High-Throughput, Contact-Free Detection of Atrial Fibrillation From Video With Deep Learning

Abstract

-Throughput, Contact-Free Detection of Atrial Fibrillation From Video With Deep Learning Approaches for atrial fibrillation (AF) detection can screen only 1 patient at a time. 1 In 2018, 2 we demonstrated a novel method of AF detection by analyzing facial photoplethysmographic (FPPG) signals without physical contact using a smartphone camera. 2 In this proof-ofconcept study, we prospectively evaluated the feasibility of high-throughput AF detection by analyzing FPPG signals 3 from multiple patients concurrently using a single digital cam-era and a pretrained deep convolutional neural network (DCNN). 4 Methods | After institutional approval from the Joint Chinese University of Hong Kong-New Territories East Cluster Clinical Research Ethics Committee and individual written informed consent, 20 patients (mean [SD] age, 76.6 [7.6] years; 12 men [60%]) with permanent AF and 24 control individuals (mean [SD] age, 56.8 [20.2] years; 14 men [58.3%]) in sinus rhythm (SR) were recruited. A digital camera (50D; Canon) was used to film 5 patients sitting in a row 150 cm away (Figure). We recorded 64 videos (1-minute duration, 24 FPS), each capturing 5 patients simultaneously in 32 different heart-rhythm A, Example video recording of 5 patients arranged in 1 of the 32 different heart rhythm permutations with extracted facial photoplethysmographic (FPPG) signals and reference electrocardiogram (ECG) traces. B, The 5-participant binary (atrial fibrillation [AF]/sinus rhythm [SR]) matrix.