The Application of Synthetic Signals for ECG Beat Classification

The Application of Synthetic Signals for ECG Beat Classification

A brief overview of electrocardiogram (ECG) properties and the characteristics of various cardiac conditions is given. Two different models are used to generate synthetic ECG signals. Domain knowledge is used to create synthetic examples of 16 different heart beat types with these models. Other tech...

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Bibliographic Details
Main Author: Brown, Elliot Morgan
Format: Others
Published: BYU ScholarsArchive 2019
Subjects:
ECG
synthetic data
SMOTE
signals
classification
machine learning
neural networks
Online Access:https://scholarsarchive.byu.edu/etd/8116
https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=9116&context=etd
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spelling ndltd-BGMYU2-oai-scholarsarchive.byu.edu-etd-91162021-09-17T05:00:54Z The Application of Synthetic Signals for ECG Beat Classification Brown, Elliot Morgan A brief overview of electrocardiogram (ECG) properties and the characteristics of various cardiac conditions is given. Two different models are used to generate synthetic ECG signals. Domain knowledge is used to create synthetic examples of 16 different heart beat types with these models. Other techniques for synthesizing ECG signals are explored. Various machine learning models with different combinations of real and synthetic data are used to classify individual heart beats. The performance of the different methods and models are compared, and synthetic data is shown to be useful in beat classification. 2019-09-01T07:00:00Z text application/pdf https://scholarsarchive.byu.edu/etd/8116 https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=9116&context=etd http://lib.byu.edu/about/copyright/ Theses and Dissertations BYU ScholarsArchive ECG synthetic data SMOTE signals classification machine learning neural networks
collection NDLTD
format Others
sources NDLTD
topic ECG
synthetic data
SMOTE
signals
classification
machine learning
neural networks
spellingShingle ECG
synthetic data
SMOTE
signals
classification
machine learning
neural networks
Brown, Elliot Morgan
The Application of Synthetic Signals for ECG Beat Classification
description A brief overview of electrocardiogram (ECG) properties and the characteristics of various cardiac conditions is given. Two different models are used to generate synthetic ECG signals. Domain knowledge is used to create synthetic examples of 16 different heart beat types with these models. Other techniques for synthesizing ECG signals are explored. Various machine learning models with different combinations of real and synthetic data are used to classify individual heart beats. The performance of the different methods and models are compared, and synthetic data is shown to be useful in beat classification.
author Brown, Elliot Morgan
author_facet Brown, Elliot Morgan
author_sort Brown, Elliot Morgan
title The Application of Synthetic Signals for ECG Beat Classification
title_short The Application of Synthetic Signals for ECG Beat Classification
title_full The Application of Synthetic Signals for ECG Beat Classification
title_fullStr The Application of Synthetic Signals for ECG Beat Classification
title_full_unstemmed The Application of Synthetic Signals for ECG Beat Classification
title_sort application of synthetic signals for ecg beat classification
publisher BYU ScholarsArchive
publishDate 2019
url https://scholarsarchive.byu.edu/etd/8116
https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=9116&context=etd
work_keys_str_mv AT brownelliotmorgan theapplicationofsyntheticsignalsforecgbeatclassification
AT brownelliotmorgan applicationofsyntheticsignalsforecgbeatclassification
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