用单导联设备在本地实时计算心电参数，准确率达医生水平。
On-device Computation of Single-lead ECG Parameters for Real-time Remote Cardiac Health Assessment: A Real-world Validation Study
- 基于单导联心电图在设备端直接计算心电参数
- 对房室传导阻滞和长QT综合征检测的AUC达0.787和0.684
- 适合远程医疗、社区筛查与长期居家监测
精准、持续的院外心电图参数测量对实时心脏健康监测和远程医疗至关重要。在设备端计算单导联心电图参数可实现无需依赖中心化处理的及时评估，推动个性化、普及化心脏健康管理，但跨异质真实人群的全面验证仍不足。本研究使用两个数据集验证了设备端算法FeatureDB（https://github.com/PKUDigitalHealth/FeatureDB）：HeartVoice-ECG-lite（369名参与者，单导联心电图由两名医师标注）和PTB-XL/PTB-XL+（21,354名患者，12导联心电图及医师诊断标注）。FeatureDB计算PR、QT和QTc间期，通过平均绝对误差（MAE）、相关性分析和Bland-Altman分析与医师标注对比。第一度房室传导阻滞（AVBI，基于PR）和长QT综合征（LQT，基于QTc）的诊断性能与商用12导联系统（12SL、Uni-G）及开源算法Deli对比，采用AUC、准确率、敏感性和特异性。结果显示与专家标注高度一致（皮尔逊相关系数：0.836–0.960），MAE与观察者间差异相当，偏差极小。AVBI AUC达0.787（12SL：0.859；Uni-G：0.812；Deli：0.501）；LQT AUC为0.684（12SL：0.716；Uni-G：0.605；Deli：0.569），与商用工具相当且优于开源方案。FeatureDB实现了医生级参数精度和商用级异常检测能力，支持可扩展的远程医疗、去中心化心脏筛查及社区与门诊环境下的连续监测。
原文摘要 · Abstract (English)
Accurate, continuous out-of-hospital electrocardiogram (ECG) parameter measurement is vital for real-time cardiac health monitoring and telemedicine. On-device computation of single-lead ECG parameters enables timely assessment without reliance on centralized data processing, advancing personalized, ubiquitous cardiac care-yet comprehensive validation across heterogeneous real-world populations remains limited. This study validated the on-device algorithm FeatureDB (https://github.com/PKUDigitalHealth/FeatureDB) using two datasets: HeartVoice-ECG-lite (369 participants with single-lead ECGs annotated by two physicians) and PTB-XL/PTB-XL+ (21,354 patients with 12-lead ECGs and physicians' diagnostic annotations). FeatureDB computed PR, QT, and QTc intervals, with accuracy evaluated against physician annotations via mean absolute error (MAE), correlation analysis, and Bland-Altman analysis. Diagnostic performance for first-degree atrioventricular block (AVBI, PR-based) and long QT syndrome (LQT, QTc-based) was benchmarked against commercial 12-lead systems (12SL, Uni-G) and open-source algorithm Deli, using AUC, accuracy, sensitivity, and specificity. Results showed high concordance with expert annotations (Pearson correlations: 0.836-0.960), MAEs matching inter-observer variability, and minimal bias. AVBI AUC reached 0.787 (12SL: 0.859; Uni-G: 0.812; Deli: 0.501); LQT AUC was 0.684 (12SL: 0.716; Uni-G: 0.605; Deli: 0.569)-comparable to commercial tools and superior to open-source alternatives. FeatureDB delivers physician-level parameter accuracy and commercial-grade abnormality detection via single-lead devices, supporting scalable telemedicine, decentralized cardiac screening, and continuous monitoring in community and outpatient settings.
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