Kavli Affiliate: Cheng Peng
| First 5 Authors: Sumei Fan, Sumei Fan, , ,
| Summary:
Accurate, continuous out-of-hospital electrocardiogram (ECG) parameter
measurement is vital for real-time cardiac health monitoring and telemedicine.
On-device computation of single-lead ECG parameters enables timely assessment
without reliance on centralized data processing, advancing personalized,
ubiquitous cardiac care-yet comprehensive validation across heterogeneous
real-world populations remains limited. This study validated the on-device
algorithm FeatureDB (https://github.com/PKUDigitalHealth/FeatureDB) using two
datasets: HeartVoice-ECG-lite (369 participants with single-lead ECGs annotated
by two physicians) and PTB-XL/PTB-XL+ (21,354 patients with 12-lead ECGs and
physicians’ diagnostic annotations). FeatureDB computed PR, QT, and QTc
intervals, with accuracy evaluated against physician annotations via mean
absolute error (MAE), correlation analysis, and Bland-Altman analysis.
Diagnostic performance for first-degree atrioventricular block (AVBI, PR-based)
and long QT syndrome (LQT, QTc-based) was benchmarked against commercial
12-lead systems (12SL, Uni-G) and open-source algorithm Deli, using AUC,
accuracy, sensitivity, and specificity. Results showed high concordance with
expert annotations (Pearson correlations: 0.836-0.960), MAEs matching
inter-observer variability, and minimal bias. AVBI AUC reached 0.787 (12SL:
0.859; Uni-G: 0.812; Deli: 0.501); LQT AUC was 0.684 (12SL: 0.716; Uni-G:
0.605; Deli: 0.569)-comparable to commercial tools and superior to open-source
alternatives. FeatureDB delivers physician-level parameter accuracy and
commercial-grade abnormality detection via single-lead devices, supporting
scalable telemedicine, decentralized cardiac screening, and continuous
monitoring in community and outpatient settings.
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