Biomedical Signal Processing and System Identification

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The long term objectives of my research are to develop and validate a general purpose tool-set for the quantitative analysis of biomedical signals and systems. The work proposed here will build tools for two specific areas: joint stiffness and cardio-respiratory signals. System Identification Methods for Dynamic Joint Neuromechanics A joint's neuromechanical behavior arises from the intrinsic joint and muscle properties combined with reflex activation of muscle. Neuromechanics is important in the control of posture, where it determines the response to disturbances, and movement, where it defines the internal load that must be controlled. Dynamic joint stiffness describes neuromechanics quantitatively as the relation between joint position and torque. Currently, there is a good understanding of joint stiffness under postural conditions, where position and torque remain nearly constant. However, little is known about joint stiffness during functional activities, when there are large, rapid changes in position and torque. The objective of this work is to develop tools to measure and model dynamic joint stiffness during the execution of functional activities. This is essential to understand the mechanisms underlying normal motor function and the changes accompanying motor system disease. Use of these tools will permit the quantitative, objective evaluation of joint mechanics for the diagnosis and monitoring of pathological changes, evaluation of treatment modalities, and the informed design and control of prosthetic, orthotic, and rehabilitation devices. Signal Processing and Classification of Cardio Respiratory Signals The analysis of respiratory and cardiac signals is important for the diagnosis and monitoring of many diseases including sleep apnea. The current "gold-standard" analysis approach is to score cardiorespiratory signals manually to identify significant events such as apneas. This approach is very labor intensive, and subject to large inter- and intra- scorer variability. The objective of this work is to develop an automated tool-set to classify respiratory signals into clinically recognized patterns. The resulting tools will have a wide range of applications including: the evaluation of sleep disordered breathing, the management of pediatric apneas including the apnea of prematurity, the extubation of infants from mechanical ventilation, and the evaluation of non-invasive support modalities. The algorithms resulting from this research will provide researchers with the means to address important questions in neuromuscular control and cardio-respiratory function. To facilitate the translation of these tools to practice, we will make them available as comprehensive, ready-to-use toolboxes, accompanied by extensive documentation, sample data sets, tutorials and demonstrations. We will also hold workshops to teach researchers, students, and industrial practitioners how to use our toolboxes to address their R&D needs