A Photoplethysmography-Based Machine Learning Algorithm for Early Atrial Fibrillation Detection: A Prospective Validation Study
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: PPG-based AF detection algorithm.
- Who it may be relevant to
- Registry conditions: Atrial Fibrillation (AF), Heart Failure. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Slovakia
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Prospective Validation of a Machine-Learning Algorithm Using Photoplethysmography Signals for Early Detection of Atrial Fibrillation During Remote Telemonitoring
Overview
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.
Detailed description
Atrial fibrillation (AF) and heart failure (HF) frequently coexist and share a bidirectional causal relationship; their concurrence is associated with worse clinical outcomes. Early detection of AF may enable timely intervention and improve outcomes. This study is prospectively validating a machine-learning algorithm for AF detection from PPG signals, intended 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 device under the EU MDR that monitors left ventricular filling pressures in heart failure patients). It is a stand-alone algorithm designed specifically to detect clinically relevant (≥ 30s) atrial fibrillation.
Validation of the algorithm will proceed in three stages: (1) internal cross-validation; (2) external validation against an independent cohort with paired PPG-ECG recordings, to confirm generalizability; and (3) validation in a cohort of patients with paroxysmal atrial fibrillation and frequent sinus-AF transitions, to assess performance during clinically challenging rhythm changes.
The study is enrolling toward an estimated 1,000 unique PPG recordings. A 12-lead ECG is used to confirm cardiac rhythm classification (gold standard) as the reference for evaluating algorithm performance.
Interventions
- Other 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.
Primary outcome measures
- Diagnostic accuracy (area under the ROC curve) of the PPG-based machine-learning algorithm for detecting clinically relevant AF (≥ 30s), compared with gold-standard 12-lead ECG [Time frame: Through study completion (estimated November 2026)]
Secondary outcome measures (8)
- Sensitivity and specificity of the algorithm at the Youden-optimal threshold [Time frame: Through study completion (estimated November 2026)]
- Positive predictive value and negative predictive value [Time frame: Through study completion (estimated November 2026)]
- Average precision [Time frame: Through study completion (estimated November 2026)]
- Model calibration [Time frame: Through study completion (estimated November 2026)]
- Matthews correlation coefficient [Time frame: Through study completion (estimated November 2026)]
- Overall classification accuracy [Time frame: Through study completion (estimated November 2026)]
- Specificity and false-positive rate in the subgroup with frequent atrial/ventricular extrasystoles [Time frame: Through study completion (estimated November 2026)]
- Accuracy of AF detection during sinus-AF transitions at the individual patient level [Time frame: Through study completion (estimated November 2026)]
Eligibility criteria
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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
Slovakia · 1 center
- Premedix — Bratislava
Identifiers
NCT: NCT07749183 · HeartCoreAF01