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<p>Accurate and continuous electrocardiogram (ECG) parameter measurement outside hospital environments is essential for real-time cardiac health monitoring and telemedicine, yet comprehensive validation of on-device computational methods across heterogeneous real-world populations remains limited. We conducted a real-world validation using two datasets: HeartVoice-ECG-lite (369 participants with single-lead ECG recordings annotated by two cardiologists) and PTB-XL/PTB-XL+ (21,354 patients with 12-lead ECG recordings and diagnostic annotations). FeatureDB (<a href="https://github.com/PKUDigitalHealth/FeatureDB">https://github.com/PKUDigitalHealth/FeatureDB</a>) was applied to compute PR, QT, and QTc intervals from single-lead signals. Accuracy was assessed using mean absolute error (MAE), correlation, and Bland-Altman analysis. Diagnostic performance for first-degree atrioventricular block (AVBI, based on PR) and long QT syndrome (LQT, based on QTc) was benchmarked against commercial 12-lead systems (12SL, Uni-G) and an open-source algorithm (Deli) using AUC, accuracy, sensitivity, and specificity. FeatureDB-derived parameters showed high concordance with annotations, with MAEs comparable to inter-observer variability and Pearson correlations ranging from 0.836 to 0.960. For AVBI detection, FeatureDB achieved an AUC of 0.787; for LQT, an AUC of 0.684. These results indicate that FeatureDB enables accurate real-time on-device ECG parameter computation suitable for scalable telemedicine, decentralized screening, and continuous community monitoring.</p>

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Multicenter real world validation of on device single lead ECG parameters for remote cardiac assessment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月10日

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  • Sumei Fan,
  • Deyun Zhang,
  • Yue Wang,
  • Shijia Geng,
  • Kun Lu,
  • Meng Sang,
  • Weilun Xu,
  • Haixue Wang,
  • Qinghao Zhao,
  • Chuandong Cheng,
  • Peng Wang,
  • Shenda Hong

摘要

Accurate and continuous electrocardiogram (ECG) parameter measurement outside hospital environments is essential for real-time cardiac health monitoring and telemedicine, yet comprehensive validation of on-device computational methods across heterogeneous real-world populations remains limited. We conducted a real-world validation using two datasets: HeartVoice-ECG-lite (369 participants with single-lead ECG recordings annotated by two cardiologists) and PTB-XL/PTB-XL+ (21,354 patients with 12-lead ECG recordings and diagnostic annotations). FeatureDB (https://github.com/PKUDigitalHealth/FeatureDB) was applied to compute PR, QT, and QTc intervals from single-lead signals. Accuracy was assessed using mean absolute error (MAE), correlation, and Bland-Altman analysis. Diagnostic performance for first-degree atrioventricular block (AVBI, based on PR) and long QT syndrome (LQT, based on QTc) was benchmarked against commercial 12-lead systems (12SL, Uni-G) and an open-source algorithm (Deli) using AUC, accuracy, sensitivity, and specificity. FeatureDB-derived parameters showed high concordance with annotations, with MAEs comparable to inter-observer variability and Pearson correlations ranging from 0.836 to 0.960. For AVBI detection, FeatureDB achieved an AUC of 0.787; for LQT, an AUC of 0.684. These results indicate that FeatureDB enables accurate real-time on-device ECG parameter computation suitable for scalable telemedicine, decentralized screening, and continuous community monitoring.