Adult; Humans; Belgium; Electrocardiography; Electrocardiography, Ambulatory; Atrial Fibrillation/diagnosis; Atrial Fibrillation; Statistics and Probability; Information Systems; Education; Computer Science Applications; Statistics, Probability and Uncertainty; Library and Information Sciences
Abstract :
[en] Atrial fibrillation (AF) is the most common sustained heart arrhythmia in adults. Holter monitoring, a long-term 2-lead electrocardiogram (ECG), is a key tool available to cardiologists for AF diagnosis. Machine learning (ML) and deep learning (DL) models have shown great capacity to automatically detect AF in ECG and their use as medical decision support tool is growing. Training these models rely on a few open and annotated databases. We present a new Holter monitoring database from patients with paroxysmal AF with 167 records from 152 patients, acquired from an outpatient cardiology clinic from 2006 to 2017 in Belgium. AF episodes were manually annotated and reviewed by an expert cardiologist and a specialist cardiac nurse. Records last from 19 hours up to 95 hours, divided into 24-hour files. In total, it represents 24 million seconds of annotated Holter monitoring, sampled at 200 Hz. This dataset aims at expanding the available options for researchers and offers a valuable resource for advancing ML and DL use in the field of cardiac arrhythmia diagnosis.
general metadata file and 167 folders, one for each record in the database. Each record folder includes
the ECG waveform from the Holter record and the associated annotations. It also contains the RR intervals and associated annotations.
Hindricks, G. et al. 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS). European Heart Journal 42, 373–498, 10.1093/eurheartj/ehaa612 (2021). DOI: 10.1093/eurheartj/ehaa612
Benjamin, E. J. et al. Heart Disease and Stroke Statistics-2019 Update: A Report From the American Heart Association. Circulation 139, e56–e528, 10.1161/CIR.0000000000000659 (2019). DOI: 10.1161/CIR.0000000000000659
Wolf, P. A., Dawber, T. R., Thomas, H. E. & Kannel, W. B. Epidemiologic assessment of chronic atrial fibrillation and risk of stroke: The fiamingham Study. Neurology 28, 973–973, 10.1212/WNL.28.10.973 (1978). DOI: 10.1212/WNL.28.10.973
Magnussen, C. et al. Sex Differences and Similarities in Atrial Fibrillation Epidemiology, Risk Factors, and Mortality in Community Cohorts: Results From the BiomarCaRE Consortium (Biomarker for Cardiovascular Risk Assessment in Europe). Circulation 136, 1588–1597, 10.1161/CIRCULATIONAHA.117.028981 (2017). DOI: 10.1161/CIRCULATIONAHA.117.028981
Hannun, A. Y. et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine 25, 65–69, 10.1038/s41591-018-0268-3 (2019). DOI: 10.1038/s41591-018-0268-3
Attia, Z. I. et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. The Lancet 394, 861–867, 10.1016/S0140-6736(19)31721-0 (2019). DOI: 10.1016/S0140-6736(19)31721-0
Moody, G. & Mark, R. The impact of the MIT-BIH Arrhythmia Database. IEEE Engineering in Medicine and Biology Magazine 20, 45–50, 10.1109/51.932724 (2001). DOI: 10.1109/51.932724
Goldberger, A. L. et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101, 10.1161/01.CIR.101.23.e215 (2000).
Wagner, P. et al. PTB-XL, a large publicly available electrocardiography dataset. Scientific Data 7, 154, 10.1038/s41597-020-0495-6 (2020). DOI: 10.1038/s41597-020-0495-6
Clifford, G. D. et al. AF Classification from a Short Single Lead ECG Recording: the PhysioNet/Computing in Cardiology Challenge 2017. Computing in cardiology 44 (2017).
Gilon, C., Gregoire, J.-M. & Bersini, H. Forecast of paroxysmal atrial fibrillation using a deep neural network. In 2020 International Joint Conference on Neural Networks (IJCNN), 1–7, https://doi.org/10.1109/IJCNN48605.2020.9207227 (2020).
Badilini, F., ISHNE Standard Output Format Task Force. The ISHNE Holter Standard Output File Format. Annals of Noninvasive Electrocardiology 3, 263–266, 10.1111/j.1542-474X.1998.tb00353.x (1998). DOI: 10.1111/j.1542-474X.1998.tb00353.x
Liu, H. et al. A large-scale multi-label 12-lead electrocardiogram database with standardized diagnostic statements. Scientific Data 9, 272, 10.1038/s41597-022-01403-5 (2022). DOI: 10.1038/s41597-022-01403-5
Gilon, C., Grégoire, J.-M., Mathieu, M., Carlier, S. & Bersini, H. IRIDIA-AF, a large paroxysmal atrial fibrillation long-term electrocardiogram monitoring database. Zenodo 10.5281/zenodo.8405941 (2023).
Makowski, D. et al. NeuroKit2: A Python toolbox for neurophysiological signal processing. Behavior Research Methods 53, 1689–1696, 10.3758/s13428-020-01516-y (2021). DOI: 10.3758/s13428-020-01516-y
Pedregosa, F. et al. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011).
Abadi, M. et al. TensorFlow: a system for large-scale machine learning. In Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation, 265–283 (2016).
Paszke, A. et al. PyTorch: an imperative style, high-performance deep learning library. Proceedings of the 33rd International Conference on Neural Information Processing Systems 721, 8026–8037 (2019).
Zheng, J. et al. A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients. Scientific Data 7, 48, 10.1038/s41597-020-0386-x (2020). DOI: 10.1038/s41597-020-0386-x