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Updated: Sep 19, 2025
Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
Published on: December 11, 2019
Inconclusive Single-Lead ECGs From Smart-Devices: Impact of Baseline ECG Anomalies
Corinne Isenegger1, Diego Mannhart1, Simon Weidlich1
1Department of Cardiology, University Hospital Basel, Basel, Switzerland; Cardiovascular Research Institute Basel, University Hospital Basel, University of Basel, Basel, Switzerland.
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.
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