An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection

By E. Ng, Feifei Liu, Chengyu Liu, Lina Zhao, X. Zhang, Xiaoling Wu, Xiaoyan Xu, Yulin Liu, Caiyun Ma, Shoushui Wei and 2 more. Journal of Medical Imaging and Health Informatics, 2018

641 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d3cac034de1c3695b621d475ba9c6f9f5c1893d3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Nils Strodthoff
2. E. A. Perez Alday
3. Patrick Wagner
4. A. Gu
5. Tobias Schaeffter
6. Zhibo Zhang
7. A. Shah
8. C. Robichaux

Citations in the corpus

• Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020 (2020, background): “The first source is the China Physiological Signal Challenge 2018 (CPSC2018), held during the 7th International Conference on Biomedical Engineering and Biotechnology in Nanjing, China [8].”
• Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research (2022, methodology): “To date, it contains 2,480 benign and 5,429 malignant samples [129] CPSC2018 2018 204 One normal ECG type and eight abnormal ECG types are part of the data utilized in dataset CPSC2018.”
• Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL (2020, methodology): “3: Summary of the ICBEB2018 dataset [39] in terms of ECG statements.”