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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, 3.6 MB) |
| Persistent link: | http://urn.fi/urn:nbn:fi-fe2022082456049 |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers,
2022
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| Publish Date: | 2022-08-24 |
| Description: |
AbstractObjective: 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. see all
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| Series: |
IEEE journal of biomedical and health informatics |
| ISSN: | 2168-2194 |
| ISSN-E: | 2168-2208 |
| ISSN-L: | 2168-2194 |
| Volume: | 26 |
| Issue: | 9 |
| Pages: | 4587 - 4598 |
| 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/. |
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https://creativecommons.org/licenses/by/4.0/ |