Minimization of the Wilson's Central Terminal voltage potential via a genetic algorithm

Hossein Moeinzadeh, Paolo Bifulco, Mario Cesarelli, Alistair L. McEwan, Aiden O’Loughlin, Ibrahim M. Shugman, Jonathan C. Tapson, Aravinda Thiagalingam, Gaetano D. Gargiulo

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Objective: The Wilson Central Terminal (WCT) is an artifcially constructed reference for surface electrocardiography, which is assumed to be near zero and steady during the cardiac cycle; namely it is the simple average of the three recorded limbs (right arm, left arm and left leg) composing the Einthoven triangle and considered to be electrically equidistant from the electrical center of the heart. This assumption has been challenged and disproved in 1954 with an experiment designed just to measure and minimize WCT. Minimization was attempted varying in real time the weight resistors connected to the limbs. Unfortunately, the experiment required a very cumbersome setup and showed that WCT amplitude could not be universally minimized, in other words, the weight resistors change for each person. Taking advantage of modern computation techniques as well as of a special ECG device that aside of the standard 12-lead Electrocardiogram (ECG) can measure WCT components, we propose a software minimization (genetic algorithm) method using data recorded from 72 volunteers. Result: We show that while the WCT presents average amplitude relative to lead II of 58.85% (standard deviation of 30.84%), our minimization method yields an amplitude as small as 7.45% of lead II (standard deviation of 9.04%).
Original languageEnglish
Article number915
Number of pages5
JournalBMC Research Notes
Volume11
Issue number1
DOIs
Publication statusPublished - 2018

Open Access - Access Right Statement

© The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/ publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Keywords

  • electrocardiography
  • genetic algorithms

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