Detecting Long QT Syndrome and First-Degree Atrioventricular Block using Single-Lead AI-ECG: A Multi-Center Real-World Study

• URL: http://arxiv.org/abs/2502.17499v2
• Date: Sun, 27 Apr 2025 02:46:38 GMT
• Title: Detecting Long QT Syndrome and First-Degree Atrioventricular Block using Single-Lead AI-ECG: A Multi-Center Real-World Study
• Authors: Sumei Fan, Deyun Zhang, Yue Wang, Shijia Geng, Kun Lu, Meng Sang, Weilun Xu, Haixue Wang, Qinghao Zhao, Chuandong Cheng, Peng Wang, Shenda Hong,
• Abstract summary: Home-based single-lead AI-ECG devices have enabled continuous, real-world cardiac monitoring.<n>In this study, we assessed FeatureDB, an ECG measurements computation algorithm, in the context of single-lead monitoring.<n>In detecting LQTS and AVBI, FeatureDB demonstrated diagnostic performance comparable to commercial ECG systems.
• Score: 12.802249610851181
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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