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

31. juli 2026 oppdatert av: 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.

Studieoversikt

Status

Rekruttering

Forhold

Intervensjon / Behandling

Detaljert 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.

Studietype

Observasjonsmessig

Registrering (Antatt)

200

Kontakter og plasseringer

Denne delen inneholder kontaktinformasjon for de som utfører studien, og informasjon om hvor denne studien blir utført.

Studiekontakt

Studiesteder

  • Slovakia
      • Bratislava, Slovakia
        • Rekruttering
        • PreMedix
        • Ta kontakt med:
        • Hovedetterforsker:
          • Allan Bohm, M.D., MSc., PhD.

Deltakelseskriterier

Forskere ser etter personer som passer til en bestemt beskrivelse, kalt kvalifikasjonskriterier. Noen eksempler på disse kriteriene er en persons generelle helsetilstand eller tidligere behandlinger.

Kvalifikasjonskriterier

Alder som er kvalifisert for studier

  • Voksen
  • Eldre voksen

Tar imot friske frivillige

Nei

Prøvetakingsmetode

Ikke-sannsynlighetsprøve

Studiepopulasjon

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

Denne delen gir detaljer om studieplanen, inkludert hvordan studien er utformet og hva studien måler.

Hvordan er studiet utformet?

Designdetaljer

Kohorter og intervensjoner

Gruppe / Kohort
Intervensjon / Behandling
Documented AF
HF patients with a history of permanent/paroxysmal AF and AF documented on 12-lead ECG at enrollment
Annen: 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
Annen: 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.

Hva måler studien?

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
Tiltaksbeskrivelse
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)

Samarbeidspartnere og etterforskere

Det er her du vil finne personer og organisasjoner som er involvert i denne studien.

Sponsor

Seerlinq s. r. o.

Samarbeidspartnere

Premedix Academy
ACADEMY - občianske združenie

Studierekorddatoer

Disse datoene sporer fremdriften for innsending av studieposter og sammendragsresultater til ClinicalTrials.gov. Studieposter og rapporterte resultater gjennomgås av National Library of Medicine (NLM) for å sikre at de oppfyller spesifikke kvalitetskontrollstandarder før de legges ut på det offentlige nettstedet.

Studer hoveddatoer

Studiestart (Faktiske)

1. oktober 2025

Primær fullføring (Antatt)

1. august 2026

Studiet fullført (Antatt)

1. november 2026

Datoer for studieregistrering

Først innsendt

31. juli 2026

Først innsendt som oppfylte QC-kriteriene

31. juli 2026

Først lagt ut (Faktiske)

6. august 2026

Oppdateringer av studieposter

Sist oppdatering lagt ut (Faktiske)

6. august 2026

Siste oppdatering sendt inn som oppfylte QC-kriteriene

31. juli 2026

Sist bekreftet

1. juli 2026

Mer informasjon

Begreper knyttet til denne studien

Nøkkelord

Ytterligere relevante MeSH-vilkår

Andre studie-ID-numre

  • HeartCoreAF01

Plan for individuelle deltakerdata (IPD)

Planlegger du å dele individuelle deltakerdata (IPD)?

NEI

IPD-planbeskrivelse

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

Legemiddel- og utstyrsinformasjon, studiedokumenter

Studerer et amerikansk FDA-regulert medikamentprodukt

Nei

Studerer et amerikansk FDA-regulert enhetsprodukt

Nei

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