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A MACHINE LEARNING APPROACH TO ASSESS THE SEPARATION OF SEISMOCARDIOGRAPHIC SIGNALS BY RESPIRATION

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Date Issued:
2018
Abstract/Description:
The clinical usage of Seismocardiography (SCG) is increasing as it is being shown to be an effective non-invasive measurement for heart monitoring. SCG measures the vibrational activity at the chest surface and applications include non-invasive assessment of myocardial contractility and systolic time intervals. Respiratory activity can also affect the SCG signal by changing the hemodynamic characteristics of cardiac activity and displacing the position of the heart. Other clinically significant information, such as systolic time intervals, can thus manifest themselves differently in an SCG signal during inspiration and expiration. Grouping SCG signals into their respective respiratory cycle can mitigate this issue. Prior research has focused on developing machine learning classification methods to classify SCG events as according to their respiration cycle. However, recent research at the Biomedical Acoustics Research Laboratory (BARL) at UCF suggests grouping SCG signals into high and low lung volume may be more effective. This research aimed at com- paring the efficiency of grouping SCG signals according to their respiration and lung volume phase and also developing a method to automatically identify the respiration and lung volume phase of SCG events.
Title: A MACHINE LEARNING APPROACH TO ASSESS THE SEPARATION OF SEISMOCARDIOGRAPHIC SIGNALS BY RESPIRATION.
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Name(s): Solar, Brian, Author
Mansy, Hansen, Committee Chair
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2018
Publisher: University of Central Florida
Language(s): English
Abstract/Description: The clinical usage of Seismocardiography (SCG) is increasing as it is being shown to be an effective non-invasive measurement for heart monitoring. SCG measures the vibrational activity at the chest surface and applications include non-invasive assessment of myocardial contractility and systolic time intervals. Respiratory activity can also affect the SCG signal by changing the hemodynamic characteristics of cardiac activity and displacing the position of the heart. Other clinically significant information, such as systolic time intervals, can thus manifest themselves differently in an SCG signal during inspiration and expiration. Grouping SCG signals into their respective respiratory cycle can mitigate this issue. Prior research has focused on developing machine learning classification methods to classify SCG events as according to their respiration cycle. However, recent research at the Biomedical Acoustics Research Laboratory (BARL) at UCF suggests grouping SCG signals into high and low lung volume may be more effective. This research aimed at com- paring the efficiency of grouping SCG signals according to their respiration and lung volume phase and also developing a method to automatically identify the respiration and lung volume phase of SCG events.
Identifier: CFH2000310 (IID), ucf:45877 (fedora)
Note(s): 2018-05-01
B.S.M.E.
College of Engineering and Computer Science, Mechanical and Aerospace Engineering
Bachelors
This record was generated from author submitted information.
Subject(s): machine learning
seismocardiography
cardio-respiratory
lung volume
classification
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFH2000310
Restrictions on Access: public
Host Institution: UCF

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