This is a prospective study validating a new machine-learning algorithm that detects atrial fibrillation (AF) from photoplethysmography (PPG) signals, developed for integration into the Seerlinq remote monitoring platform. This algorithm builds on the same core PPG signal-processing technology as Seerlinq's HeartCore device, a CE-certified (Class IIb, MDR) device that monitors left ventricular filling pressures in heart failure patients. The algorithm will be validated through internal cross-validation, external validation against an independent cohort with paired PPG-ECG recordings, and validation in a cohort of patients with paroxysmal atrial fibrillation and frequent sinus-AF transitions.
Inclusion Criteria:
Exclusion Criteria:
marta.kollarova@premedix.org
HF patients with a history of permanent/paroxysmal AF and AF documented on 12-lead ECG at enrollment
HF patients in sinus rhythm on the index 12-lead ECG with no prior documented AF episodes
Precision Detection and Prediction of Atrial Arrhythmias Using Artificial Intelligence and Consumer Wearable Devices
Portable Measurement Methods Combined With Artificial Intelligence in Detection of Atrial Fibrillation
REal-worLd vAlidaTION of PhotoPlehtysmoGraphy (for Atrial Fibrillation Detection)
Atrial Fibrillation Risk Estimation With Single-lead Handheld Electrocardiograms