On-device Computation of Single-lead ECG Parameters for Real-time Remote Cardiac Health Assessment: A Real-world Validation Study Article Swipe

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Sumei Fan , Deyun Zhang , Yue Wang , Shijia Geng , Kun Lu , M Sang , Weixiang Xu , Hua Wang , Qinghao Zhao , Chuandong Cheng , Wang Peng , Shenda Hong ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2502.17499

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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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2502.17499
PDF: https://arxiv.org/pdf/2502.17499
OA Status: green
OpenAlex ID: https://openalex.org/W4415906761

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DOI: https://doi.org/10.48550/arxiv.2502.17499

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Title: On-device Computation of Single-lead ECG Parameters for Real-time Remote Cardiac Health Assessment: A Real-world Validation Study

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-02-21

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Authors: Sumei Fan, Deyun Zhang, Yue Wang, Shijia Geng, Kun Lu, M Sang, Weixiang Xu, Hua Wang, Qinghao Zhao, Chuandong Cheng, Wang Peng, Shenda Hong

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Landing page: https://arxiv.org/abs/2502.17499

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PDF URL: https://arxiv.org/pdf/2502.17499

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