Arrhythmia recognition from electrocardiogram using non-linear analysis and unsupervised clustering techniques

In this dissertation, a new analytical framework for arrhythmia recognition in ECG signals using nonlinear analysis and unsupervised clustering techniques is developed. The problem of ECG signal conditioning, ECG episode characterization, characteristic wave detection, and arrhythmia recognition, ha...

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Bibliographic Details
Main Author:	Sun, Yan.
Other Authors:	Chan, Kap Luk
Format:	Theses and Dissertations
Published:	2008
Subjects:	DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics
Online Access:	http://hdl.handle.net/10356/3312
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Institution:	Nanyang Technological University

id	sg-ntu-dr.10356-3312
record_format	dspace
spelling	sg-ntu-dr.10356-33122023-07-04T16:40:53Z Arrhythmia recognition from electrocardiogram using non-linear analysis and unsupervised clustering techniques Sun, Yan. Chan, Kap Luk School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics In this dissertation, a new analytical framework for arrhythmia recognition in ECG signals using nonlinear analysis and unsupervised clustering techniques is developed. The problem of ECG signal conditioning, ECG episode characterization, characteristic wave detection, and arrhythmia recognition, have been tackled in this thesis. Doctor of Philosophy (EEE) 2008-09-17T09:27:09Z 2008-09-17T09:27:09Z 2002 2002 Thesis http://hdl.handle.net/10356/3312 Nanyang Technological University application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
topic	DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics Sun, Yan. Arrhythmia recognition from electrocardiogram using non-linear analysis and unsupervised clustering techniques
description	In this dissertation, a new analytical framework for arrhythmia recognition in ECG signals using nonlinear analysis and unsupervised clustering techniques is developed. The problem of ECG signal conditioning, ECG episode characterization, characteristic wave detection, and arrhythmia recognition, have been tackled in this thesis.
author2	Chan, Kap Luk
author_facet	Chan, Kap Luk Sun, Yan.
format	Theses and Dissertations
author	Sun, Yan.
author_sort	Sun, Yan.
title	Arrhythmia recognition from electrocardiogram using non-linear analysis and unsupervised clustering techniques
title_short	Arrhythmia recognition from electrocardiogram using non-linear analysis and unsupervised clustering techniques
title_full	Arrhythmia recognition from electrocardiogram using non-linear analysis and unsupervised clustering techniques
title_fullStr	Arrhythmia recognition from electrocardiogram using non-linear analysis and unsupervised clustering techniques
title_full_unstemmed	Arrhythmia recognition from electrocardiogram using non-linear analysis and unsupervised clustering techniques
title_sort	arrhythmia recognition from electrocardiogram using non-linear analysis and unsupervised clustering techniques
publishDate	2008
url	http://hdl.handle.net/10356/3312
_version_	1772827503248801792

Arrhythmia recognition from electrocardiogram using non-linear analysis and unsupervised clustering techniques

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