Cardiac arrhythmia classification in 12-lead ECG using synthetic atrial activity signal

Or Perlman, Yaniv Zigel, Guy Amit, Amos Katz

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

Abstract

Analysis of the ECG signal is the prevalent method for diagnosing cardiac arrhythmia. In order to achieve a precise diagnosis, the physician must carefully examine the quantity, location, and relations between the ECG signal elements, with emphasis given to the atrial electrical activity (AEA) wave characteristics. Nevertheless, in some cases the AEA-waves are hidden in other waves, and in order to classify the correct arrhythmia an invasive procedure is performed. We propose a fully automated computer-based method for arrhythmia classification, based on our recently developed AEA detection algorithm, combined with two extracted rhythm-based features and a clinically oriented set of rules. Twenty-nine patients presenting atrioventricular nodal reentry tachycardia, atrioventricular reentry tachycardia, sinus tachycardia, atrial flutter, and sinus rhythm were studied. The arrhythmia classifier achieved 92.2% accuracy, 83.9% sensitivity, and 94.9% specificity.

Original languageEnglish
Title of host publication2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012 - Eilat, Israel
Duration: 14 Nov 201217 Nov 2012

Publication series

Name2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012

Conference

Conference2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012
Country/TerritoryIsrael
CityEilat
Period14/11/1217/11/12

Keywords

  • arrhythmia classification
  • atrial electrical activity
  • ECG
  • signal processing

Fingerprint

Dive into the research topics of 'Cardiac arrhythmia classification in 12-lead ECG using synthetic atrial activity signal'. Together they form a unique fingerprint.

Cite this