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

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.