In recent years, wearable devices have revolutionized cardiac monitoring by enabling continuous, non-invasive ECG recording in real-world settings. Despite these advances, the accuracy of ECG parameter calculations (PR interval, QRS interval, QT interval, etc.) from wearables remains to be rigorously validated against conventional ECG machines and expert clinician assessments. In this large-scale, multicenter study, we evaluated FeatureDB, a novel algorithm for automated computation of ECG parameters from wearable single-lead signals Three diverse datasets were employed: the AHMU-FH dataset (n=88,874), the CSE dataset (n=106), and the HeartVoice-ECG-lite dataset (n=369) with annotations provided by two experienced cardiologists. FeatureDB demonstrates a statistically significant correlation with key parameters (PR interval, QRS duration, QT interval, and QTc) calculated by standard ECG machines and annotated by clinical doctors. Bland-Altman analysis confirms a high level of agreement.Moreover,FeatureDB exhibited robust diagnostic performance in detecting Long QT syndrome (LQT) and atrioventricular block interval abnormalities (AVBI),with excellent area under the ROC curve (LQT: 0.836, AVBI: 0.861),accuracy (LQT: 0.856, AVBI: 0.845),sensitivity (LQT: 0.815, AVBI: 0.877),and specificity (LQT: 0.856, AVBI: 0.845).This further validates its clinical reliability. These results validate the clinical applicability of FeatureDB for wearable ECG analysis and highlight its potential to bridge the gap between traditional diagnostic methods and emerging wearable technologies.Ultimately,this study supports integrating wearable ECG devices into large-scale cardiovascular disease management and early intervention strategies,and it highlights the potential of wearable ECG technologies to deliver accurate,clinically relevant cardiac monitoring while advancing broader applications in cardiovascular care.

翻译：近年来，可穿戴设备通过在真实场景中实现连续、无创的心电图记录，彻底改变了心脏监测方式。尽管取得了这些进展，但可穿戴设备计算心电图参数（PR间期、QRS间期、QT间期等）的准确性，仍需针对传统心电图机和临床专家评估进行严格验证。在这项大规模、多中心研究中，我们评估了FeatureDB——一种用于从可穿戴单导联信号自动计算ECG参数的新型算法。研究采用了三个不同的数据集：AHMU-FH数据集（n=88,874）、CSE数据集（n=106）以及HeartVoice-ECG-lite数据集（n=369），并由两位经验丰富的心脏病专家提供标注。FeatureDB与标准心电图机计算并由临床医生标注的关键参数（PR间期、QRS时限、QT间期及QTc）显示出统计学上显著的相关性。Bland-Altman分析证实了高度的一致性。此外，FeatureDB在检测长QT综合征（LQT）和房室传导阻滞间期异常（AVBI）方面表现出稳健的诊断性能，其ROC曲线下面积（LQT: 0.836, AVBI: 0.861）、准确率（LQT: 0.856, AVBI: 0.845）、灵敏度（LQT: 0.815, AVBI: 0.877）和特异度（LQT: 0.856, AVBI: 0.845）均表现优异，这进一步验证了其临床可靠性。这些结果验证了FeatureDB在可穿戴心电图分析中的临床适用性，并突显了其弥合传统诊断方法与新兴可穿戴技术之间差距的潜力。最终，本研究支持将可穿戴心电图设备整合到大规模心血管疾病管理和早期干预策略中，并强调了可穿戴心电图技术在提供准确、具有临床意义的心脏监测方面的潜力，同时推动了其在心血管护理领域更广泛的应用。