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

31. juli 2026 opdateret af: Seerlinq s. r. o.

Prospective Validation of a Machine-Learning Algorithm Using Photoplethysmography Signals for Early Detection of Atrial Fibrillation During Remote Telemonitoring

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.

Studieoversigt

Status

Rekruttering

Betingelser

Intervention / Behandling

Detaljeret beskrivelse

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.

Undersøgelsestype

Observationel

Tilmelding (Anslået)

200

Kontakter og lokationer

Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.

Studiekontakt

Studiesteder

  • Slovakiet
      • Bratislava, Slovakiet
        • Rekruttering
        • PreMedix
        • Kontakt:
        • Ledende efterforsker:
          • Allan Bohm, M.D., MSc., PhD.

Deltagelseskriterier

Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.

Berettigelseskriterier

Aldre berettiget til at studere

  • Voksen
  • Ældre voksen

Tager imod sunde frivillige

Ingen

Prøveudtagningsmetode

Ikke-sandsynlighedsprøve

Studiebefolkning

Adults ≥18 years with a diagnosis of heart failure (HFrEF, HFmrEF, or HFpEF) from Slovakia

Beskrivelse

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

Studieplan

Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

Kohorter og interventioner

Gruppe / kohorte
Intervention / Behandling
Documented AF
HF patients with a history of permanent/paroxysmal AF and AF documented on 12-lead ECG at enrollment
Andet: 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.
Non-AF
HF patients in sinus rhythm on the index 12-lead ECG with no prior documented AF episodes
Andet: 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.

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Tidsramme
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
Tidsramme: Through study completion (estimated November 2026)
Through study completion (estimated November 2026)

Sekundære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Sensitivity and specificity of the algorithm at the Youden-optimal threshold
Tidsramme: Through study completion (estimated November 2026)
Through study completion (estimated November 2026)
Positive predictive value and negative predictive value
Tidsramme: Through study completion (estimated November 2026)
Through study completion (estimated November 2026)
Average precision
Tidsramme: Through study completion (estimated November 2026)
area under the precision-recall curve
Through study completion (estimated November 2026)
Model calibration
Tidsramme: Through study completion (estimated November 2026)
e.g., calibration curve / Brier score
Through study completion (estimated November 2026)
Matthews correlation coefficient
Tidsramme: Through study completion (estimated November 2026)
Through study completion (estimated November 2026)
Overall classification accuracy
Tidsramme: Through study completion (estimated November 2026)
Through study completion (estimated November 2026)
Specificity and false-positive rate in the subgroup with frequent atrial/ventricular extrasystoles
Tidsramme: Through study completion (estimated November 2026)
Through study completion (estimated November 2026)
Accuracy of AF detection during sinus-AF transitions at the individual patient level
Tidsramme: Through study completion (estimated November 2026)
Through study completion (estimated November 2026)

Samarbejdspartnere og efterforskere

Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.

Sponsor

Seerlinq s. r. o.

Samarbejdspartnere

Premedix Academy
ACADEMY - občianske združenie

Datoer for undersøgelser

Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.

Studer store datoer

Studiestart (Faktiske)

1. oktober 2025

Primær færdiggørelse (Anslået)

1. august 2026

Studieafslutning (Anslået)

1. november 2026

Datoer for studieregistrering

Først indsendt

31. juli 2026

Først indsendt, der opfyldte QC-kriterier

31. juli 2026

Først opslået (Faktiske)

6. august 2026

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

6. august 2026

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

31. juli 2026

Sidst verificeret

1. juli 2026

Mere information

Begreber relateret til denne undersøgelse

Nøgleord

Yderligere relevante MeSH-vilkår

Andre undersøgelses-id-numre

  • HeartCoreAF01

Plan for individuelle deltagerdata (IPD)

Planlægger du at dele individuelle deltagerdata (IPD)?

INGEN

IPD-planbeskrivelse

The data will not be shared publicly, but anonymized data can be shared upon reasonable request to the corresponding author.

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