Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Improved EASI coefficients: their derivation, values, and performance.

Dirk Q Feild1, Charles L Feldman, B Milan Horácek

  • 1Advanced Algorithm Research Center, Philips Medical Systems, Oxnard, CA 93030, USA. dirk.feild@philips.com

Journal of Electrocardiology
|January 23, 2003
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Hybrid Neural State-Space Modeling for Supervised and Unsupervised Electrocardiographic Imaging.

IEEE transactions on medical imaging·2024
Same author

Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters <i>via</i> Bayesian Active Learning.

Frontiers in physiology·2021
Same author

Assessment of an ECG-Based System for Localizing Ventricular Arrhythmias in Patients With Structural Heart Disease.

Journal of the American Heart Association·2021
Same author

Learning to Disentangle Inter-Subject Anatomical Variations in Electrocardiographic Data.

IEEE transactions on bio-medical engineering·2021
Same author

Prospective Multicenter Assessment of a New Intraprocedural Automated System for Localizing Idiopathic Ventricular Arrhythmia Origins.

JACC. Clinical electrophysiology·2021
Same author

Feasibility study shows concordance between image-based virtual-heart ablation targets and predicted ECG-based arrhythmia exit-sites.

Pacing and clinical electrophysiology : PACE·2021

This study introduces improved EASI system coefficients for deriving standard 12-lead electrocardiograms (ECG) from fewer electrode placements. New coefficients, based on a large dataset, enhance ECG accuracy for clinical use.

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Medical Instrumentation

Background:

  • The EASI lead system enables electrocardiogram (ECG) derivation from 4 electrode sites, simplifying lead application and freeing the precordium.
  • Previous EASI implementations had accuracy limitations, attributed to coefficients derived from limited datasets.

Purpose of the Study:

  • To develop and validate a new set of EASI coefficients for deriving standard 12-lead ECG and other leads.
  • To improve the accuracy of ECGs derived from the EASI system using a comprehensive dataset.

Main Methods:

  • Calculated new EASI coefficients using a dataset of 983 adult subjects with 120-lead ECGs and documented diagnoses.
  • Derived coefficients for standard 12 leads, Frank orthogonal leads, posterior and right-sided leads, and vessel-specific leads.

Related Experiment Videos

  • Optimized coefficients by maximizing the correlation between derived and true leads, with amplitude adjustments for best fit over specific ECG intervals.

    • Main Results:

    • A new comprehensive set of EASI coefficients was calculated for standard and additional ECG leads.
    • The new coefficients were derived from a large and diverse dataset of 983 subjects.
    • Goodness-of-fit measures for all derived coefficients are presented.

    Conclusions:

    • The refined EASI coefficients offer improved accuracy for deriving standard and other ECG leads.
    • This advancement facilitates more precise ECG analysis with simplified electrode placement.
    • The updated coefficients are applicable to both standard and modified EASI electrode configurations.