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.
College and Department
Physical and Mathematical Sciences; Mathematics
BYU ScholarsArchive Citation
Brown, Elliot Morgan, "The Application of Synthetic Signals for ECG Beat Classification" (2019). Theses and Dissertations. 8116.
ECG, synthetic data, SMOTE, signals, classification, machine learning, neural networks