Kavli Affiliate: Cheng Peng
| First 5 Authors: Sumei Fan, Deyun Zhang, Yue Wang, Shijia Geng, Kun Lu
| Summary:
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.
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