Home-based single-lead AI-ECG devices have enabled continuous, real-world cardiac monitoring. However, the accuracy of parameter calculations from single-lead AI-ECG algorithm remains to be fully validated, which is critical for conditions such as Long QT Syndrome (LQTS) and First-Degree Atrioventricular Block (AVBI). In this multicenter study, we assessed FeatureDB, an ECG measurements computation algorithm, in the context of single-lead monitoring using three annotated datasets: PTB-XL+ (n=21,354), CSE (n=105), and HeartVoice-ECG-lite (n=369). FeatureDB showed strong correlation with standard ECG machines (12SL and Uni-G) in key measurements (PR, QRS, QT, QTc), and high agreement confirmed by Bland-Altman analysis. In detecting LQTS (AUC=0.786) and AVBI (AUC=0.684), FeatureDB demonstrated diagnostic performance comparable to commercial ECG systems (12SL: 0.859/0.716; Uni-G: 0.817/0.605), significantly outperforming ECGDeli (0.501/0.569). Notably, FeatureDB can operate locally on resource-limited devices, facilitating use in low-connectivity settings. These findings confirm the clinical reliability of FeatureDB for single-lead ECG diagnostics and highlight its potential to bridge traditional ECG diagnostics with wearable technology for scalable cardiovascular monitoring and early intervention.

翻译：基于家庭场景的单导联AI-ECG设备已实现连续的真实世界心脏监测。然而，单导联AI-ECG算法参数计算的准确性仍有待充分验证，这对于长QT综合征（LQTS）和一度房室传导阻滞（AVBI）等疾病的诊断至关重要。在这项多中心研究中，我们使用三个标注数据集——PTB-XL+（n=21,354）、CSE（n=105）和HeartVoice-ECG-lite（n=369），评估了心电图测量计算算法FeatureDB在单导联监测场景下的性能。FeatureDB在关键测量参数（PR、QRS、QT、QTc）上与标准心电图机（12SL和Uni-G）表现出强相关性，且Bland-Altman分析证实了其高度一致性。在检测LQTS（AUC=0.786）和AVBI（AUC=0.684）方面，FeatureDB的诊断性能与商用心电图系统相当（12SL：0.859/0.716；Uni-G：0.817/0.605），并显著优于ECGDeli（0.501/0.569）。值得注意的是，FeatureDB可在资源受限的设备上本地运行，便于在低连接性环境中使用。这些结果证实了FeatureDB在单导联心电图诊断中的临床可靠性，并突显了其在连接传统心电图诊断与可穿戴技术、实现可扩展心血管监测及早期干预方面的潜力。