Literature DB >> 25666902

Low-complexity detection of atrial fibrillation in continuous long-term monitoring.

Andrius Petrėnas1, Vaidotas Marozas2, Leif Sörnmo3.   

Abstract

This study describes an atrial fibrillation (AF) detector whose structure is well-adapted both for detection of subclinical AF episodes and for implementation in a battery-powered device for use in continuous long-term monitoring applications. A key aspect for achieving these two properties is the use of an 8-beat sliding window, which thus is much shorter than the 128-beat window used in most existing AF detectors. The building blocks of the proposed detector include ectopic beat filtering, bigeminal suppression, characterization of RR interval irregularity, and signal fusion. With one design parameter, the performance can be tuned to put more emphasis on avoiding false alarms due to non-AF arrhythmias or more emphasis on detecting brief AF episodes. Despite its very simple structure, the results show that the detector performs better on the MIT-BIH Atrial Fibrillation database than do existing detectors, with high sensitivity and specificity (97.1% and 98.3%, respectively). The detector can be implemented with just a few arithmetical operations and does not require a large memory buffer thanks to the short window.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Atrial fibrillation; Brief episodes; Continuous ambulatory monitoring; Low-complexity detection; RR-based detection

Mesh:

Year:  2015        PMID: 25666902     DOI: 10.1016/j.compbiomed.2015.01.019

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  13 in total

1.  Automatic, electrocardiographic-based detection of autonomic arousals and their association with cortical arousals, leg movements, and respiratory events in sleep.

Authors:  Mads Olsen; Logan Douglas Schneider; Joseph Cheung; Paul E Peppard; Poul J Jennum; Emmanuel Mignot; Helge Bjarup Dissing Sorensen
Journal:  Sleep       Date:  2018-03-01       Impact factor: 5.849

2.  High Specificity Wearable Device With Photoplethysmography and Six-Lead Electrocardiography for Atrial Fibrillation Detection Challenged by Frequent Premature Contractions: DoubleCheck-AF.

Authors:  Justinas Bacevicius; Zygimantas Abramikas; Ernestas Dvinelis; Deimile Audzijoniene; Marija Petrylaite; Julija Marinskiene; Justina Staigyte; Albinas Karuzas; Vytautas Juknevicius; Rusne Jakaite; Viktorija Basyte-Bacevice; Neringa Bileisiene; Andrius Solosenko; Daivaras Sokas; Andrius Petrenas; Monika Butkuviene; Birute Paliakaite; Saulius Daukantas; Andrius Rapalis; Germanas Marinskis; Eugenijus Jasiunas; Angeliki Darma; Vaidotas Marozas; Audrius Aidietis
Journal:  Front Cardiovasc Med       Date:  2022-04-06

3.  HAN-ECG: An interpretable atrial fibrillation detection model using hierarchical attention networks.

Authors:  Sajad Mousavi; Fatemeh Afghah; U Rajendra Acharya
Journal:  Comput Biol Med       Date:  2020-10-15       Impact factor: 4.589

4.  ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network.

Authors:  Zhaohan Xiong; Martyn P Nash; Elizabeth Cheng; Vadim V Fedorov; Martin K Stiles; Jichao Zhao
Journal:  Physiol Meas       Date:  2018-09-24       Impact factor: 2.833

5.  A Real-Time Atrial Fibrillation Detection Algorithm Based on the Instantaneous State of Heart Rate.

Authors:  Xiaolin Zhou; Hongxia Ding; Wanqing Wu; Yuanting Zhang
Journal:  PLoS One       Date:  2015-09-16       Impact factor: 3.240

Review 6.  A Review of Atrial Fibrillation Detection Methods as a Service.

Authors:  Oliver Faust; Edward J Ciaccio; U Rajendra Acharya
Journal:  Int J Environ Res Public Health       Date:  2020-04-29       Impact factor: 3.390

7.  Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine.

Authors:  Robert Czabanski; Krzysztof Horoba; Janusz Wrobel; Adam Matonia; Radek Martinek; Tomasz Kupka; Michal Jezewski; Radana Kahankova; Janusz Jezewski; Jacek M Leski
Journal:  Sensors (Basel)       Date:  2020-01-30       Impact factor: 3.576

8.  A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices.

Authors:  Álvaro Huerta Herraiz; Arturo Martínez-Rodrigo; Vicente Bertomeu-González; Aurelio Quesada; José J Rieta; Raúl Alcaraz
Journal:  Entropy (Basel)       Date:  2020-07-01       Impact factor: 2.524

9.  Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention.

Authors:  Ningrong Lei; Murtadha Kareem; Seung Ki Moon; Edward J Ciaccio; U Rajendra Acharya; Oliver Faust
Journal:  Int J Environ Res Public Health       Date:  2021-01-19       Impact factor: 3.390

10.  Identification of Transient Noise to Reduce False Detections in Screening for Atrial Fibrillation.

Authors:  Hesam Halvaei; Emma Svennberg; Leif Sörnmo; Martin Stridh
Journal:  Front Physiol       Date:  2021-06-04       Impact factor: 4.566

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