Clinical Research Directory
Browse clinical research sites, groups, and studies.
A Photoplethysmography-Based Machine Learning Algorithm for Early Atrial Fibrillation Detection: A Prospective Validation Study
Sponsor: Seerlinq s. r. o.
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.
Official title: Prospective Validation of a Machine-Learning Algorithm Using Photoplethysmography Signals for Early Detection of Atrial Fibrillation During Remote Telemonitoring
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
200
Start Date
2025-10-01
Completion Date
2026-11
Last Updated
2026-08-06
Healthy Volunteers
No
Conditions
Interventions
PPG-based AF detection algorithm
The PPG-based atrial fibrillation detection algorithm is a non-invasive signal processing approach that analyzes photoplethysmographic waveforms obtained during remote monitoring. The algorithm evaluates pulse-to-pulse variability, waveform characteristics, and signal quality parameters to identify irregular rhythm patterns associated with atrial fibrillation and provide early detection of potential arrhythmic events.
Locations (1)
Premedix
Bratislava, Slovakia