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Cervical Auscultation for the Identification of Swallowing Difficulties

Dudik, Joshua (2016) Cervical Auscultation for the Identification of Swallowing Difficulties. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

Swallowing difficulties, commonly referred to as dysphagia, affect thousands of Americans every year. They have a multitude of causes, but in general they are known to increase the risk of aspiration when swallowing in addition to other physiological effects. Cervical auscultation has been recently applied to detect such difficulties non-invasively and various techniques for analysis and processing of the recorded signals have been proposed. We attempted to further this research in three key areas. First, we characterized swallows with regards to a multitude of time, frequency, and time-frequency features while paying special attention to the differences between swallows from healthy adults and safe dysphagic swallows as well as safe and unsafe dysphagic swallows. Second, we attempted to utilize deep belief networks in order to classify these states automatically and without the aid of a concurrent videofluoroscopic examination. Finally, we sought to improve some of the signal processing techniques used in this field. We both implemented the DBSCAN algorithm to better segment our physiological signals as well as applied the matched complex wavelet transform to cervical auscultation data in order to improve its quality for mathematical analysis.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Dudik, Joshuajmd151@pitt.eduJMD151
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairSejdić, Ervinesejdic@pitt.eduESEJDIC
Committee MemberMao, Zhi-Hongzhm4@pitt.eduZHM4
Committee MemberEl-Jaroudi, Amroamro@pitt.eduAMRO
Committee MemberSun, Minguidrsun@pitt.eduDRSUN
Committee MemberCoyle, James Ljcoyle@pitt.eduJCOYLE
Date: 25 January 2016
Date Type: Publication
Defense Date: 5 November 2015
Approval Date: 25 January 2016
Submission Date: 24 November 2015
Access Restriction: 5 year -- Restrict access to University of Pittsburgh for a period of 5 years.
Number of Pages: 134
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: cervical auscultation, dysphagia, deep learning, signal analysis, signal features, classification
Date Deposited: 25 Jan 2016 21:06
Last Modified: 15 Nov 2016 14:31
URI: http://d-scholarship.pitt.edu/id/eprint/26453

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