CPSC2018 (China Physiological Signal Challenge 2018): 12-lead ECG arrhythmia set, redistributed in PhysioNet Challenge 2020. Recorded signals include ecg. Licence: Creative Commons Attribution 4.0 International (PhysioNet Challenge 2020 v1.0.2 redistribution).
The electrocardiogram (ECG) is a non-invasive representation of the electrical activity of the heart from electrodes placed on the surface of the torso. The standard 12-lead ECG has been widely used to diagnose a variety of cardiac abnormalities such as cardiac arrhythmias, and predicts cardiovascular morbidity and mortality [1]. The early and correct diagnosis of cardiac abnormalities can increase the chances of successful treatments [2]. However, manual interpretation of the electrocardiogram is time-consuming, and requires skilled personnel with a high degree of training [3].
Automatic detection and classification of cardiac abnormalities can assist physicians in the diagnosis of the growing number of ECGs recorded. Over the last decade, there have been increasing numbers of attempts to stimulate 12-lead ECG classification. Many of these algorithms seem to have the potential for accurate identification of cardiac abnormalities. However, most of these methods have only been tested or developed in single, small, or relatively homogeneous datasets. The PhysioNet/Computing in Cardiology Challenge 2020 provides an opportunity to address this problem by providing data from a wide set of sources.
Verbatim fromPhysioNet· Licence: Creative Commons Attribution 4.0 International Public License
ECG-JEPA: Learning General Representation of 12-Lead ECG with a Joint-Embedding Predictive ArchitectureEvaluationNeural Networks · 2027
BenchECG and xECG: a benchmark and baseline for ECG foundation models (xLSTM)npj Digital Medicine · 2026
ECGJev: Question-Conditioned Option Scoring for 12-Lead ECG Interpretation (arXiv 2611.02041)TrainingEvaluationarXiv 2611.02041v1 · 2026
MS-ECG-FM: Towards a More Universal Electrocardiogram Foundation Model for Health Monitoring using Multi-source Contrastive LearningarXiv preprint · 2026
QECGBERT: a scalable ECG foundation model based on self-supervised language learning via VQ-VAE waveform tokenizationarXiv 2610.33964v1 · 2026#29 of 158 most-used datasets by published papers and models · #13 of 70 ECG
Browse these 5 papers in the research explorer (filtered to CPSC2018 (China Physiological Signal Challenge 2018): 12-lead ECG arrhythmia set, redistributed in PhysioNet Challenge 2020)
CPSC2018 (China Physiological Signal Challenge 2018): 12-lead ECG arrhythmia set, redistributed in PhysioNet Challenge 2020 [Data set]. (n.d.). physionet. https://doi.org/10.13026/dvyd-kd57
@misc{cpsc2018cpsc2018,
title = {{CPSC2018 (China Physiological Signal Challenge 2018): 12-lead ECG arrhythmia set, redistributed in PhysioNet Challenge 2020}},
howpublished = {physionet},
doi = {10.13026/dvyd-kd57}
}Cite the dated snapshot 2026-10 for a fixed, reproducible version. Please also cite the original publications listed above.
ChatPPG. (2026). CPSC2018 (China Physiological Signal Challenge 2018): 12-lead ECG arrhythmia set, redistributed in PhysioNet Challenge 2020: dataset record (Version 2026-10) [Data set]. ChatPPG Research Hub. https://chatppg.com/research/datasets/cpsc2018
@misc{chatppg_dataset_cpsc2018,
author = {{ChatPPG}},
title = {{CPSC2018 (China Physiological Signal Challenge 2018): 12-lead ECG arrhythmia set, redistributed in PhysioNet Challenge 2020: dataset record}},
year = {2026},
month = nov,
version = {2026-10},
howpublished = {\url{https://chatppg.com/research/datasets/cpsc2018}},
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: "CPSC2018 (China Physiological Signal Challenge 2018): 12-lead ECG arrhythmia set, redistributed in PhysioNet Challenge 2020: dataset record" authors: - name: "ChatPPG" url: "https://chatppg.com/research/datasets/cpsc2018" repository-artifact: "https://chatppg.com/research/snapshots/2026-10" version: "2026-10" date-released: "2026-11-09"
Record updated
BenchECG and xECG: a benchmark and baseline for ECG foundation models (xLSTM)Uses CPSC2018 (China Physiological Signal Challenge 2018): 12-lead ECG arrhythmia set, redistributed in PhysioNet Challenge 2020npj Digital Medicine · 2026ECG-JEPA: Learning General Representation of 12-Lead ECG with a Joint-Embedding Predictive ArchitectureUses CPSC2018 (China Physiological Signal Challenge 2018): 12-lead ECG arrhythmia set, redistributed in PhysioNet Challenge 2020Neural Networks · 2027ECGJev: Question-Conditioned Option Scoring for 12-Lead ECG Interpretation (arXiv 2611.02041)Uses CPSC2018 (China Physiological Signal Challenge 2018): 12-lead ECG arrhythmia set, redistributed in PhysioNet Challenge 2020arXiv 2611.02041v1 · 2026
ECGJevUses CPSC2018 (China Physiological Signal Challenge 2018): 12-lead ECG arrhythmia set, redistributed in PhysioNet Challenge 2020question-conditioned ECG tokens + 0.8B option-scoring decoder for 12-lead ECG QA/classificationECG2026
IECGUses CPSC2018 (China Physiological Signal Challenge 2018): 12-lead ECG arrhythmia set, redistributed in PhysioNet Challenge 2020weakly-supervised interpretability-enhancing ECG foundation modelFoundation modelECGText2026
QECGBERT (VQ-VAE beat-token BERT ECG FM; Afghah lab)Uses CPSC2018 (China Physiological Signal Challenge 2018): 12-lead ECG arrhythmia set, redistributed in PhysioNet Challenge 2020Foundation modelECG2026