Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients
Authors: Dani Kiyasseh, Tingting Zhu, David A Clifton
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct our experiments1 using Py Torch (Paszke et al., 2019) on four ECG datasets that include cardiac arrhythmia labels. |
| Researcher Affiliation | Academia | 1Department of Engineering Science, University of Oxford, Oxford, United Kingdom 2Oxford-Suzhou Centre for Advanced Research, Suzhou, China. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Code can be accessed at: https://github.com/ danikiyasseh/CLOCS |
| Open Datasets | Yes | We conduct our experiments1 using Py Torch (Paszke et al., 2019) on four ECG datasets that include cardiac arrhythmia labels. Physio Net 2020 (Perez Alday et al., 2020) consists of 12-lead ECG recordings from 6,877 patients alongside 9 different classes of cardiac arrhythmia. Chapman (Zheng et al., 2020) consists of 12-lead ECG recordings from 10,646 patients alongside 11 different classes of cardiac arrhythmia. Physio Net 2017 (Clifford et al., 2017) consists of 8,528 single-lead ECG recordings alongside 4 different classes. Cardiology (Hannun et al., 2019) consists of single-lead ECG recordings from 328 patients alongside 12 different classes of cardiac arrhythmia. |
| Dataset Splits | Yes | All datasets were split into training, validation, and test sets according to patient ID using a 60, 20, 20 configuration. |
| Hardware Specification | No | The paper does not specify the hardware used (e.g., GPU/CPU models, memory) for running experiments. |
| Software Dependencies | No | The paper mentions "Py Torch (Paszke et al., 2019)" but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | During self-supervised pre-training, we chose the temperature parameter, τ = 0.1, as per (Chen et al., 2020). For BYOL, we chose the decay rate, τd = 0.90, after experimenting with various alternatives (see Appendix F). For all experiments, we use a neural architecture composed of three 1D convolutional layers followed by two fully connected layers. |