Arrhythmia discrimination using a smart phone

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

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.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Body Sensor Networks, BSN 2013
DOIs
StatePublished - 2013
Event2013 IEEE International Conference on Body Sensor Networks, BSN 2013 - Cambridge, MA, United States
Duration: May 6 2013May 9 2013

Publication series

Name2013 IEEE International Conference on Body Sensor Networks, BSN 2013

Conference

Conference2013 IEEE International Conference on Body Sensor Networks, BSN 2013
Country/TerritoryUnited States
CityCambridge, MA
Period05/6/1305/9/13

Keywords

  • Kullback-Leibler divergence
  • Poincare plot
  • Shannon entropy
  • atrial fibrillation
  • premature atrial contraction
  • premature ventricular contraction
  • turning point ratio

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