Respiratory Information Extraction from Electrocardiogram Signals

Handle URI:
http://hdl.handle.net/10754/133969
Title:
Respiratory Information Extraction from Electrocardiogram Signals
Authors:
Amin, Gamal El Din Fathy
Abstract:
The Electrocardiogram (ECG) is a tool measuring the electrical activity of the heart, and it is extensively used for diagnosis and monitoring of heart diseases. The ECG signal reflects not only the heart activity but also many other physiological processes. The respiratory activity is a prominent process that affects the ECG signal due to the close proximity of the heart and the lungs. In this thesis, several methods for the extraction of respiratory process information from the ECG signal are presented. These methods allow an estimation of the lung volume and the lung pressure from the ECG signal. The potential benefit of this is to eliminate the corresponding sensors used to measure the respiration activity. A reduction of the number of sensors connected to patients will increase patients’ comfort and reduce the costs associated with healthcare. As a further result, the efficiency of diagnosing respirational disorders will increase since the respiration activity can be monitored with a common, widely available method. The developed methods can also improve the detection of respirational disorders that occur while patients are sleeping. Such disorders are commonly diagnosed in sleeping laboratories where the patients are connected to a number of different sensors. Any reduction of these sensors will result in a more natural sleeping environment for the patients and hence a higher sensitivity of the diagnosis.
Advisors:
Kosel, Jürgen ( 0000-0002-8998-8275 )
Committee Member:
Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim ( 0000-0003-4827-1793 )
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Program:
Electrical Engineering
Issue Date:
Dec-2010
Type:
Thesis
Appears in Collections:
Theses; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.advisorKosel, Jürgenen
dc.contributor.authorAmin, Gamal El Din Fathyen
dc.date.accessioned2011-06-21T08:48:29Z-
dc.date.available2011-06-21T08:48:29Z-
dc.date.issued2010-12en
dc.identifier.urihttp://hdl.handle.net/10754/133969en
dc.description.abstractThe Electrocardiogram (ECG) is a tool measuring the electrical activity of the heart, and it is extensively used for diagnosis and monitoring of heart diseases. The ECG signal reflects not only the heart activity but also many other physiological processes. The respiratory activity is a prominent process that affects the ECG signal due to the close proximity of the heart and the lungs. In this thesis, several methods for the extraction of respiratory process information from the ECG signal are presented. These methods allow an estimation of the lung volume and the lung pressure from the ECG signal. The potential benefit of this is to eliminate the corresponding sensors used to measure the respiration activity. A reduction of the number of sensors connected to patients will increase patients’ comfort and reduce the costs associated with healthcare. As a further result, the efficiency of diagnosing respirational disorders will increase since the respiration activity can be monitored with a common, widely available method. The developed methods can also improve the detection of respirational disorders that occur while patients are sleeping. Such disorders are commonly diagnosed in sleeping laboratories where the patients are connected to a number of different sensors. Any reduction of these sensors will result in a more natural sleeping environment for the patients and hence a higher sensitivity of the diagnosis.en
dc.language.isoenen
dc.subjectElectro Cardiogram (ECG) Signalen
dc.subjectHeart Diseasesen
dc.titleRespiratory Information Extraction from Electrocardiogram Signalsen
dc.typeThesisen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
thesis.degree.grantorKing Abdullah University of Science and Technologyen_GB
dc.contributor.committeememberAl-Naffouri, Tareq Y.en
dc.contributor.committeememberAlouini, Mohamed-Slimen
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.nameMaster of Scienceen
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