Literature DB >> 15535182

Real time detection of ventricular fibrillation and tachycardia.

Irena Jekova1, Vessela Krasteva.   

Abstract

The automatic external defibrillator (AED) is a lifesaving device, which processes and analyses the electrocardiogram (ECG) and delivers a defibrillation shock to terminate ventricular fibrillation or tachycardia above 180 bpm. The built-in algorithm for ECG analysis has to discriminate between shockable and non-shockable rhythms and its accuracy, represented by sensitivity and specificity, is aimed at approaching the maximum values of 100%. An algorithm for VF/VT detection is proposed using a band-pass digital filter with integer coefficients, which is very simple to implement in real-time operation. A branch for wave detection is activated for heart rate measurement and an auxiliary parameter calculation. The method was tested with ECG records from the widely recognized databases of the American Heart Association (AHA) and the Massachusetts Institute of Technology (MIT). A sensitivity of 95.93% and a specificity of 94.38% were obtained.

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Year:  2004        PMID: 15535182     DOI: 10.1088/0967-3334/25/5/007

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


  10 in total

1.  Detection of Shockable Ventricular Arrhythmia using Variational Mode Decomposition.

Authors:  R K Tripathy; L N Sharma; S Dandapat
Journal:  J Med Syst       Date:  2016-01-21       Impact factor: 4.460

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.  Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms.

Authors:  Vessela Krasteva; Sarah Ménétré; Jean-Philippe Didon; Irena Jekova
Journal:  Sensors (Basel)       Date:  2020-05-19       Impact factor: 3.576

5.  Detection of Life Threatening Ventricular Arrhythmia Using Digital Taylor Fourier Transform.

Authors:  Rajesh K Tripathy; Alejandro Zamora-Mendez; José A de la O Serna; Mario R Arrieta Paternina; Juan G Arrieta; Ganesh R Naik
Journal:  Front Physiol       Date:  2018-06-13       Impact factor: 4.566

6.  Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia.

Authors:  Artzai Picon; Unai Irusta; Aitor Álvarez-Gila; Elisabete Aramendi; Felipe Alonso-Atienza; Carlos Figuera; Unai Ayala; Estibaliz Garrote; Lars Wik; Jo Kramer-Johansen; Trygve Eftestøl
Journal:  PLoS One       Date:  2019-05-20       Impact factor: 3.240

7.  Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest.

Authors:  Andoni Elola; Elisabete Aramendi; Unai Irusta; Artzai Picón; Erik Alonso; Pamela Owens; Ahamed Idris
Journal:  Entropy (Basel)       Date:  2019-03-21       Impact factor: 2.524

8.  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

9.  Automated Method for Discrimination of Arrhythmias Using Time, Frequency, and Nonlinear Features of Electrocardiogram Signals.

Authors:  Shirin Hajeb-Mohammadalipour; Mohsen Ahmadi; Reza Shahghadami; Ki H Chon
Journal:  Sensors (Basel)       Date:  2018-06-29       Impact factor: 3.576

10.  A Machine Learning Model for the Prognosis of Pulseless Electrical Activity during Out-of-Hospital Cardiac Arrest.

Authors:  Jon Urteaga; Elisabete Aramendi; Andoni Elola; Unai Irusta; Ahamed Idris
Journal:  Entropy (Basel)       Date:  2021-06-30       Impact factor: 2.524

  10 in total

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