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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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 |
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NTU Library |
| continent |
Asia |
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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 |