Showing papers by "James Bailey published in 2011"

Neuroadaptive Output Feedback Control for Automated Anesthesia With Noisy EEG Measurements

Abstract: Critical care patients, whether undergoing surgery or recovering in intensive care units, require drug administration to regulate physiological variables such as blood pressure, cardiac output, heart rate, and degree of consciousness. The rate of infusion of each administered drug is critical, requiring constant monitoring and frequent adjustments. Nonnegative and compartmental models provide a broad framework for biological and physiological systems, including clinical pharmacology, and are well suited for developing models for closed-loop control of drug administration. In this paper, we develop a neuroadaptive output feedback control framework for nonlinear uncertain nonnegative and compartmental systems with nonnegative control inputs and noisy measurements. The proposed framework is Lyapunov-based and guarantees ultimate boundedness of the error signals. In addition, the neuroadaptive controller guarantees that the physical system states remain in the nonnegative orthant of the state space. Finally, the proposed approach is used to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for surgery in the face of noisy electroencephalographic (EEG) measurements. Clinical trials demonstrate excellent regulation of unconsciousness allowing for a safe and effective administration of the anesthetic agent propofol.

Automated and semi‐automated cell tracking: addressing portability challenges

Abstract: Cell tracking is a key task in the high-throughput quantitative study of important biological processes, such as immune system regulation and neurogenesis Variability in cell density and dynamics in different videos, hampers portability of existing trackers across videos We address these potability challenges in order to develop a portable cell tracking algorithm Our algorithm can handle noise in cell segmentation as well as divisions and deaths of cells We also propose a parameter-free variation of our tracker In the tracker, we employ a novel method for recovering the distribution of cell displacements Further, we present a mathematically justified procedure for determining the gating distance in relation to tracking performance For the range of real videos tested, our tracker correctly recovers on average 96% of cell moves, and outperforms an advanced probabilistic tracker when the cell detection quality is high The scalability of our tracker was tested on synthetic videos with up to 200 cells per frame For more challenging tracking conditions, we propose a novel semi-automated framework that can increase the ratio of correctly recovered tracks by 12%, through selective manual inspection of only 10% of all frames in a video

Multistability, bifurcations, and biological neural networks: A synaptic drive firing model for cerebral cortex transition in the induction of general anesthesia

Abstract: This paper focuses on multistability theory for discontinuous dynamical systems having a set of multiple isolated equilibria and/or a continuum of equilibria Multistability is the property whereby the solutions of a dynamical system can alternate between two or more mutually exclusive Lyapunov stable and convergent equilibrium states under asymptotically slowly changing inputs or system parameters In this paper, we extend the definition and theory of multistability to discontinuous autonomous dynamical systems In particular, nontangency Lyapunov-based tests for multistability of discontinuous systems with Filippov solutions are established The results are then applied to excitatory and inhibitory biological neuronal networks to explain the underlying mechanism of action for anesthesia and consciousness from a multistable dynamical system perspective, thereby providing a theoretical foundation for general anesthesia using the network properties of the brain

Distribution based data filtering for financial time series forecasting

Abstract: Changes in the distribution of financial time series, particularly stock market prices, can happen at a very high frequency. Such changes make the prediction of future behavior very challenging. Application of traditional regression algorithms in this scenario is based on the assumption that all data samples are equally important for model building. Our work examines the use of an alternative data pre-processing approach, whereby knowledge of distribution changes is used to pre-filter the training dataset. Experimental results indicate that this simple and efficient technique can produce effective results and obtain improvements in prediction accuracy when used in conjunction with a range of forecasting techniques.

Overview of Contrast Data Mining as a Field and Preview of an Upcoming Book

Abstract: This report provides an overview of the field of contrast data mining and its applications, and offers a preview of an upcoming book on the topic. The importance of contrasting is discussed and a brief survey is given covering the following topics: general definitions and terminology for contrast patterns, representative contrast pattern mining algorithms, applications of contrast mining for fundamental data mining tasks such as classification and clustering, applications of contrast mining in bioinformatics, medicine, blog analysis, image analysis and subgroup mining, results on contrast based dataset similarity measure, and on analyzing item interaction in contrast patterns, and open research questions.