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Use of Bayesian Filtering and Adapti...

Arizona State University.

Use of Bayesian Filtering and Adaptive Learning Methods to Improve the Detection and Estimation of Pathological and Neurological Disorders.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Use of Bayesian Filtering and Adaptive Learning Methods to Improve the Detection and Estimation of Pathological and Neurological Disorders.
作者:	Maurer, Alexander Joseph.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2016
面頁冊數:	158 p.
附註:	Source: Dissertation Abstracts International, Volume: 77-12(E), Section: B.
附註:	Adviser: Antonia Papandreou-Suppappola.
Contained By:	Dissertation Abstracts International77-12B(E).
標題:	Electrical engineering.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10145441
ISBN:	9781369009675

Use of Bayesian Filtering and Adaptive Learning Methods to Improve the Detection and Estimation of Pathological and Neurological Disorders.
Maurer, Alexander Joseph.

Use of Bayesian Filtering and Adaptive Learning Methods to Improve the Detection and Estimation of Pathological and Neurological Disorders. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 158 p.

Source: Dissertation Abstracts International, Volume: 77-12(E), Section: B.

Thesis (Ph.D.)--Arizona State University, 2016.

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, 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. As another 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. This work proposes advanced statistical signal processing and machine learning methodologies to assess neurostimulation from neural recordings and to extract diagnostic information from biosequences.

ISBN: 9781369009675Subjects--Topical Terms:

454503
Electrical engineering.

Use of Bayesian Filtering and Adaptive Learning Methods to Improve the Detection and Estimation of Pathological and Neurological Disorders.
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520 $a 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.
520 $a 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.
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