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.
LanguageEnglish
Title of host publicationUnknown Host Publication
Place of Publicationwww
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

Fingerprint

Electrocardiography
Time series
Health risks
Processing
Monitoring

Keywords

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

Cite this

@inproceedings{28578d195d0a4aeab801476de9b3b317,
title = "A Fast and Robust Time-Series Based Decision Rule for Identification of Atrial Fibrillation Arrhythmic Patterns in the ECG",
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.",
keywords = "Atrial Fibrillation, ECG Time Series, Real-time ECG Algorithm, Arrhythmia Detection, Decision Rule, AF Diagnosis, Computational Cardiology, Intelligent Processing, AF Detection",
author = "OJ Escalona and ME Reina",
year = "2010",
month = "11",
day = "15",
language = "English",
volume = "37",
pages = "995--998",
booktitle = "Unknown Host Publication",

}

Escalona, OJ & Reina, ME 2010, A Fast and Robust Time-Series Based Decision Rule for Identification of Atrial Fibrillation Arrhythmic Patterns in the ECG. in Unknown Host Publication. vol. 37, www, pp. 995-998, Computing in Cardiology, 15/11/10.

A Fast and Robust Time-Series Based Decision Rule for Identification of Atrial Fibrillation Arrhythmic Patterns in the ECG. / Escalona, OJ; Reina, ME.

Unknown Host Publication. Vol. 37 www, 2010. p. 995-998.

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

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T1 - A Fast and Robust Time-Series Based Decision Rule for Identification of Atrial Fibrillation Arrhythmic Patterns in the ECG

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AU - Reina, ME

PY - 2010/11/15

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N2 - 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.

AB - 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.

KW - Atrial Fibrillation

KW - ECG Time Series

KW - Real-time ECG Algorithm

KW - Arrhythmia Detection

KW - Decision Rule

KW - AF Diagnosis

KW - Computational Cardiology

KW - Intelligent Processing

KW - AF Detection

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