Tan Jian, Fan Haoyi, Luo Jiawei, Zhou Yanjie, Wang Ning, Wang Xizheng, Liu Guizhi, Liu Chengyu, Wang Zongmin
ZhengZhou University, Zhengzhou, 450001, China.
The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
Sci Data. 2025 May 26;12(1):867. doi: 10.1038/s41597-025-05225-z.
Electrocardiogram (ECG) is a common non-invasive diagnostic tool for cardiovascular diseases. Adequate data is crucial in utilizing deep learning to achieve intelligent diagnosis of ECG. The existing ECG datasets almost only focus on adults and most of them do not provide cardiovascular disease diagnosis. In this study, we propose an ECG database with cardiovascular disease diagnosis for children aged 0-14 years old. This dataset is acquired from 11643 hospitalized children at the First Affiliated Hospital of Zhengzhou University from 2018 to 2024, including 14190 pediatric ECG records, of which 12334 were 12 lead and 1856 were 9 lead. The sampling rate is 500 Hz and the record length is 5-120 seconds. We followed the recommendations of AHA/ACC/HRS and the diagnostic statements in the consensus of Chinese ECG experts to encode and convert all ECG records. In this dataset, 3516 ECG records were diagnosed with cardiovascular diseases, and these labels were derived from 19 common diseases in the pediatric cardiovascular field, including myocarditis, cardiomyopathy, congenital heart disease, and Kawasaki disease.
心电图（ECG）是用于心血管疾病的常见无创诊断工具。充足的数据对于利用深度学习实现心电图智能诊断至关重要。现有的心电图数据集几乎只关注成年人，且大多数不提供心血管疾病诊断。在本研究中，我们提出了一个针对0至14岁儿童的具有心血管疾病诊断功能的心电图数据库。该数据集来自2018年至2024年郑州大学第一附属医院的11643名住院儿童，包括14190份儿科心电图记录，其中12334份为12导联，1856份为9导联。采样率为500Hz，记录长度为5至120秒。我们遵循美国心脏协会（AHA）/美国心脏病学会（ACC）/美国心律学会（HRS）的建议以及中国心电图专家共识中的诊断声明，对所有心电图记录进行编码和转换。在该数据集中，3516份心电图记录被诊断患有心血管疾病，这些标签来自儿科心血管领域的19种常见疾病，包括心肌炎、心肌病、先天性心脏病和川崎病。
