|

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

RECRUITINGSponsored by Seerlinq s. r. o.
Actively Recruiting
SponsorSeerlinq s. r. o.
Started2025-10-01
Est. completion2026-08
Eligibility
Age18 Years+
Healthy vol.Accepted
View on ClinicalTrials.gov →

NCT07749183

Summary

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.

Eligibility

Age: 18 Years+Healthy volunteers accepted
Inclusion Criteria:

* 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)

Exclusion Criteria:

* Missing a valid PPG recording

Conditions3

Atrial Fibrillation (AF)Heart DiseaseHeart Failure

Browse More Trials

Heart Disease trials

Trial data from ClinicalTrials.gov. Trial status and eligibility can change — verify directly with the study contact or on ClinicalTrials.gov.

This site does not provide medical advice. Always consult your doctor before considering enrollment in a clinical trial. Learn more on our About page.