Recas J., Buelga Suárez M., González-Cabeza S., Sanz-Guerrero M., Diaz-Vicente M., Rebolleda A. et al. (Complutense University of Madrid). A METRON PS-440 simulator generated calibrated ECG with heart rate 30–300 bpm, R-wave amplitude 500–2000 µV and ST elevation/depression −800 to +800 µV. Each watch was mounted on a stand and connected to the simulator's RA/LA outputs (crown/button and back electrode); the Philips TC30 recorded the same signals with standard 12-lead cables. Watch PDFs are not included; only raw numeric data converted to WFDB.
Wearable technology has progressed significantly, such that today's smartwatches are able to offer advanced health monitoring functions, including the electrocardiogram (ECG). This study presents a dataset that can be used to clinically assess the accuracy of four leading smartwatch models: Apple Watch Series 9, Samsung Galaxy Watch 6, Fitbit Sense 2, and Withings ScanWatch. Using the standardized protocol of the International Electrotechnical Commission (IEC 60601-2-25:2011) and a METRON PS-440 patient simulator, controlled ECG signals were generated and recorded to assess smartwatch performance against a reference electrocardiograph (Philips TC30). Parameters such as heart rate (30 to 300 beats per minute), R-wave amplitude (500 to 2000 µV), and ST-segment elevation/depression (-800 to +800 µV) were configured in the patient simulator and recorded five times across all devices, resulting in a total of 900 records in the dataset. This extensive dataset provides valuable information on the feasibility of smartwatches as diagnostic tools, allowing for the observation of both their strengths and limitations for clinical and remote cardiovascular monitoring.
The recordings from the smartwatches last 30 seconds and correspond to Lead I, with a different sampling rate for each device: 512 Hz for Apple Watch Series 9; 500 Hz for Samsung Galaxy Watch 6; 250 Hz for Fitbit Sense 2; and 300 Hz for Withings ScanWatch. In the case of the Philips TC30 reference device, the recordings last 11 seconds, include 12 leads and have a sampling rate of 500 Hz.
Verbatim fromPhysioNet· Licence: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License
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ECG-capable smartwatches (PhysioNet) [Data set]. (n.d.). physionet. https://doi.org/10.13026/g59m-vn30
@misc{ecgcapablesmartwecgcapable,
title = {{ECG-capable smartwatches (PhysioNet)}},
howpublished = {physionet},
doi = {10.13026/g59m-vn30}
}Cite the dated snapshot 2026-10 for a fixed, reproducible version. Please also cite the original publications listed above.
ChatPPG. (2026). ECG-capable smartwatches (PhysioNet): dataset record (Version 2026-10) [Data set]. ChatPPG Research Hub. https://chatppg.com/research/datasets/ecg-capable-smartwatches
@misc{chatppg_dataset_ecg_capable_smartwatches,
author = {{ChatPPG}},
title = {{ECG-capable smartwatches (PhysioNet): dataset record}},
year = {2026},
month = oct,
version = {2026-10},
howpublished = {\url{https://chatppg.com/research/datasets/ecg-capable-smartwatches}},
note = {ChatPPG Research Hub, Snapshot 2026-10, https://chatppg.com/research/snapshots/2026-10}
}cff-version: 1.2.0 message: "If you use this resource, please cite it as below." type: dataset title: "ECG-capable smartwatches (PhysioNet): dataset record" authors: - name: "ChatPPG" url: "https://chatppg.com/research/datasets/ecg-capable-smartwatches" repository-artifact: "https://chatppg.com/research/snapshots/2026-10" version: "2026-10" date-released: "2026-10-31"
Record updated
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