Non-Contact Atrial Fibrillation Detection From Face Videos by Learning Systolic Peaks
Objective: We propose a non-contact approach for atrial fibrillation (AF) detection from face videos. nn
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. nn
Conclusion: We achieve good performance of non-contact AF detection by learning systolic peaks. nn
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.