Atrial Fibrillation (AF)HeartCore AFNCT07749183
A Photoplethysmography-Based Machine Learning Algorithm for Early Atrial Fibrillation Detection: A Prospective Validation Study
An observational study of Atrial Fibrillation (AF) and Heart Failure, run by Seerlinq s. r. o. at 1 site in Slovakia.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Atrial Fibrillation (AF), Heart Failure
- What is being tested?
- PPG-based AF detection algorithm
- How many people
- 200 participants
- Where
- 1 siteSlovakia
- Study dates
- 1 Oct 2025to Nov 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Adults ≥18 years with a diagnosis of heart failure (HFrEF, HFmrEF, or HFpEF) from Slovakia
Who may be able to join
- 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)
Who cannot join
- Missing a valid PPG recording
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Slovakia1 site, 1 recruiting
- Recruiting
Premedix
Bratislava
Allan Bohm, M.D., MSc. PhD., +421 907 411 499, allan.bohm@premedix.org
Allan Bohm, M.D., MSc., PhD.
About this study
Official title: 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.
Read the 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.
- Conditions
- Atrial Fibrillation (AF), Heart Failure
- Being tested
- PPG-based AF detection algorithm
- Keywords
- Photoplethysmography, Machine learning, Remote telemonitoring, Wearable device, Digital biomarker, Arrhythmia detection
- Medical subject headings
- Atrial Fibrillation, Heart Failure
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
PPG-based AF detection algorithmOther
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.
| Study group | Role | What they receive |
|---|---|---|
| Documented AF | HF patients with a history of permanent/paroxysmal AF and AF documented on 12-lead ECG at enrollment Receives: Other: PPG-based AF detection algorithm | |
| Non-AF | HF patients in sinus rhythm on the index 12-lead ECG with no prior documented AF episodes Receives: Other: PPG-based AF detection algorithm |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: Through study completion (estimated November 2026)
Secondary outcomes (8)
Sensitivity and specificity of the algorithm at the Youden-optimal threshold
Measured: Through study completion (estimated November 2026)
Positive predictive value and negative predictive value
Measured: Through study completion (estimated November 2026)
Average precision
area under the precision-recall curve
Measured: Through study completion (estimated November 2026)
Model calibration
e.g., calibration curve / Brier score
Measured: Through study completion (estimated November 2026)
Matthews correlation coefficient
Measured: Through study completion (estimated November 2026)
Overall classification accuracy
Measured: Through study completion (estimated November 2026)
Specificity and false-positive rate in the subgroup with frequent atrial/ventricular extrasystoles
Measured: Through study completion (estimated November 2026)
Accuracy of AF detection during sinus-AF transitions at the individual patient level
Measured: Through study completion (estimated November 2026)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Aug 2026
- Study start1 Oct 2025
- Primary completionAug 2026 (estimated)
- Study completionNov 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Atrial Fibrillation (AF)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Seerlinq s. r. o.
- Sponsor type
- University, hospital or other
- Collaborators
- ACADEMY - občianske združenie, Premedix Academy
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07749183
- Sponsor's protocol ID
- HeartCoreAF01
- Acronym
- HeartCore AF
- Status last verified
- Jul 2026
- First submitted
- 31 Jul 2026
- Last updated
- 6 Aug 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: The data will not be shared publicly, but anonymized data can be shared upon reasonable request to the corresponding author.
Study at a glance
From the registry record, last updated by the sponsor on 6 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prospective Validation of a Machine-Learning Algorithm Using Photoplethysmography Signals for Early Detection of Atrial Fibrillation During Remote Telemonitoring
- ClinicalTrials.gov ID
- NCT07749183
- Acronym / sponsor ID
- HeartCore AF / HeartCoreAF01
- Conditions
- Atrial Fibrillation (AF), Heart Failure
- Keywords
- Photoplethysmography, Machine learning, Remote telemonitoring, Wearable device, Digital biomarker, Arrhythmia detection
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Seerlinq s. r. o.University, hospital or other
- Collaborators
- ACADEMY - občianske združenie, Premedix Academy
- Study start
- 1 Oct 2025 (actual)
- Primary completion
- Aug 2026 (estimated)
- Study completion
- Nov 2026 (estimated)
- First posted
- 6 Aug 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Documented AF | – | Other: PPG-based AF detection algorithmHF patients with a history of permanent/paroxysmal AF and AF documented on 12-lead ECG at enrollment |
| Non-AF | – | Other: PPG-based AF detection algorithmHF patients in sinus rhythm on the index 12-lead ECG with no prior documented AF episodes |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adults ≥18 years with a diagnosis of heart failure (HFrEF, HFmrEF, or HFpEF) from Slovakia
Key inclusion criteria (2 listed)
- 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)
Key exclusion criteria (1 listed)
- Missing a valid PPG recording
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| PPG-based AF detection algorithmThe 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. | Other | Documented AF, Non-AF |
Primary outcome
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)
8 secondary outcomes
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)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Aug 2026.
- Individual participant dataSharing planned: No. The data will not be shared publicly, but anonymized data can be shared upon reasonable request to the corresponding author.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Atrial Fibrillation (AF) and Heart Failure, run by Seerlinq s. r. o. at 1 site in Slovakia.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 6 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Slovakia. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Marta Kollárová, MSc., PhD. (+421 950 896 026, marta.kollarova@premedix.org), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Aug 2026.