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

Recruiting now

Conditions studied: Atrial Fibrillation (AF), Heart Failure

In brief

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.

Key facts

Study ID

NCT07749183

Run by

Seerlinq s. r. o.

People needed

200

Starts

2025-10-01

Expected to finish

2026-11-01

Last updated by the study team

2026-08-06

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

• Adults ≥18 years with a diagnosis of heart failure (HFrEF, HFmrEF, or HFpEF)
• 12-lead ECG performed to confirm cardiac rhythm classification (AF vs. non-AF)

You may not qualify if…

• Missing a valid PPG recording

Where it is running

• Premedix — Bratislava, Slovakia (enrolling)

Full record on ClinicalTrials.gov

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