Contactless facial video recording with deep learning models for the detection of atrial fibrillation.

Journal: Scientific reports
PMID: 34996908

Abstract

Atrial fibrillation (AF) is often asymptomatic and paroxysmal. Screening and monitoring are needed especially for people at high risk. This study sought to use camera-based remote photoplethysmography (rPPG) with a deep convolutional neural network (DCNN) learning model for AF detection. All participants were classified into groups of AF, normal sinus rhythm (NSR) and other abnormality based on 12-lead ECG. They then underwent facial video recording for 10 min with rPPG signals extracted and segmented into 30-s clips as inputs of the training of DCNN models. Using voting algorithm, the participant would be predicted as AF if > 50% of their rPPG segments were determined as AF rhythm by the model. Of the 453 participants (mean age, 69.3 ± 13.0 years, women, 46%), a total of 7320 segments (1969 AF, 1604 NSR & 3747others) were analyzed by DCNN models. The accuracy rate of rPPG with deep learning model for discriminating AF from NSR and other abnormalities was 90.0% and 97.1% in 30-s and 10-min recording, respectively. This contactless, camera-based rPPG technique with a deep-learning model achieved significantly high accuracy to discriminate AF from non-AF and may enable a feasible way for a large-scale screening or monitoring in the future.

Authors

  • Yu Sun
    Department of Neurology, China-Japan Friendship Hospital, Beijing, China.
  • Yin-Yin Yang
    Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu, 30010, Taiwan, ROC.
  • Bing-Jhang Wu
    Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu, 30010, Taiwan, ROC.
  • Po-Wei Huang
    Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu, 30010, Taiwan, ROC.
  • Shao-En Cheng
    Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu, 30010, Taiwan, ROC.
  • Bing-Fei Wu
    Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu, 30010, Taiwan, ROC. bwu@cssp.cn.nctu.edu.tw.
  • Chun-Chang Chen
    Department of Cardiology, New Taipei City Hospital, New Taipei City, Taiwan, ROC.

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