Arrhythmia discrimination using a smart phone

Author

Chong, Jo Woon ; McManus, David D. ; Chon, Ki H.

Author_Institution

Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA

fYear

2013

fDate

6-9 May 2013

Firstpage

1

Lastpage

4

Abstract

We propose an arrhythmia discrimination algorithm for a smart phone that can reliably distinguish among normal sinus rhythm (NSR), atrial fibrillation (AF), premature ventricular contractions (PVCs) and premature atrial contraction (PACs). To evaluate the algorithm in clinical application, we recruited 27 subjects with 3 PVC and 4 PAC subjects as well as 20 AF pre- and post-electrical cardioversion. From each subjects, two-minute pulsatile time series from a fingertip is measured using a smart phone. Our arrhythmia discrimination approach combines Poincare plot and Kulback-Leibler (KL) divergence with Root Mean Square of Successive RR Differences (RMSSD) and Shannon Entropy (ShE). Clinical results show that our algorithm discriminates PVC and PAC with accuracy of 100% and 97.87%, respectively.

Keywords

Accuracy; Atrial fibrillation; Entropy; Monitoring; Picture archiving and communication systems; Smart phones; Time series analysis; Kullback-Leibler divergence; Poincare plot; Shannon entropy; atrial fibrillation; premature atrial contraction; premature ventricular contraction; turning point ratio;

fLanguage

English

Publisher

ieee

Conference_Titel

Body Sensor Networks (BSN), 2013 IEEE International Conference on

Conference_Location

Cambridge, MA, USA

ISSN

2325-1425

Print_ISBN

978-1-4799-0331-3

Type

conf

DOI

10.1109/BSN.2013.6575493

Filename

6575493