Date of Award
Spring 2019
Document Type
Thesis
Publisher
Santa Clara : Santa Clara University, 2019.
Degree Name
Master of Science (MS)
Department
Bioengineering
First Advisor
Yuling Yan
Abstract
In this study, a novel Atrial Fibrillation (AFib) detection algorithm is presented based on Electrocardiography (ECG) signals. In particular, the spectrogram of ECG signal is used as an input to a Convolutional Neural Network (CNN) to classify normal and AFib ECG signals. This model is shown to perform well with an accuracy of 92.91% and a value of 0.9789 for the area under the ROC curve (AUC). This study demonstrated the potential of using image classification methods and CNN model to detect abnormal biosignals with noise.
Recommended Citation
Lu, Senbao, "Automated Atrial Fibrillation Detection from Electrocardiogram" (2019). Bioengineering Master's Theses. 5.
https://scholarcommons.scu.edu/bioe_mstr/5