Literature DB >> 17027837

Vectorcardiographic lead systems for the characterization of atrial fibrillation.

Adriaan van Oosterom1, Zenichi Ihara, Vincent Jacquemet, Rudi Hoekema.   

Abstract

OBJECTIVE: The aim of the study was to design a vectorcardiographic lead system dedicated to the analysis of atrial fibrillation (AF).
METHODS: Body surface potentials during AF were simulated by using a biophysical model of the human atria and thorax. The XYZ components of the equivalent dipole were derived from the Gabor-Nelson equations. These served as the gold standard while searching for an optimal orthogonal lead system for the estimation of the heart vector while using a limited number of electrode positions. Six electrode configurations and their dedicated transfer matrices were tested by using 10 different episodes of simulated AF and 25 different thorax geometries.
RESULTS: Root-mean-square-based relative estimation error of the vectorcardiogram using the Frank electrodes was 0.39. An adaptation of 4 of the 9 electrode locations of the standard electrocardiogram, with 1 electrode moved to the back, reduced the error to 0.24.
CONCLUSION: The Frank lead system is suboptimal for estimating the equivalent dipole components (VCG) during AF. Alternative electrode configurations should include at least 1 electrode on the back.

Entities:  

Mesh:

Year:  2006        PMID: 17027837     DOI: 10.1016/j.jelectrocard.2006.08.002

Source DB:  PubMed          Journal:  J Electrocardiol        ISSN: 0022-0736            Impact factor:   1.438


  7 in total

1.  Frontal plane vectorcardiograms: theory and graphics visualization of cardiac health status.

Authors:  Dhanjoo N Ghista; U Rajendra Acharya; T Nagenthiran
Journal:  J Med Syst       Date:  2009-02-21       Impact factor: 4.460

2.  Non-Invasive Estimation Of Left Atrial Dominant Frequency In Atrial Fibrillation From Different Electrode Sites: Insight From Body Surface Potential Mapping.

Authors:  Marjan Bojarnejad; James R Blake; John Bourke; Ewan Shepherd; Alan Murray; Philip Langley
Journal:  J Atr Fibrillation       Date:  2014-10-31

3.  Spatiotemporal representation of cardiac vectorcardiogram (VCG) signals.

Authors:  Hui Yang; Satish Ts Bukkapatnam; Ranga Komanduri
Journal:  Biomed Eng Online       Date:  2012-03-30       Impact factor: 2.819

4.  Detection and classification of cardiac ischemia using vectorcardiogram signal via neural network.

Authors:  Ali Reza Mehri Dehnavi; Iman Farahabadi; Hossain Rabbani; Amin Farahabadi; Mohamad Parsa Mahjoob; Nasser Rajabi Dehnavi
Journal:  J Res Med Sci       Date:  2011-02       Impact factor: 1.852

5.  Noninvasive Assessment of Atrial Fibrillation Complexity in Relation to Ablation Characteristics and Outcome.

Authors:  Marianna Meo; Thomas Pambrun; Nicolas Derval; Carole Dumas-Pomier; Stéphane Puyo; Josselin Duchâteau; Pierre Jaïs; Mélèze Hocini; Michel Haïssaguerre; Rémi Dubois
Journal:  Front Physiol       Date:  2018-07-17       Impact factor: 4.566

Review 6.  Review of Processing Pathological Vectorcardiographic Records for the Detection of Heart Disease.

Authors:  Jaroslav Vondrak; Marek Penhaker
Journal:  Front Physiol       Date:  2022-03-21       Impact factor: 4.755

7.  A simplified 3D model of whole heart electrical activity and 12-lead ECG generation.

Authors:  Siniša Sovilj; Ratko Magjarević; Nigel H Lovell; Socrates Dokos
Journal:  Comput Math Methods Med       Date:  2013-04-22       Impact factor: 2.238

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.