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