title: Monitoring and detecting atrial fibrillation using wearable technology.
abstract: Atrial fibrillation (AFib) is diagnosed by analysis of the morphological and rhythmic properties of the electrocardiogram. It was recently shown that accurate detection of AFib is possible using beat-to-beat interval variations. This raises the question of whether AFib detection can be performed using a pulsatile waveform such as the Photoplethysmogram (PPG). The recent explosion in use of recreational and professional ambulatory wrist-based pulse monitoring devices means that an accurate pulse-based AFib screening algorithm would enable large scale screening for silent or undiagnosed AFib, a significant risk factor for multiple diseases. We propose a noise-resistant machine learning approach to detecting AFib from noisy ambulatory PPG recorded from the wrist using a modern research watch-based wearable device (the Samsung Simband). Ambulatory pulsatile and movement data were recorded from 46 subjects, 15 with AFib and 31 non symptomatic. Single channel electrocardiogram (ECG), multi-wavelength PPG and tri-axial accelerometry were recorded simultaneously at 128 Hz from the non-dominant wrist using the Simband. Recording lengths varied from 3.5 to 8.5 minutes. Pulse (beat) detection was performed on the PPG waveforms, and eleven features were extracted based on beat-to-beat variability and waveform signal quality. Using 10-fold cross validation, an accuracy of 95 % on out-of-sample data was achieved, with a sensitivity of 97%, specificity of 94%, and an area under the receiver operating curve (AUROC) of 0.99. The described approach provides a noise-resistant, accurate screening tool for AFib from PPG sensors located in an ambulatory wrist watch. To our knowledge this is the first study to demonstrate an algorithm with a high enough accuracy to be used in general population studies that does not require an ambulatory Holter electrocardiographic monitor.
authors.forename: Shamim
authors.initials: S
authors.lastname: Nemati
authors.forename: Mohammad M
authors.initials: MM
authors.lastname: Ghassemi
authors.forename: Vaidehi
authors.initials: V
authors.lastname: Ambai
authors.forename: Nino
authors.initials: N
authors.lastname: Isakadze
authors.forename: Oleksiy
authors.initials: O
authors.lastname: Levantsevych
authors.forename: Amit
authors.initials: A
authors.lastname: Shah
authors.forename: Gari D
authors.initials: GD
authors.lastname: Clifford
mesh_terms: D061725:Accelerometry / Q000379:methods; D000465:Algorithms; D001281:Atrial Fibrillation / Q000175:diagnosis* / Q000503:physiopathology; D004562:Electrocardiography / Q000379:methods; D006339:Heart Rate; D006801:Humans; D018670:Monitoring, Ambulatory / Q000295:instrumentation; D017156:Photoplethysmography / Q000295:instrumentation / Q000379:methods*; D011674:Pulse; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012815:Signal Processing, Computer-Assisted