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School of Electrical, Computer and Energy Engineering PhD Final Oral Defense Use of Bayesian Filtering and Adaptive Learning Methods to Improve the Detection and Estimation of Pathological and Neurological Disorders by Alexander Joseph Maurer June 24, 2016 3 PM GWC 305 Committee: Dr. Antonia Papandreou-Suppappola (chair) Dr. Daniel Bliss Dr. Chaitali Chakrabarti Dr. Narayan Kovvali Abstract Biological and biomedical measurements, when adequately analyzed and processed, can be used to impart quantitative diagnosis during primary health care consultation to improve patient adherence to recommended treatments. For example, biosequences, such as sequences from peptide microarrays obtained from a biological sample, can potentially provide pre-symptomatic diagnosis for infectious diseases when processed to associate antibodies to specific pathogens or infectious agents. As another example, analyzing neural recordings from neurostimulators implanted in patients with neurological disorders can be used by a physician to adjust detrimental stimulation parameters to improve treatment. This work proposes advanced statistical signal processing and machine learning methodologies to extract diagnostic information from biosequences and to assess neurostimulation from neural recordings. For pathogen detection and identification, random peptide sequences and their properties are first uniquely mapped to highly-localized signals and their corresponding parameters in the time-frequency plane. Time-frequency signal processing methods are then applied to estimate antigenic determinants or epitope candidates for detecting and identifying potential pathogens. For locating specific cognitive and behavioral information in different regions of the brain, neural recordings are processed using sequential Bayesian filtering methods to detect and estimate both the number of neural sources and their corresponding parameters. Time-frequency based feature selection algorithms are combined with adaptive machine learning approaches to suppress physiological and non-physiological artifacts present in neural recordings. Adaptive processing and unsupervised clustering methods applied to neural recordings are also used to suppress neurostimulation artifacts and classify between various behavior tasks to assess the level of neurostimulation in patients.
