Literature DB >> 9473845

Detection of atrial activity from high-voltage leads of implantable ventricular defibrillators using a cancellation technique.

S Shkurovich1, A V Sahakian, S Swiryn.   

Abstract

The inability to detect atrial activity limits implantable ventricular cardioverter defibrillators (ICD) in discriminating tachycardias and can result in inappropriate therapy. This study attempted to detect atrial activity on the wide-spaced bipole signals formed by the high-voltage (HV) leads of the ICD during device implantation and to develop an algorithm for the detection of atrial fibrillation (AFib) from these signals. We used a method that canceled ventricular and correlated atrial activity from the HV lead signals and measured frequency and amplitude distribution information to discriminate sinus rhythm (SR) and AFib segments. We analyzed 186 data segments from 21 patients (six AFib, 14 SR, one AFib and SR). For individual segments in this data set, the sensitivity of the algorithm was 78%, specificity 92.65%, positive and negative predictive values 79.59 and 91.97%, respectively. These results demonstrate that atrial activity is present in the HV lead signals, and AFib detection can be achieved in many, but not all cases, using information currently available to ICD's. Prior work from surface electrocardiograms suggests that this algorithm can function during ventricular tachycardias. However, specificity of the algorithm is not high enough for clinical use.

Entities:  

Mesh:

Year:  1998        PMID: 9473845     DOI: 10.1109/10.661270

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  8 in total

1.  Characterisation of human AV-nodal properties using a network model.

Authors:  Mikael Wallman; Frida Sandberg
Journal:  Med Biol Eng Comput       Date:  2017-07-13       Impact factor: 2.602

2.  Measure of synchronisation of right atrial depolarisation wavefronts during atrial fibrillation.

Authors:  V Barbaro; P Bartolini; G Calcagnini; F Censi; A Michelucci
Journal:  Med Biol Eng Comput       Date:  2002-01       Impact factor: 2.602

3.  A non-invasive method to predict electrical cardioversion outcome of persistent atrial fibrillation.

Authors:  Raúl Alcaraz; José Joaquín Rieta
Journal:  Med Biol Eng Comput       Date:  2008-04-24       Impact factor: 2.602

4.  Analysis of QRS-T subtraction in unipolar atrial fibrillation electrograms.

Authors:  J L Salinet; J P V Madeiro; P C Cortez; P J Stafford; G André Ng; G André Ng; F S Schlindwein
Journal:  Med Biol Eng Comput       Date:  2013-04-07       Impact factor: 2.602

5.  Rotor Tracking Using Phase of Electrograms Recorded During Atrial Fibrillation.

Authors:  Caroline H Roney; Chris D Cantwell; Norman A Qureshi; Rasheda A Chowdhury; Emmanuel Dupont; Phang Boon Lim; Edward J Vigmond; Jennifer H Tweedy; Fu Siong Ng; Nicholas S Peters
Journal:  Ann Biomed Eng       Date:  2016-12-05       Impact factor: 3.934

6.  Spatial concentration and distribution of phase singularities in human atrial fibrillation: Insights for the AF mechanism.

Authors:  Madeline Schopp; Dhani Dharmaprani; Pawel Kuklik; Jing Quah; Anandaroop Lahiri; Kathryn Tiver; Christian Meyer; Stephan Willems; Andrew D McGavigan; Anand N Ganesan
Journal:  J Arrhythm       Date:  2021-06-19

7.  Time-Averaged Wavefront Analysis Demonstrates Preferential Pathways of Atrial Fibrillation, Predicting Pulmonary Vein Isolation Acute Response.

Authors:  Caroline H Roney; Nicholas Child; Bradley Porter; Iain Sim; John Whitaker; Richard H Clayton; Jacob I Laughner; Allan Shuros; Petr Neuzil; Steven E Williams; Reza S Razavi; Mark O'Neill; Christopher A Rinaldi; Peter Taggart; Matt Wright; Jaswinder S Gill; Steven A Niederer
Journal:  Front Physiol       Date:  2021-09-27       Impact factor: 4.755

8.  F-Wave Extraction from Single-Lead Electrocardiogram Signals with Atrial Fibrillation by Utilizing an Optimized Resonance-Based Signal Decomposition Method.

Authors:  Junjiang Zhu; Jintao Lv; Dongdong Kong
Journal:  Entropy (Basel)       Date:  2022-06-10       Impact factor: 2.738

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.