Accuracy considerations in catheter based estimation of Left ventricular volume

John A. Pearce, John E. Porterfield, Erik R. Larson, Jonathan W. Valvano, Marc D. Feldman

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

4 Scopus citations

Abstract

Cardiac volume estimation in the Left Ventricle from impedance or admittance measurement is subject to two major sources of error: parallel current pathways in surrounding tissues and a non uniform current density field. The accuracy of volume estimation can be enhanced by incorporating the complex electrical properties of myocardium to identify the muscle component in the measurement and by including the transient nature of the field non uniformity. Cardiac muscle is unique in that the permittivity is high enough at audio frequencies to make the muscle component of the signal identifiable in the imaginary part of an admittance measurement. The muscle contribution can thus be uniquely identified and removed from the combined muscle - blood measurement. In general, both error sources are transient and are best removed in real time as data are collected. This paper reviews error correction methods and establishes that the relative magnitudes of the error concerns are different in small and large hearts.

Original languageEnglish (US)
Title of host publication2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Pages3556-3558
Number of pages3
DOIs
StatePublished - 2010
Event2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 - Buenos Aires, Argentina
Duration: Aug 31 2010Sep 4 2010

Publication series

Name2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10

Other

Other2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
CountryArgentina
CityBuenos Aires
Period8/31/109/4/10

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Health Informatics

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