A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms.
Journal:
Physiological measurement
Published Date:
Oct 24, 2018
Abstract
OBJECTIVE: Electrocardiography is the most common tool to diagnose cardiovascular diseases. Annotation, segmentation and rhythm classification of ECGs are challenging tasks, especially in the presence of atrial fibrillation and other arrhythmias. Our aim is to increase the accuracy of heart rhythm estimation by the use of extreme gradient boosting trees and the development of a deep convolutional neural network for ECG segmentation.