Paper Info
Title
An Electrocardiogram Delineator Via Deep Segmentation Network
Abstract
Electrocardiogram (ECG) delineation is a process to detect multiple characteristic points, which contain critical diagnostic information about cardiac diseases. We treat the ECG delineation task as an one-dimensional segmentation problem, and propose a novel end-to-end deep learning method to segment sections of ECG signal. Our neural network consists of two parts: a segmentation network composed of multiple 1D Convolutional Neural Networks (CNN) and a postprocessing network composed of a sequential Conditional Random Field (CRF). Our method is trained and validated on QT database. The experimental results show that our method yields competitive overall performance compared with other state-of-the-art works and outperform them on onset of the P wave and offset of the T wave.
Year
DOI
Venue
2019
10.1109/EMBC.2019.8856987
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Conditional random field,Computer vision,Convolution,Convolutional neural network,Segmentation,Computer science,Image segmentation,Artificial intelligence,Deep learning,Artificial neural network,Offset (computer science)
Conference
2019
ISSN
Citations 
PageRank 
1557-170X
1
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Dongya Jia144.80
Wei Zhao221.70
Zhenqi Li343.11
Jing Hu421.36
Cong Yan532.09
Hongmei Wang63113.44
Tianyuan You721.36