Year: 2019
Type: article
Abstract: Cardiac waves in Electrocardiogram (ECG) provide important information for heart conditions and effects of cardiac medications.Segmentation and analysis of ECG and its constituent cardiac waves are of high significance in cardiology diagnosis and pharmaceutical studies.Traditionally ECG analysis has been performed by well-trained clinicians and cardiologists.However, utilizing clinicians in large scale ECG screening such as drug test phases or populationbased screening programs is simply not economically feasible.Thus, an automated ECG segmentation approach that can segment cardiac waves accurately is of high importance.This dissertation studies Deep Learning (DL) based automated ECG segmentation and delineation methods.Due to various shapes and abnormalities presented in ECG signal, the traditional simple feature filters (i.e.correlators, or derivative-based filters) fail to extract the variety of cardiac wave formations.Convolutional Neural Network (ConvNet) applies multilayer feature filters on the input to extract complex features from the signal.Thus, ConvNets can be utilized to extract hierarchical i Ph.D. studies.Also, I thank my mentors at the Simulation Center, Dr.
Author: Abrishami, Hedayat
Institution: University of Cincinnati
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7065089080