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Automatic Detection of Atrial Fibrillation Using Electrocardiomatrix and Convolutional Neural Network

Salinas Martinez, Ricardo LU ; De Bie, Johannes ; Marzocchi, Nicoletta and Sandberg, Frida LU (2021) 2020 Computing in Cardiology, CinC 2020
Abstract
Long-term electrocardiogram (ECG) monitoring is a standard clinical routine in cryptogenic stroke survivors to assess the presence of atrial fibrillation (AF). However, manual evaluation of such recordings is time consuming, in particular when brief episodes are of interest. The electrocardiomatrix (ECM) technique allows compact, two-dimensional representation of the ECG and facilitates its review. In this study, we present a convolutional neural network (CNN) approach for automatic detection of AF based on ECM images. ECG segments of only 10 beats were converted into ECM images. A CNN was implemented to classify the ECMs between non-AF and AF. The CNN was trained using the MIT-BIH-NSR and the MIT-BIH-LTAF, and tested on the MIT-BIH-AF. A... (More)
Long-term electrocardiogram (ECG) monitoring is a standard clinical routine in cryptogenic stroke survivors to assess the presence of atrial fibrillation (AF). However, manual evaluation of such recordings is time consuming, in particular when brief episodes are of interest. The electrocardiomatrix (ECM) technique allows compact, two-dimensional representation of the ECG and facilitates its review. In this study, we present a convolutional neural network (CNN) approach for automatic detection of AF based on ECM images. ECG segments of only 10 beats were converted into ECM images. A CNN was implemented to classify the ECMs between non-AF and AF. The CNN was trained using the MIT-BIH-NSR and the MIT-BIH-LTAF, and tested on the MIT-BIH-AF. A total of 120088 non-AF and 108088 AF ECM images were classified with accuracy of 86.95%. This study suggests that a CNN allows automatic detection of AF episodes of only 10 beats when the ECG data is represented as an ECM image. (Less)
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author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2020 Computing in Cardiology, CinC 2020
pages
4 pages
publisher
IEEE Computer Society
conference name
2020 Computing in Cardiology, CinC 2020
conference location
Rimini, Italy
conference dates
2020-09-13 - 2020-09-16
external identifiers
  • scopus:85100939088
DOI
10.22489/CinC.2020.170
project
Ph.D. project: Paroxysmal atrial fibrillation: Continuous tracking of arrhythmia progression
language
English
LU publication?
yes
id
79df8bf7-132b-4c5c-8566-754ccb154350
date added to LUP
2021-03-04 17:59:10
date last changed
2023-01-01 04:42:50
@inproceedings{79df8bf7-132b-4c5c-8566-754ccb154350,
  abstract     = {{Long-term electrocardiogram (ECG) monitoring is a standard clinical routine in cryptogenic stroke survivors to assess the presence of atrial fibrillation (AF). However, manual evaluation of such recordings is time consuming, in particular when brief episodes are of interest. The electrocardiomatrix (ECM) technique allows compact, two-dimensional representation of the ECG and facilitates its review. In this study, we present a convolutional neural network (CNN) approach for automatic detection of AF based on ECM images. ECG segments of only 10 beats were converted into ECM images. A CNN was implemented to classify the ECMs between non-AF and AF. The CNN was trained using the MIT-BIH-NSR and the MIT-BIH-LTAF, and tested on the MIT-BIH-AF. A total of 120088 non-AF and 108088 AF ECM images were classified with accuracy of 86.95%. This study suggests that a CNN allows automatic detection of AF episodes of only 10 beats when the ECG data is represented as an ECM image.}},
  author       = {{Salinas Martinez, Ricardo and De Bie, Johannes and Marzocchi, Nicoletta and Sandberg, Frida}},
  booktitle    = {{2020 Computing in Cardiology, CinC 2020}},
  language     = {{eng}},
  month        = {{02}},
  publisher    = {{IEEE Computer Society}},
  title        = {{Automatic Detection of Atrial Fibrillation Using Electrocardiomatrix and Convolutional Neural Network}},
  url          = {{https://lup.lub.lu.se/search/files/94829138/CinC2020_170.pdf}},
  doi          = {{10.22489/CinC.2020.170}},
  year         = {{2021}},
}