False Alarm Reduction in Atrial Fibrillation Screening

Hesam Halvaei, Emma Svennberg, Leif Sörnmo, Martin Stridh

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

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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.
Original languageEnglish
Title of host publication2020 Computing in Cardiology
Place of PublicationRimini, Italy
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)978-1-7281-7382-5
ISBN (Print)978-1-7281-1105-6
DOIs
Publication statusPublished - 2021 Feb 10
Event2020 Computing in Cardiology, CinC 2020 - Rimini, Italy
Duration: 2020 Sept 132020 Sept 16

Conference

Conference2020 Computing in Cardiology, CinC 2020
Country/TerritoryItaly
CityRimini
Period2020/09/132020/09/16

Subject classification (UKÄ)

  • Cardiac and Cardiovascular Systems

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