A Fast and Robust Time-Series Based Decision Rule for Identification of Atrial Fibrillation Arrhythmic Patterns in the ECG

OJ Escalona, ME Reina

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Atrial fibrillation (AF) is an arrhythmic behaviour of the heart, which occurs when the myocardium of the atrial chambers enter into a sustained chaotic and fractionated muscular contraction dynamic. Reliable detection of AF episodes in ECG monitoring devices, is important for early treatment and health risks reduction. A decision rule for identifying AF arrhythmic patterns was derived from RR-intervals analysis of time-seriesgenerated from ECG recordings before, during and after AF episodes. Time-series elements were obtained by consecutive RR intervals time differences (dRR). In theproposed decision rule, two arguments must be satisfied for identifying an AF pattern within a window of 35 beats: (1) the number of dRR elements above 50 msabsolute value, is >10, and (2) there is a uniform dispersion of all the corresponding RR-interval elements within the same 35 beat window. Detection of AF using the proposed decision rule scheme was achieved with 96% exactitude, 93% sensitivity and 97% specificity. The longest case of processing time per ECG beat was of 129ms. Thiscomputing time requirement can enable real-time ECG processing algorithms for AF identification.
Original languageEnglish
Title of host publicationUnknown Host Publication
Place of Publicationwww
PublisherIEEE
Pages995-998
Number of pages4
Volume37
Publication statusPublished - 15 Nov 2010
EventComputing in Cardiology - Belfast-UK
Duration: 15 Nov 2010 → …
http://www.cinc.org

Conference

ConferenceComputing in Cardiology
Period15/11/10 → …
Internet address

Keywords

  • Atrial Fibrillation
  • ECG Time Series
  • Real-time ECG Algorithm
  • Arrhythmia Detection
  • Decision Rule
  • AF Diagnosis
  • Computational Cardiology
  • Intelligent Processing
  • AF Detection

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