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False Alarm Reduction in Atrial Fibrillation Screening

Halvaei, Hesam LU ; Svennberg, Emma ; Sörnmo, Leif LU and Stridh, Martin LU (2021) 2020 Computing in Cardiology, CinC 2020
Abstract
Early detection of AF is essential and emphasizes the significance of AF screening. However, AF detection in screening ECGs, usually recorded by handheld and portable devices, is limited because of their high susceptibility to noise. In this study, the feasibility of applying a machine learning-based quality control stage, inserted between the QRS detector and AF detector blocks, is investigated with the aim to improve AF detection. A convolutional neural network was trained to classify the detections into either true or false. False detections were excluded and an updated series of QRS complexes was fed to the AF detector. The results show that the convolutional neural network-based quality control reduces the number of false alarms by... (More)
Early detection of AF is essential and emphasizes the significance of AF screening. However, AF detection in screening ECGs, usually recorded by handheld and portable devices, is limited because of their high susceptibility to noise. In this study, the feasibility of applying a machine learning-based quality control stage, inserted between the QRS detector and AF detector blocks, is investigated with the aim to improve AF detection. A convolutional neural network was trained to classify the detections into either true or false. False detections were excluded and an updated series of QRS complexes was fed to the AF detector. The results show that the convolutional neural network-based quality control reduces the number of false alarms by 24.8% at the cost of 1.9% decrease in sensitivity compared to AF detection without any quality control. (Less)
Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2020 Computing in Cardiology
pages
4 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2020 Computing in Cardiology, CinC 2020
conference location
Rimini, Italy
conference dates
2020-09-13 - 2020-09-16
external identifiers
  • scopus:85100948806
ISBN
978-1-7281-1105-6
978-1-7281-7382-5
DOI
10.22489/CinC.2020.255
language
English
LU publication?
yes
id
d4d03ecc-9c85-43e4-b8b9-d3f078f85ec4
date added to LUP
2021-03-04 14:36:05
date last changed
2025-01-11 05:54:58
@inproceedings{d4d03ecc-9c85-43e4-b8b9-d3f078f85ec4,
  abstract     = {{Early detection of AF is essential and emphasizes the significance of AF screening. However, AF detection in screening ECGs, usually recorded by handheld and portable devices, is limited because of their high susceptibility to noise. In this study, the feasibility of applying a machine learning-based quality control stage, inserted between the QRS detector and AF detector blocks, is investigated with the aim to improve AF detection. A convolutional neural network was trained to classify the detections into either true or false. False detections were excluded and an updated series of QRS complexes was fed to the AF detector. The results show that the convolutional neural network-based quality control reduces the number of false alarms by 24.8% at the cost of 1.9% decrease in sensitivity compared to AF detection without any quality control.}},
  author       = {{Halvaei, Hesam and Svennberg, Emma and Sörnmo, Leif and Stridh, Martin}},
  booktitle    = {{2020 Computing in Cardiology}},
  isbn         = {{978-1-7281-1105-6}},
  language     = {{eng}},
  month        = {{02}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{False Alarm Reduction in Atrial Fibrillation Screening}},
  url          = {{https://lup.lub.lu.se/search/files/95378290/42Q3OV_CinC2020_255.pdf}},
  doi          = {{10.22489/CinC.2020.255}},
  year         = {{2021}},
}