Literature DB >> 19525573

Bench study of the accuracy of a commercial AED arrhythmia analysis algorithm in the presence of electromagnetic interferences.

Irena Jekova1, Vessela Krasteva, Sarah Ménétré, Todor Stoyanov, Ivaylo Christov, Roman Fleischhackl, Johann-Jakob Schmid, Jean-Philippe Didon.   

Abstract

This paper presents a bench study on a commercial automated external defibrillator (AED). The objective was to evaluate the performance of the defibrillation advisory system and its robustness against electromagnetic interferences (EMI) with central frequencies of 16.7, 50 and 60 Hz. The shock advisory system uses two 50 and 60 Hz band-pass filters, an adaptive filter to identify and suppress 16.7 Hz interference, and a software technique for arrhythmia analysis based on morphology and frequency ECG parameters. The testing process includes noise-free ECG strips from the internationally recognized MIT-VFDB ECG database that were superimposed with simulated EMI artifacts and supplied to the shock advisory system embedded in a real AED. Measurements under special consideration of the allowed variation of EMI frequency (15.7-17.4, 47-52, 58-62 Hz) and amplitude (1 and 8 mV) were performed to optimize external validity. The accuracy was reported using the American Heart Association (AHA) recommendations for arrhythmia analysis performance. In the case of artifact-free signals, the AHA performance goals were exceeded for both sensitivity and specificity: 99% for ventricular fibrillation (VF), 98% for rapid ventricular tachycardia (VT), 90% for slow VT, 100% for normal sinus rhythm, 100% for asystole and 99% for other non-shockable rhythms. In the presence of EMI, the specificity for some non-shockable rhythms (NSR, N) may be affected in some specific cases of a low signal-to-noise ratio and extreme frequencies, leading to a drop in the specificity with no more than 7% point. The specificity for asystole and the sensitivity for VF and rapid VT in the presence of any kind of 16.7, 50 or 60 Hz EMI simulated artifact were shown to reach the equivalence of sensitivity required for non-noisy signals. In conclusion, we proved that the shock advisory system working in a real AED operates accurately according to the AHA recommendations without artifacts and in the presence of EMI. The results may be affected for specificity in the case of a low signal-to-noise ratio or in some extreme frequency setting.

Entities:  

Mesh:

Year:  2009        PMID: 19525573     DOI: 10.1088/0967-3334/30/7/012

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


  5 in total

1.  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

2.  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

3.  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

Review 4.  Role of artificial intelligence in defibrillators: a narrative review.

Authors:  Grace Brown; Samuel Conway; Mahmood Ahmad; Divine Adegbie; Nishil Patel; Vidushi Myneni; Mohammad Alradhawi; Niraj Kumar; Daniel R Obaid; Dominic Pimenta; Jonathan J H Bray
Journal:  Open Heart       Date:  2022-07

5.  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

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.