Non-contact atrial fibrillation detection from face videos by learning systolic peaks
Sun, Zhaodong; Junttila, Juhani; Tulppo, Mikko; Seppänen, Tapio; Li, Xiaobai (2022-07-22)
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, vol. 26, no. 9, pp. 4587-4598, Sept. 2022, doi: 10.1109/JBHI.2022.3193117
© 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/
https://urn.fi/URN:NBN:fi-fe2022082456049
Tiivistelmä
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.
Kokoelmat
- Avoin saatavuus [32007]