University of Oulu

J. Shi, I. Alikhani, X. Li, Z. Yu, T. Seppänen and G. Zhao, "Atrial Fibrillation Detection From Face Videos by Fusing Subtle Variations," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 8, pp. 2781-2795, Aug. 2020, doi: 10.1109/TCSVT.2019.2926632

Atrial fibrillation detection from face videos by fusing subtle variations

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Author: Shi, Jingang1; Alikhani, Iman1; Li, Xiaobai1;
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu, FI-90014 Oulu, Finland
2School of Information and Technology, Northwest University, Xi’an 710069, China
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 3.1 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2019-07-03


Atrial fibrillation (AF) is one of the most common cardiac arrhythmias, which particularly occurs in the elderly individuals with heart disease. Though AF is often asymptomatic during normal activities, it has huge potential risks for stroke and other severe diseases. Thus, early detection of AF has great importance in the field of public health. Currently, electrocardiography (ECG) is the commonly used measure for the diagnosis of AF, which presents the irregular rhythm of waveform for AF patients. However, the measurement of the ECG signal requires special medical acquisition devices, which are not comfortable for practical monitoring in daily life. In this paper, we explore a very promising algorithm to detect AF from remote face videos by analyzing the color variations of face skin. The main challenge is that the current remote photoplethysmography (rPPG) technique is rather immature, which causes difficulty in extracting accurate pulse signals for describing the cardiac rhythm. To solve this problem, we first utilize various rPPG algorithms to capture pulse rhythms from different regions on the face video. We then investigate biomedical statistical methods to extract suitable features from each pulse signal. Due to the imprecision of video-extracted pulse signals, some traditional physiological features may lose their utility since they were originally proposed for ECG signals. Furthermore, some of them are very susceptible to the influence of noise. Thus, we propose a feature fusion algorithm to select and combine reasonable information from multiple physiological features, which aims to preserve the discriminability of detecting AF in the presence of the noise and outlier disturbances. The experimental results on a real-world database demonstrate the effectiveness of the proposed method in providing useful information for AF detection.

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Series: IEEE transactions on circuits and systems for video technology
ISSN: 1051-8215
ISSN-E: 1558-2205
ISSN-L: 1051-8215
Volume: 30
Issue: 8
Pages: 2781 - 2795
DOI: 10.1109/TCSVT.2019.2926632
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This work was supported by the National Natural Science Foundation of China (No. 61772419), Academy of Finland, Tekes Fidipro Program (No. 1849/31/2015), Business Finland Project (No. 3116/31/2017), Academy of Finland 6Genesis Flagship (No. 318927), Tekniikan Edistämissäätiö and Infotech Oulu.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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