Biomedical Signal Analysis and System Identification

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This research seeks to develop algorithms and tools for the analysis and identification of biomedical signals and systems. The problems addressed are motivated and defined by the requirements of biological research but the focus of the research is on the development and validation of practical tools and algorithms, rather than investigating specific biological questions. Problems from two biomedical areas will be addressed.(1) Identification methods to study the control of posture and movement. The objective is to provide the tools needed to characterize the dynamic, mechanical properties of joints during normal daily function and understand the underlying mechanisms. This is essential to understand both normal and pathological motor function and will have important practical results for the diagnosis of motor abnormalities, the quantitative assessment of treatment, and the design of advanced assistive devices such as prostheses and orthoses.(2) Automated signal analysis for pediatric cardio-respiratory function monitoring. It is known that respiratory and cardiac behavior contains information that could aid in the management of respiratory problems, such as apnea of prematurity and the extubation of infants from mechanical ventilation can safely terminated. However, application of this information is hampered by the need for manual data analysis. This is expensive, time consuming and inaccurate. We will address this issue by developing a fully automated system for the analysis of cardio-respiratory signals and combine these with machine learning techniques to predict the probability that an infant will encounter an adverse respiratory event.