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Yan BP, Lai WHS, Chan CKY, et al. High-Throughput, Contact-Free Detection of Atrial Fibrillation From Video With Deep Learning. JAMA Cardiol. 2020;5(1):105–107. doi:10.1001/jamacardio.2019.4004
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-of-concept study, we prospectively evaluated the feasibility of high-throughput AF detection by analyzing FPPG signals3 from multiple patients concurrently using a single digital camera and a pretrained deep convolutional neural network (DCNN).4
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 permutations based on a 5-participant binary (AF/SR) matrix and repeated once using patients chosen at random. Patients were instructed to keep their head stationary and not talk. The FPPG signals from patients were automatically extracted from the videos, resampled to 30 Hz, and analyzed in segments of 512 samples using Cardiio Deep Rhythm (Cardiio Inc), a DCNN previously trained for detecting AF from PPG waveforms.4 Pulse irregularity in more than 50% FPPG segments for each patient was considered positive for AF. The investigator applying DCNN was blinded to the reference electrocardiogram (ECG) and participant binary matrix.
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