Paper Info
Title
Diagnosis Of Af Based On Time And Frequency Features By Using A Hierarchical Classifier
Abstract
Early diagnosis of Atrial Fibrillation (AF) could be benefited from automatic analysis of a short single-lead ECG recording that can be collected easily by a portable device. Due to the limitations of both quantity and quality of the signal, it is challenging to distinguish AF from a broad taxonomy of rhythms. This paper presents a new method which classifies the recordings of single lead ECGs by combined time and time-frequency features. The time features of a recording are represented by some characteristics of its RR intervals and Poincare plot, while the time-frequency features are extracted from its representative beat waveforms by Matching Pursuits algorithm. A set of methods are adopted in the process to eliminate the effects of noise. With the features extracted, a hierarchical classifier is trained based on the CinC Challenge 2017 dataset to classify the recordings into four classes: normal sinus rhythm, AF, other rhythm and too noisy to classify. The final score of our work in the CinC Challenge 2017 is 0.78.
Year
DOI
Venue
2017
10.22489/CinC.2017.180-102
2017 COMPUTING IN CARDIOLOGY (CINC)
Field
DocType
Volume
Atrial fibrillation,Pattern recognition,Computer science,Normal Sinus Rhythm,Artificial intelligence,Beat (music),Hierarchical classifier,Rhythm,Poincaré plot
Conference
44
ISSN
Citations 
PageRank 
2325-8861
0
0.34
References 
Authors
1
8
Name
Order
Citations
PageRank
Yang Liu1294.05
Kuanquan Wang21617141.11
Qince Li379.91
Runnan He474.84
Yong Xia5204.68
Zhen Li639790.65
Hao Liu721259.74
Henggui Zhang810551.88