A Photoplethysmography-Based Machine Learning Algorithm for Early Atrial Fibrillation Detection: A Prospective Validation Study

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.

NCT Number	NCT07749183
Lead Sponsor	Seerlinq s. r. o. Collaborators: ACADEMY - občianske združenie, Premedix Academy
Conditions	Atrial Fibrillation (AF), Heart Failure
Enrollment	200 participants
Start Date	2025-10-01
Primary Completion	2026-08 (estimated)
Study Completion	2026-11 (estimated)
Updated on ClinicalTrials.gov	2026-08-06