Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol

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Heart Rate Variability (HRV) Analysis: A Methodology for Organizational Neuroscience (2019, methodology): “Moreover, several accurate openaccess tools have been recently developed (e.g., Singh & Bharti, 2015), such as PhysioToolkit (Goldberger et al., 2000) and Kubios, which is an intuitive platform allowing HRV analysis on all its main domains (Tarvainen et al., 2009).”
A robust algorithm for heart rate variability time series artefact correction using novel beat classification (2019, methodology): “from the Fantasia database [14] and MIT-BIH arrhythmia database [15], both available at the PhysioNet archive [16].”
Efficient Epileptic Seizure Prediction Based on Deep Learning (2019, background): “Children’s Hospital Boston [18], [19] which is publicly available [20].”
Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing (2023, methodology): “Datasets For pre-training, we use the MIMIC-CXR v2 [28, 36] chest X-ray dataset, which contains longitudinal imaging studies with corresponding radiological reports,”
Analysis of Human Electrocardiogram for Biometric Recognition (2008, methodology): “After preprocessing, the R peaks of an ECG trace are localized by using a QRS detector, ECGPUWAVE [15, 16].”
Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020 (2020, methodology): “by providing data from a wide set of sources with a large set of cardiac abnormalities [5, 12, 13].”