Literature DB >> 11110242

Comparison of five algorithms for the detection of ventricular fibrillation from the surface ECG.

I Jekova1.   

Abstract

The introduction and widening application of automatic external defibrillators (AEDs) present very strong requirements for external ECG signal analysis. Highly accuratc discrimination between shockable and non-shockable rhythms is required, with sensitivity and specificity aimed to approach the maximum values of 100%. We undertook an assessment of the performance of five detection algorithms, selected from among several others for their good published results. Test signals were 71 8 s ECG episodes on sinus rhythm and 90 8 s episodes on ventricular fibrillation, which were taken from the well known ECG-signal databases of the American Heart Association (AHA) and the Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH-'cudb' and 'vfdb' files). The purpose of this study is to assess the accuracy of the algorithms with signals other than those used for their development. An expected reduction of the sensitivity and specificity was found. The results could be used for further assessment, e.g. of noise and artefact sensitivity, for comparison with newly developed algorithms, etc.

Entities:  

Mesh:

Year:  2000        PMID: 11110242     DOI: 10.1088/0967-3334/21/4/301

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


  9 in total

1.  Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators.

Authors:  Anton Amann; Robert Tratnig; Karl Unterkofler
Journal:  Biomed Eng Online       Date:  2005-10-27       Impact factor: 2.819

2.  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

3.  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

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.  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

8.  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

9.  Computational Model for Therapy Optimization of Wearable Cardioverter Defibrillator: Shockable Rhythm Detection and Optimal Electrotherapy.

Authors:  Oishee Mazumder; Rohan Banerjee; Dibyendu Roy; Ayan Mukherjee; Avik Ghose; Sundeep Khandelwal; Aniruddha Sinha
Journal:  Front Physiol       Date:  2021-12-10       Impact factor: 4.566

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.