Literature DB >> 11804173

Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and deltaRR intervals.

K Tateno1, L Glass.   

Abstract

The paper describes a method for the automatic detection of atrial fibrillation, an abnormal heart rhythm, based on the sequence of intervals between heartbeats. The RR interval is the interbeat interval, and deltaRR is the difference between two successive RR intervals. Standard density histograms of the RR and deltaRR intervals were prepared as templates for atrial fibrillation detection. As the coefficients of variation of the RR and deltaRR intervals were approximately constant during atrial fibrillation, the coefficients of variation in the test data could be compared with the standard coefficients of variation (CV test). Further, the similarities between the density histograms of the test data and the standard density histograms were estimated using the Kolmogorov-Smirnov test. The CV test based on the RR intervals showed a sensitivity of 86.6% and a specificity of 84.3%. The CV test based on the deltaRR intervals showed that the sensitivity and the specificity are both approximately 84%. The Kolmogorov-Smirnov test based on the RR intervals did not improve on the result of the CV test. In contrast, the Kolmogorov-Smirnov test based on the ARR intervals showed a sensitivity of 94.4% and a specificity of 97.2%.

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Year:  2001        PMID: 11804173     DOI: 10.1007/BF02345439

Source DB:  PubMed          Journal:  Med Biol Eng Comput        ISSN: 0140-0118            Impact factor:   3.079


  4 in total

1.  Analysis of R-R intervals in patients with atrial fibrillation at rest and during exercise.

Authors:  B K Bootsma; A J Hoelsen; J Strackee; F L Meijler
Journal:  Circulation       Date:  1970-05       Impact factor: 29.690

Review 2.  AV nodal function during atrial fibrillation: the role of electrotonic modulation of propagation.

Authors:  F L Meijler; J Jalife; J Beaumont; D Vaidya
Journal:  J Cardiovasc Electrophysiol       Date:  1996-09

Review 3.  Heart rate variability preceding onset of atrial fibrillation.

Authors:  D Andresen; T Brüggemann
Journal:  J Cardiovasc Electrophysiol       Date:  1998-08

4.  Identification of atrial fibrillation episodes in ambulatory electrocardiographic recordings: validation of a method for obtaining labeled R-R interval files.

Authors:  F D Murgatroyd; B Xie; X Copie; I Blankoff; A J Camm; M Malik
Journal:  Pacing Clin Electrophysiol       Date:  1995-06       Impact factor: 1.976

  4 in total
  37 in total

1.  Loss of p21-activated kinase 1 (Pak1) promotes atrial arrhythmic activity.

Authors:  Jaime DeSantiago; Dan J Bare; Disha Varma; R John Solaro; Rishi Arora; Kathrin Banach
Journal:  Heart Rhythm       Date:  2018-04-03       Impact factor: 6.343

2.  Addressing Noise and Skewness in Interpretable Health-Condition Assessment by Learning Model Confidence.

Authors:  Yuxi Zhou; Shenda Hong; Junyuan Shang; Meng Wu; Qingyun Wang; Hongyan Li; Junqing Xie
Journal:  Sensors (Basel)       Date:  2020-12-19       Impact factor: 3.576

Review 3.  Heart rate variability indices for very short-term (30 beat) analysis. Part 1: survey and toolbox.

Authors:  Anne-Louise Smith; Harry Owen; Karen J Reynolds
Journal:  J Clin Monit Comput       Date:  2013-05-15       Impact factor: 2.502

4.  PULSE-SMART: Pulse-Based Arrhythmia Discrimination Using a Novel Smartphone Application.

Authors:  David D McMANUS; Jo Woon Chong; Apurv Soni; Jane S Saczynski; Nada Esa; Craig Napolitano; Chad E Darling; Edward Boyer; Rochelle K Rosen; Kevin C Floyd; Ki H Chon
Journal:  J Cardiovasc Electrophysiol       Date:  2015-11-13

5.  Accurate, Automated Detection of Atrial Fibrillation in Ambulatory Recordings.

Authors:  David T Linker
Journal:  Cardiovasc Eng Technol       Date:  2016-02-05       Impact factor: 2.495

6.  Wavelet leader multifractal analysis of heart rate variability in atrial fibrillation.

Authors:  Kais Gadhoumi; Duc Do; Fabio Badilini; Michele M Pelter; Xiao Hu
Journal:  J Electrocardiol       Date:  2018-08-23       Impact factor: 1.438

7.  HAN-ECG: An interpretable atrial fibrillation detection model using hierarchical attention networks.

Authors:  Sajad Mousavi; Fatemeh Afghah; U Rajendra Acharya
Journal:  Comput Biol Med       Date:  2020-10-15       Impact factor: 4.589

8.  A novel application for the detection of an irregular pulse using an iPhone 4S in patients with atrial fibrillation.

Authors:  David D McManus; Jinseok Lee; Oscar Maitas; Nada Esa; Rahul Pidikiti; Alex Carlucci; Josephine Harrington; Eric Mick; Ki H Chon
Journal:  Heart Rhythm       Date:  2012-12-06       Impact factor: 6.343

9.  Dynamic analysis of cardiac rhythms for discriminating atrial fibrillation from lethal ventricular arrhythmias.

Authors:  Deeptankar DeMazumder; Douglas E Lake; Alan Cheng; Travis J Moss; Eliseo Guallar; Robert G Weiss; Steven R Jones; Gordon F Tomaselli; J Randall Moorman
Journal:  Circ Arrhythm Electrophysiol       Date:  2013-05-16

10.  Impact of atrial fibrillation on the cardiovascular system through a lumped-parameter approach.

Authors:  Stefania Scarsoglio; Andrea Guala; Carlo Camporeale; Luca Ridolfi
Journal:  Med Biol Eng Comput       Date:  2014-09-06       Impact factor: 2.602

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