Bertinho A. Costa

Diode/magnetic tunnel junction cell for fully scalable matrix-based biochip

Magnetoresistive biochips have been recently introduced for the detection of biomolecular recognition. In this work, the detection site incorporates a thin-film diode in series with a magnetic tunnel junction (MTJ), leading to a matrix-based biochip that can be easily scaled up to screen large numbers of different target analytes. The fabricated 16×16 cell matrix integrates hydrogenated amorphous silicon (a-Si:H) diodes with aluminum oxide barrier MTJ. Each detection site also includes a U-shaped current line for magnetically assisted target concentration at probe sites. The biochip is being integrated in a portable, credit card size electronics control platform. Detection of 250nm diameter magnetic nanoparticles by one of the matrix cells is demonstrated.

Local identifiability and sensitivity analysis of neuromuscular blockade and depth of hypnosis models

This paper addresses the local identifiability and sensitivity properties of two classes of Wiener models for the neuromuscular blockade and depth of hypnosis, when drug dose profiles like the ones commonly administered in the clinical practice are used as model inputs. The local parameter identifiability was assessed based on the singular value decomposition of the normalized sensitivity matrix. For the given input signal excitation, the results show an over-parameterization of the standard pharmacokinetic/pharmacodynamic models. The same identifiability assessment was performed on recently proposed minimally parameterized parsimonious models for both the neuromuscular blockade and the depth of hypnosis. The results show that the majority of the model parameters are identifiable from the available input-output data. This indicates that any identification strategy based on the minimally parameterized parsimonious Wiener models for the neuromuscular blockade and for the depth of hypnosis is likely to be more successful than if standard models are used.

Robust Control of Maintenance-Phase Anesthesia [Applications of Control]

In biomedical systems, feedback control can be applied whenever adequate sensors, actuators, and sufficiently accurate mathematical models are available. The key issue is the capacity of the control algorithm to tackle the large levels of uncertainty, both structured and unstructured, associated with patient dynamics. In the particular case of intravenous anesthesia considered here, manipulated variables are drug infusion rates, administered by syringe pumps, and the measured signal outputs are the levels of hypnosis or depth of anesthesia (DoA) and of neuromuscular blockade (NMB).

Temperature Control of a Solar Furnace with Exact Linearization and Off-Line Identification

The ability to predict the behavior of materials is crucial in several industries due to safety aspects, such as in high temperature furnaces for the production of glass, in the nuclear energy industry or in the high concentrated thermal solar systems. Solar furnaces use concentrated solar radiation that can be used to perform material stress tests with high temperatures. These devices have nonlinear dynamics that are caused by the actuator, the shutter, and by the interaction between the solar energy and the properties of the material. The contribution of this paper consists in the design of a controller for practical use based on the exact linearization method with off-line identification. The aim is to improve the controller performance to track the temperature profile. In this context it is assumed that the thermodynamic properties of the sample material to be tested are unknown.

A polynomial design approach to robust control of neuromuscular blockade of patients subject to general anesthesia

This work addresses the design of robust controllers for the neuromuscular blockade (NMB) level of patients subject to general anesthesia. The approach followed relies on the design of pole-placement controllers using input-output models and polynomial techniques. Robust performance is imposed in the NMB control design in order to perform appropriate responses on the model database. Robust stability is required in the design for the controller to be able to tackle the uncertainty present in these biomedical systems. Clinical results with the resulting controller are presented.

Batch identification of neuromuscular blockade models

This work addresses the problem of identifying neuromuscular blockade models of patients undergoing general surgery. First, a sensitivity analysis is made, exploring the Wiener structure of the system. The outcomes of this analysis are twofold: First, it provides information about the time periods in which data is more informative for parameter estimation. Second, it is the basis of a local identifiability analysis that allows to decide which parameters are to be estimated from data and which are the ones whose values should be a priori selected based on previous insight. The time dependency of sensitivity is then used to adjust the weight of output errors in a Bayesian cost function whose minimization yields parameter estimates: Whenever the sensitivity is low, the weight is reduced. The contribution of the paper consists in the demonstration of this procedure using actual clinical data.

Design of depth of anesthesia controllers in the presence of model uncertainty

A major obstacle in the design of controllers to regulate the depth of anesthesia (DoA) consists in the high model uncertainty due to inter-patient variability. Surprisingly, the use of control design methods that explicitly tackle this problem is almost absent from the literature on automatic control of anesthesia. In this work, a DoA controller is designed taking into account model uncertainty to comply with robust stability and robust performance specifications for a patient population undergoing elective general surgery, with hypnosis induced by the drug propofol. Due to its Wiener nonlinear structure, the DoA model can be linearized around a given operating point. Therefore, using a database with 18 patient models, a non-parametric description of uncertainty for a linearized model is first performed. By using H∞ design methods, a continuous linear controller is then designed so as to ensure robust stability and performance within the uncertainty bounds defined. The controller that results from this procedure is approximated by a controller with a lower order that, in turn, is redesigned in discrete time for computer control application. The final result is tested in nonlinear realistic patient models, with acceptable closed-loop results.

A Bi-Phase Algorithm to Identify Hypnotic Models of Patients Subject to Deep Sedation for Ultrasonographic Endoscopy

Abstract This work addresses the problem of identifying hypnotic models of patients undergoing deep sedation for ultrasonographic endoscopy. The model used assumes that there are three inputs and one output. The output is the level of hypnosis. Two of the inputs correspond to manipulated variables and are given by the perfusion rates of the hypnotic drug (propofol) and analgesic drug (remifentanil). In addition to these input signals that are known, there is another input that is induced by the noxious stimuli applied to the patient due to the manoeuvres of the endoscopic device and may not be measured. In order to take into account the unmeasurable input, a bi-phase estimation procedure is proposed. Before the identification, a local identifiability analysis is performed. The contribution of the paper consists in the bi-phase algorithm used to identify systems with unknown inputs and to demonstrate the identification procedure proposed using actual clinical data.

Temperature modelling of a biochip for DNA analysis

This paper describes the work on temperature modelling of an Electronic Detection Cell (EDC) for DNA analysis. The EDC is part of a biochip which is under development at INESC. The main goal of the project is to build a hand-held biochip with several sites for running simultaneous detection experiments with independent temperature profiles. The basic DNA detection electronic cell comprises a Thin Film Diode (TFD) in series with a Magnetic Resistive Tunnel Junction (MRTJ). The role of the MRTJ is to detect the presence of bio-molecules using magnetic bides. The TFD is to be used as a temperature sensor, to give information about the temperature at which the hybridization process is developing and also as a electronic selector of the hybridization site, which are arranged in a matrix. Both the TFD and MRTJ will dissipate power but the main source of heat is a current line which is used to attract the bides to the MRTJ. Because of the structure of the biochip one must understand how heat diffuses between the heater and the hybridization site, and one must characterize the temperature distribution between the hybridization site, temperature sensor and the power input

Computer Implementation of Robust Controllers for Anesthesia

The reversible state of a general anesthesia encompasses three components: hypnosis, paralysis and analgesia. In this paper design and implementation issues of the controllers used to maintain paralysis and hypnosis are presented. The controllers are designed from model databases of compartmental models and rely on the H ∞ method. In order to cope with the variability of the patient models, robust techniques are used to produce a controller that is able to withstand robust stability and robust performance. The controllers are designed in continuous and discretized for computer implementation. Clinical results during elective surgery are presented to illustrate the results obtained.