft with a high angle of incidence. The scheme is an example of a generic scheme proposed in (Maciejowski, 1997), adapted to the particular needs of this application. Details of the predictive control technique when employed for reconfiguration are given. The paper also contains a discussion of various scenarios for possible actuator faults. The performance of the proposed fault-tolerant scheme in the presence of severe failures of control surfaces is examined in simulation with an High Incidence Research Model (HIRM)

Advanced control of batch reactor temperature

Describes the application of an advanced model predictive adaptive controller to the problem of batch reactor temperature control. Temperature control on these systems is difficult for conventional proportional-integral-derivative (PID) controllers because the response is characterized by an open loop integrator with long delay and time constant. Temperature control is important as many chemical reactions are sensitive to temperature for formation of desired products and reaction rates can be highly temperature dependent. The applications discussed in the paper include a PVC reactor and an Ethoxylated fatty acid reactor. In each case, the variability of the reactor temperature was reduced by 60% or more. Improved temperature control permitted operation at higher reaction temperatures with higher sustained feed rates of reactants and catalysts while remaining within product temperature limits. Batch cycle times were reduced by as much as 35% due to the higher sustained reaction rates. The applications demonstrate the attractive economics for optimization of batch reactors with model predictive controls and highlight the opportunity for tremendous improvements in batch consistency, reduced batch cycle times, and improved productivity.

Adaptive control of delayed integrating systems: a PVC batch reactor

Batch processes are a real challenge for conventional proportional-integral-derivative (PID) controllers. PID tuning can be extremely difficult due to the reduced stability margins. An industrial indirect adaptive control scheme was developed and applied to more than two dozen processes with an integrating time delay characteristic. Among these a polyvinyl chloride (PVC) batch reactor was chosen to illustrate the performance of the proposed method. The scheme, which uses identification based on Laguerre functions and predictive control, allows easy automation of batch reactors. This control strategy resulted in an improved batch consistency, reduced cycle times, and increased productivity.

Automatic Tuning for Model Based Predictive Control During Reconfiguration

Abstract When major failures are encountered in a missile controlled by a nominal Model Based Predictive Control (MBPC) these are reflected in the internal model via a relatively sophisticated mechanism involving high fidelity models written in the quasi-LPV form. At this stage an optimisation will provide the tuning parameters the controller cost function weights- which will enable the controller to match, online, a reference closed loop. The algorithm is applied to provide the MBPC tuning while reconfiguring on-line a missile for major structural impairments.

PATIENT VARIABILITY AND UNCERTAINTY QUANTIFICATION IN ANESTHESIA: PART II – PKPD UNCERTAINTY

Abstract The outcome of any surgery is particularly dependent on the adequate delivery of anesthetic drugs. Not surprisingly clinical researchers have been trying to automatize their delivery in order to provide anesthesiologists with titration tools that can target the exact needs of each individual patient. As compared to todays population-normed drug delivery strategy close loop drug delivery systems would provide patients with customized pharmacological action, thereby improving surgery outcome. While some anesthesia close loop designs have already shown promising results within controlled clinical protocols, the pharmacological variability that exists between patients needs to be addressed within a mathematical framework to prove the stability of the control laws, and gain faster and wider acceptance of these systems by the clinical community and regulatory committees. This paper is the second of a series of 2 papers addressing the issue of pharmacological variability and PKPD uncertainty. In the first paper, we presented our own drug modeling approach, which we applied towards the identification of 44 adult patient models for propofol, a central nervous system depressant drug. The individual patient models have shown a large inter-patient variability. In this paper, we further expand on our previous result in order to derive an uncertainty metrics that can be used in the control design to ensure stability and assess performances.