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

2026年7月31日 更新者：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.

研究概览

地位

招聘中

条件

干预/治疗

详细说明

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.

研究类型

观察性的

注册 (估计的)

200

联系人和位置

本节提供了进行研究的人员的详细联系信息，以及有关进行该研究的地点的信息。

学习联系方式

学习地点

  • 斯洛伐克
      • Bratislava、斯洛伐克
        • 招聘中
        • PreMedix
        • 接触：
        • 首席研究员：
          • Allan Bohm, M.D., MSc., PhD.

参与标准

研究人员寻找符合特定描述的人，称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。

资格标准

适合学习的年龄

  • 成人
  • 年长者

接受健康志愿者

不

取样方法

非概率样本

研究人群

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

描述

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

学习计划

本节提供研究计划的详细信息，包括研究的设计方式和研究的衡量标准。

研究是如何设计的？

设计细节

队列和干预

团体/队列
干预/治疗
Documented AF
HF patients with a history of permanent/paroxysmal AF and AF documented on 12-lead ECG at enrollment
其他：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
其他：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.

研究衡量的是什么？

主要结果指标

结果测量
大体时间
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
大体时间：Through study completion (estimated November 2026)
Through study completion (estimated November 2026)

次要结果测量

结果测量
措施说明
大体时间
Sensitivity and specificity of the algorithm at the Youden-optimal threshold
大体时间：Through study completion (estimated November 2026)
Through study completion (estimated November 2026)
Positive predictive value and negative predictive value
大体时间：Through study completion (estimated November 2026)
Through study completion (estimated November 2026)
Average precision
大体时间：Through study completion (estimated November 2026)
area under the precision-recall curve
Through study completion (estimated November 2026)
Model calibration
大体时间：Through study completion (estimated November 2026)
e.g., calibration curve / Brier score
Through study completion (estimated November 2026)
Matthews correlation coefficient
大体时间：Through study completion (estimated November 2026)
Through study completion (estimated November 2026)
Overall classification accuracy
大体时间：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
大体时间：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
大体时间：Through study completion (estimated November 2026)
Through study completion (estimated November 2026)

合作者和调查者

在这里您可以找到参与这项研究的人员和组织。

赞助

Seerlinq s. r. o.

合作者

Premedix Academy
ACADEMY - občianske združenie

研究记录日期

这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查，以确保它们在发布到公共网站之前符合特定的质量控制标准。

研究主要日期

学习开始 (实际的)

2025年10月1日

初级完成 (估计的)

2026年8月1日

研究完成 (估计的)

2026年11月1日

研究注册日期

首次提交

2026年7月31日

首先提交符合 QC 标准的

2026年7月31日

首次发布 (实际的)

2026年8月6日

研究记录更新

最后更新发布 (实际的)

2026年8月6日

上次提交的符合 QC 标准的更新

2026年7月31日

最后验证

2026年7月1日

更多信息

与本研究相关的术语

关键字

其他相关的 MeSH 术语

其他研究编号

  • HeartCoreAF01

计划个人参与者数据 (IPD)

计划共享个人参与者数据 (IPD)？

不

IPD 计划说明

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

药物和器械信息、研究文件

研究美国 FDA 监管的药品

不

研究美国 FDA 监管的设备产品

不

此信息直接从 clinicaltrials.gov 网站检索，没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求，请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改，我们的网站上也会自动更新.

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