Real-world validation of smartphone-based photoplethysmography for rate and rhythm monitoring in atrial fibrillation
Henri Gruwez, Daniel Ezzat, Tim Van Puyvelde, Sebastiaan Dhont, Evelyne Meekers, Liesbeth Bruckers, Femke Wouters, Michiel Kellens, Hugo Van Herendael, Maximo Rivero-Ayerza, Dieter Nuyens, Peter Haemers, Laurent Pison- Physiology (medical)
- Cardiology and Cardiovascular Medicine
Abstract
Aims
Photoplethysmography- (PPG) based smartphone applications facilitate heart rate and rhythm monitoring in patients with paroxysmal and persistent atrial fibrillation (AF). Despite an endorsement from the European Heart Rhythm Association, validation studies in this setting are lacking. Therefore, we evaluated the accuracy of PPG-derived heart rate and rhythm classification in subjects with an established diagnosis of AF in unsupervised real-world conditions.
Methods and results
Fifty consecutive patients were enrolled, 4 weeks before undergoing AF ablation. Patients used a handheld single-lead electrocardiography (ECG) device and a fingertip PPG smartphone application to record 3907 heart rhythm measurements twice daily during 8 weeks. The ECG was performed immediately before and after each PPG recording and was given a diagnosis by the majority of three blinded cardiologists. A consistent ECG diagnosis was exhibited along with PPG data of sufficient quality in 3407 measurements. A single measurement exhibited good quality more often with ECG (93.2%) compared to PPG (89.5%; P < 0.001). However, PPG signal quality improved to 96.6% with repeated measurements. Photoplethysmography-based detection of AF demonstrated excellent sensitivity [98.3%; confidence interval (CI): 96.7–99.9%], specificity (99.9%; CI: 99.8–100.0%), positive predictive value (99.6%; CI: 99.1–100.0%), and negative predictive value (99.6%; CI: 99.0–100.0%). Photoplethysmography underestimated the heart rate in AF with 6.6 b.p.m. (95% CI: 5.8 b.p.m. to 7.4 b.p.m.). Bland–Altman analysis revealed increased underestimation in high heart rates. The root mean square error was 11.8 b.p.m.
Conclusion
Smartphone applications using PPG can be used to monitor patients with AF in unsupervised real-world conditions. The accuracy of AF detection algorithms in this setting is excellent, but PPG-derived heart rate may tend to underestimate higher heart rates.