Can a Wrist-Worn sensor outsmart atrial fibrillation?

NCT ID NCT07749183

Recruiting now Diagnosis Sponsor: Seerlinq s. r. o. Source: ClinicalTrials.gov ↗

First seen Aug 06, 2026 · Last updated Aug 07, 2026 · Updated 1 time

Summary

This trial tests whether a machine-learning algorithm can detect atrial fibrillation (AF) early by analyzing pulse wave signals from a wearable sensor. The study involves 200 adults with heart failure, comparing the algorithm's readings against standard ECG results. If it works, this could allow continuous, non-invasive monitoring to catch AF sooner and help prevent complications.

What this could mean

Our plain-language read of the trial. This is informational only — not medical advice or a prediction.

Active substance: A machine-learning algorithm that analyzes photoplethysmography (PPG) signals to detect irregular heart rhythms associated with atrial fibrillation.
What this could lead to: If successful, this could enable early detection of atrial fibrillation during routine remote monitoring, potentially reducing stroke risk and improving heart failure management.
What could go wrong: The algorithm is still being validated; its accuracy may not hold up in real-world settings, and false alarms or missed detections could limit its usefulness.

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Conditions

The condition(s) this trial relates to.

atrial fibrillation heart failure

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

ATRIAL FIBRILLATION (AF) HEART FAILURE

Contacts and locations

Locations

Premedix
RECRUITING

Bratislava, Slovakia

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