Abstract
Intraoperative cardiac arrhythmias present distinct characteristics compared to non-surgical environments, yet publicly available electrocardiogram (ECG) databases have primarily focused on ambulatory or intensive care environments. To address this gap, we present the VitalDB Arrhythmia Database, a comprehensive collection of intraoperative ECG recordings with beat and rhythm labels specifically designed for developing and validating arrhythmia detection algorithms in surgical patients. The database comprises 734,528 seconds of continuous ECG data from 482 surgical patients, with a median annotated recording duration of 20 minutes. It contains over 660,000 annotated heartbeats across four beat types and 10 distinct rhythm categories. To efficiently process the extensive source data, we developed a custom deep learning beat classifier that serves as an automated screening tool for arrhythmia candidate segments. All annotations underwent rigorous validation by five anesthesiologists, with each segment independently reviewed by at least two anesthesiologists, and 9.3% required full committee consensus. Inter-rater reliability analysis demonstrated excellent agreement with an overall Cohen’s kappa of 0.930 ± 0.130. This publicly accessible resource provides the research community with clinically validated intraoperative arrhythmia data, facilitating the development of robust arrhythmia detection algorithms and enabling multimodal analysis to investigate the hemodynamic impact of intraoperative arrhythmias.
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Data availability
The VitalDB Arrhythmia Database, including all beat and rhythm annotations generated and analyzed during the current study, is publicly available at PhysioNet (https://doi.org/10.13026/axd6-wm13)18 and at the GitHub repository (https://github.com/vitaldb/arrdb)10. The dataset is made publicly available under the Creative Commons Attribution 4.0 International (CC BY 4.0) License. This license places no restrictions on commercial or derivative use, fostering open collaboration and the development of third-party algorithms, provided the original work is properly cited.
Code availability
The screening algorithm used to create this database is publicly available as an open-source tool through the Pyvital Python package. Detailed usage instructions and implementation examples can be found in the Vital_beat_noise_detection.ipynb notebook at https://github.com/vitaldb/arrdb10.
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Eun, D. et al. VitalDB Arrhythmia Database: An Anesthesiologist-Validated Large-Scale Intraoperative Arrhythmia Dataset with Beat and Rhythm Labels. PhysioNet. https://doi.org/10.13026/axd6-wm13 (2026).
Acknowledgements
This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: RS-2024-00439677, NTIS number:2460003917).
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Da-in Eun developed the screening algorithm, performed technical validation, participated in annotation labeling, and wrote the manuscript. Kayoung Shim, Hyunsoo Lee, Yeji Lim, and Hanbyeol Lim participated in the annotation labeling process and contributed to manuscript preparation. Hyeonhoon Lee and Jiwon Lee reviewed the data and contributed to manuscript preparation. Hyung-Chul Lee conceived and supervised the VitalDB project, designed the research plan, oversaw data collection and preparation, and wrote the manuscript.
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Eun, DI., Shim, K., Lee, H. et al. VitalDB Arrhythmia Database: An Anesthesiologist-Validated Large-scale Intraoperative Arrhythmia Dataset with Beat and Rhythm Labels. Sci Data (2026). https://doi.org/10.1038/s41597-026-07076-8
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DOI: https://doi.org/10.1038/s41597-026-07076-8
