Literature DB >> 12450264

Detection of ventricular fibrillation and tachycardia from the surface ECG by a set of parameters acquired from four methods.

Irena Jekova1, Petar Mitev.   

Abstract

The recent development and increased application of automatic external defibrillators have prescribed very strong requirements towards ventricular fibrillation (VF) and fast ventricular tachycardia (VT > 180 bpm) detection from the surface electrocardiogram (ECG). We attempted to use informative parameters from several existing analysis methods and from a method developed in-house. A set of nine parameters was derived initially, with four of them being selected after statistical assessment. Detection of VF against non-shockable rhythms was obtained using the K-nearest neighbours classification method, with 98.6% specificity and 96.7% sensitivity. The detection accuracy remained high after inclusion of VT episodes above and below 180 bpm to shockable and non-shockable rhythms respectively and after the addition of noise. Test signals were taken from the well-known ECG signal databases of the American Heart Association and the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH-'cudb' and 'vfdb' files).

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Year:  2002        PMID: 12450264     DOI: 10.1088/0967-3334/23/4/303

Source DB:  PubMed          Journal:  Physiol Meas        ISSN: 0967-3334            Impact factor:   2.833


  4 in total

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Authors:  Eedara Prabhakararao; M Sabarimalai Manikandan
Journal:  Healthc Technol Lett       Date:  2016-07-29

2.  Sequential algorithm for life threatening cardiac pathologies detection based on mean signal strength and EMD functions.

Authors:  Emran M Abu Anas; Soo Y Lee; Md K Hasan
Journal:  Biomed Eng Online       Date:  2010-09-04       Impact factor: 2.819

3.  Public access defibrillation: suppression of 16.7 Hz interference generated by the power supply of the railway systems.

Authors:  Ivaylo I Christov; Georgi L Iliev
Journal:  Biomed Eng Online       Date:  2005-03-15       Impact factor: 2.819

4.  Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators.

Authors:  Carlos Figuera; Unai Irusta; Eduardo Morgado; Elisabete Aramendi; Unai Ayala; Lars Wik; Jo Kramer-Johansen; Trygve Eftestøl; Felipe Alonso-Atienza
Journal:  PLoS One       Date:  2016-07-21       Impact factor: 3.240

  4 in total

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