Automated Prediction of Spontaneous Termination of Paroxysmal Atrial Fibrillation Using Support Vector Machine

Author

Huang, Zhongchao ; Chen, Zhencheng ; Zhao, Yuqian

Author_Institution

Sch. of Info-Phys. & Geomatics Eng., Central South Univ., Changsha

fYear

2007

fDate

6-8 July 2007

Firstpage

625

Lastpage

628

Abstract

Based on our previous work, we develop a classifier using support vector machine to differentiate paroxysmal atrial fibrillation (AF) from sustained AF and propose an automatic scheme to predict the spontaneous termination of paroxysmal AF. Experimental studies over the challenge database of Physionet/CinC 2004 show that our method is able to achieve high classification accuracy and is fairly reliable. This in turn justifies our previous work.

Keywords

diseases; electrocardiography; medical signal processing; signal classification; support vector machines; ECG signals; automated prediction analysis; cardiac arrhythmia; classification method; paroxysmal atrial fibrillation; support vector machine; Artificial neural networks; Atrial fibrillation; Biomedical engineering; Data engineering; Databases; Electrocardiography; Electronic mail; Support vector machine classification; Support vector machines; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Biomedical Engineering, 2007. ICBBE 2007. The 1st International Conference on

Conference_Location

Wuhan

Print_ISBN

1-4244-1120-3

Type

conf

DOI

10.1109/ICBBE.2007.163

Filename

4272647

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3049576