Heartbeat detection, classification and coupling analysis using Electrocardiography data

Heartbeat detection, classification and coupling analysis using Electrocardiography data

Bibliographic Details
Main Author: Li, Yelei
Language:English
Published: Case Western Reserve University School of Graduate Studies / OhioLINK 2014
Subjects:
Biomedical Engineering
Health Care
Information Technology
Mathematics
Statistics
Electrocardiography
QRS detection
heartbeat classification
cardio-respiratory coupling
recurrence properties
phase space reconstruction
ECG signal processing
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=case1405084050
id ndltd-OhioLink-oai-etd.ohiolink.edu-case1405084050
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-case14050840502021-08-03T06:25:46Z Heartbeat detection, classification and coupling analysis using Electrocardiography data Li, Yelei Biomedical Engineering Health Care Information Technology Mathematics Statistics Electrocardiography QRS detection heartbeat classification cardio-respiratory coupling recurrence properties phase space reconstruction ECG signal processing The recording and analysis of electroencephalography (ECG) plays crucial roles in clinical research and diagnosis. As a result, the development of automatic ECG analysis algorithms has been rapidly growing in recent decades. However, conventional ECG analysis encounters tradeoff between computational cost and performance accuracy. This study aims to develop a series of real-time (online) ECG analysis algorithms that include heartbeat detection and ECG arrhythmia classification. We first propose a novel phase space based method for heartbeat detection that maps the ECG data into a two-dimensional reconstructed attractor. Unlike conventional algorithms, our detector replaces the preprocessing stage with a reconstruction process. This improvement highly reduces the computational cost. Moreover, we introduce a two-dimensional decision mechanism in order to obtain high performance accuracy at the detection stage. For the ECG arrhythmia classification study, an unsupervised classification algorithm referred to as “superparamagnetic clustering” is introduced to ECG analysis field for the first time. Current studies in ECG classification mainly use supervised artificial intelligence methods. The common drawbacks of these classifiers include: they are incapable of discriminating clusters with significant population differences; manual annotation efforts by clinicians/researchers are required in order to form the training sets; the network training procedure is computationally expensive. The proposed arrhythmia ECG classifier overcomes these issues because of the non-parametric configuration of the classifier. Clustering with different desirable discrimination levels could be realized by adjusting the “temperature” parameter. Moreover, this study explicitly involves exploration of the feature selection issue. To ensure the most reliable configuration, an appropriate number of the most significant features should be selected from the candidate pool. A comparative study between principal component analysis (PCA) and genetic algorithm analysis (GA) is conducted with the conclusion that the 10-15 principal components from PCA is the optimal combination for ECG arrhythmia classification. We also study the dynamic interactions between cardiac contraction and respiration using time-frequency analysis tools such as Hilbert and wavelet transforms as well as the recurrence properties of the system. The analysis results show a unidirectional modulation relationship in the cardio-respiratory system as well as the existence of thoracic-abdominal asynchrony during certain periods that include significant clinical events. 2014-09-02 English text Case Western Reserve University School of Graduate Studies / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=case1405084050 http://rave.ohiolink.edu/etdc/view?acc_num=case1405084050 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Biomedical Engineering
Health Care
Information Technology
Mathematics
Statistics
Electrocardiography
QRS detection
heartbeat classification
cardio-respiratory coupling
recurrence properties
phase space reconstruction
ECG signal processing
spellingShingle Biomedical Engineering
Health Care
Information Technology
Mathematics
Statistics
Electrocardiography
QRS detection
heartbeat classification
cardio-respiratory coupling
recurrence properties
phase space reconstruction
ECG signal processing
Li, Yelei
Heartbeat detection, classification and coupling analysis using Electrocardiography data
author Li, Yelei
author_facet Li, Yelei
author_sort Li, Yelei
title Heartbeat detection, classification and coupling analysis using Electrocardiography data
title_short Heartbeat detection, classification and coupling analysis using Electrocardiography data
title_full Heartbeat detection, classification and coupling analysis using Electrocardiography data
title_fullStr Heartbeat detection, classification and coupling analysis using Electrocardiography data
title_full_unstemmed Heartbeat detection, classification and coupling analysis using Electrocardiography data
title_sort heartbeat detection, classification and coupling analysis using electrocardiography data
publisher Case Western Reserve University School of Graduate Studies / OhioLINK
publishDate 2014
url http://rave.ohiolink.edu/etdc/view?acc_num=case1405084050
work_keys_str_mv AT liyelei heartbeatdetectionclassificationandcouplinganalysisusingelectrocardiographydata
_version_ 1719436303591800832

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