Abstract
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.
Degree
MS
College and Department
Physical and Mathematical Sciences; Mathematics
Rights
http://lib.byu.edu/about/copyright/
BYU ScholarsArchive Citation
Brown, Elliot Morgan, "The Application of Synthetic Signals for ECG Beat Classification" (2019). Theses and Dissertations. 8116.
https://scholarsarchive.byu.edu/etd/8116
Date Submitted
2019-09-01
Document Type
Thesis
Handle
http://hdl.lib.byu.edu/1877/etd11068
Keywords
ECG, synthetic data, SMOTE, signals, classification, machine learning, neural networks
Language
English