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. 2025 Sep;11(9):2003-2011.
doi: 10.1016/j.jacep.2025.04.027. Epub 2025 Jun 18.

Inconclusive Single-Lead ECGs From Smart-Devices: Impact of Baseline ECG Anomalies

Corinne Isenegger  1 , Diego Mannhart  1 , Simon Weidlich  1 , Jonas Brügger  1 , Teodor Serban  1 , Fabian Jordan  1 , Philipp Krisai  1 , Sven Knecht  1 , Nicolas Schaerli  1 , Behnam Subin  1 , Luke Mosher  1 , Jeanne du Fay de Lavallaz  1 , Beat Schaer  1 , Felix Mahfoud  1 , Michael Kühne  1 , Christian Sticherling  1 , Patrick Badertscher  2
Affiliations
Free article

Inconclusive Single-Lead ECGs From Smart-Devices: Impact of Baseline ECG Anomalies

Corinne Isenegger et al. JACC Clin Electrophysiol. 2025 Sep.
Free article

Abstract

Background: Multiple smart devices can record single-lead electrocardiograms (SL-ECGs) with automated rhythm classification. The impact of pre-existing baseline ECG anomalies on the accuracy of automated rhythm classification remains largely unknown.

Objectives: This study sought to compare the presence of predefined ECG anomalies and their impact on rhythm classification ability of 5 commercially available FDA and CE-marked wearable smart-devices.

Methods: This prospective study included consecutive patients undergoing electrophysiological procedures at a tertiary referral center. Each participant obtained a 12-lead ECG followed by SL-ECGs with 5 different smart devices (AliveCor KardiaMobile, Apple Watch 6, Fitbit Sense, Samsung Galaxy Watch 3, and Withings ScanWatch). Two independent cardiologists performed manual rhythm classification and assessed the following ECG anomalies: ventricular pacing, conduction delay, low voltage, artifacts, and premature atrial or ventricular complexes.

Results: A total of 256 participants were included (29% female, mean age 66 years) generating 1,280 recorded SL-ECGs. Of these, 242 SL-ECGs (19%) were classified as inconclusive by at least 1 smart device. The presence of any ECG anomaly was significantly higher in inconclusive vs conclusive SL-ECGs, with 74% vs 42%; P < 0.001. ORs with 95% CIs for inconclusive classification by ECG anomaly were ventricular pacing 6.35 [3.84-10.61], conduction delay 2.42 [1.82-3.22], low voltage 2.37 [1.75-3.21], minor artifact 1.72 [1.17-2.51], major artifact 10.62 [6.78-16.99], premature atrial complex 2.23 [1.29-3.74], and premature ventricular complex 1.94 [1.29-2.89]. Notable differences were found between the assessed smart devices.

Conclusions: Automated rhythm classification is highly susceptible to baseline ECG anomalies. This study provides insights into the most appropriate patient population for smart device-based arrhythmia monitoring and offers guidance for selecting the optimal smart device tailored to individual patient characteristics.

Keywords: atrial fibrillation; inconclusive; smart device; smartwatch.

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Conflict of interest statement

Funding Support and Author Disclosures This work was funded by the Swiss Heart Foundation and University of Basel, Switzerland. Dr Mahfoud has been supported by Deutsche Gesellschaft für Kardiologie (DGK), Deutsche Forschungsgemeinschaft (SFB TRR219, Project-ID 322900939), and Deutsche Herzstiftung. Dr Serban has received research funding from the “Gottfried & Julia Bangerter-Rhyner Foundation” and the Swiss Academy for Medical Sciences. Dr Krisai has received speaker fees from BMS/Pfizer. Grants from the Swiss National Science Foundation, Swiss Heart Foundation, Foundation for Cardiovascular Research Basel, Machaon Foundation. Dr Knecht has received funding from the “Stiftung für kardiovaskuläre Forschung.” Dr Subin has received a travel grant from Boston Scientific. Dr du Fay de Lavallaz has received funding from the Swiss Heart Foundation outside of the submitted work. Dr Schaer has served on speaker’s bureaus for Medtronic and Zoll, both outside the submitted work. Dr Mahfoud has received speaker honoraria/consulting fees from Ablative Solutions, Amgen, Astra-Zeneca, Bayer, Boehringer Ingelheim, Inari, Medtronic, Merck, ReCor Medical, Servier, and Terumo, all outside the submitted work. Dr Kühne has received personal fees from Daiichi Sankyo; and grants from Bayer, Pfizer, Boston Scientific, BMS, Biotronik, Daiichi Sankyo, all outside the submitted work. Dr Sticherling is a member of Medtronic Advisory Board Europe and Boston Scientific Advisory Board Europe; has received educational grants from Biosense Webster and Biotronik; has received a research grant from the European Union’s FP7 program and Biosense Webster; and has received lecture and consulting fees from Abbott, Medtronic, Biosense-Webster, Boston Scientific, Microport, and Biotronik, all outside the submitted work. Dr Badertscher has received research funding from the “University of Basel,” the “Stiftung für Herzschrittmacher und Elektrophysiologie,” the “Freiwillige Akademische Gesellschaft Basel,” the “Swiss Heart Foundation” and Johnson & Johnson; and has received personal fees from BMS, Boston Scientific, and Abbott, all outside the submitted work. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

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