University of Oulu

Z. Sun, J. Junttila, M. Tulppo, T. Seppänen and X. Li, "Non-Contact Atrial Fibrillation Detection From Face Videos by Learning Systolic Peaks," in IEEE Journal of Biomedical and Health Informatics, 2022, doi: 10.1109/JBHI.2022.3193117

Non-contact atrial fibrillation detection from face videos by learning systolic peaks

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Author: Sun, Zhaodong1; Junttila, Juhani2; Tulppo, Mikko2;
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu, FI-90014 Oulu, Finland
2Research Unit of Internal Medicine, Medical Research Center Oulu, FI-90014 Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.7 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2022082456049
Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-08-24
Description:

Abstract

Objective: We propose a non-contact approach for atrial fibrillation (AF) detection from face videos.

Methods: Our proposed method can accurately extract systolic peaks from face videos for AF detection. The proposed method is trained with subject-independent 10-fold cross-validation with 30s video clips and tested on two tasks. 1) Classification of healthy versus AF: the accuracy, sensitivity, and specificity are 96.00%, 95.36%, and 96.12%. 2) Classification of SR versus AF: the accuracy, sensitivity, and specificity are 95.23%, 98.53%, and 91.12%. In addition, we also demonstrate the feasibility of non-contact AFL detection.

Conclusion: We achieve good performance of non-contact AF detection by learning systolic peaks.

Significance: non-contact AF detection can be used for self-screening of AF symptoms for suspectable populations at home or self-monitoring of AF recurrence after treatment for chronic patients.

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Series: IEEE journal of biomedical and health informatics
ISSN: 2168-2194
ISSN-E: 2168-2208
ISSN-L: 2168-2194
Issue: Online first
DOI: 10.1109/jbhi.2022.3193117
OADOI: https://oadoi.org/10.1109/jbhi.2022.3193117
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Subjects:
Funding: This study was supported by the Academy of Finland (project 323287 and 5326291), the Finnish Work Environment Fund (Project 200414), Sigrid Juselius Foundation and Foundation for Cardiovascular Research.
Academy of Finland Grant Number: 323287
Detailed Information: 323287 (Academy of Finland Funding decision)
Copyright information: © The Author(s) 2022. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
  https://creativecommons.org/licenses/by/4.0/