CODE dataset

Dataset with annotated 12-lead ECG records. The exams were taken in 811 counties in the state of Minas Gerais/Brazil by the Telehealth Network of Minas Gerais (TNMG) between 2010 and 2016. And organized by the CODE (Clinical outcomes in digital electrocardiography) group.<br><b>Requesting access</b><br>Researchers affiliated to educational or research institutions might make requests to access this data dataset. Requests will be analyzed on an individual basis and should contain: Name of PI and host organisation; Contact details (including your name and email); and, the scientific purpose of data access request.<br>If approved, a data user agreement will be forwarded to the researcher that made the request (through the email that was provided). After the agreement has been signed (by the researcher or by the research institution) access to the dataset will be granted.<br><b>Openly available subset:</b><br>A subset of this dataset (with 15% of the patients) is openly available. See: "CODE-15%: a large scale annotated dataset of 12-lead ECGs" https://doi.org/10.5281/zenodo.4916206.<br><b>Content</b><br>The folder contains: A column separated file containing basic patient attributes. The ECG waveforms in the wfdb format.<br><b>Additional references</b><br>The dataset is described in the paper "Automatic diagnosis of the 12-lead ECG using a deep neural network". https://www.nature.com/articles/s41467-020-15432-4. Related publications also using this dataset are:<br>- [1] G. Paixao et al., “Validation of a Deep Neural Network Electrocardiographic-Age as a Mortality Predictor: The CODE Study,” Circulation, vol. 142, no. Suppl_3, pp. A16883–A16883, Nov. 2020, doi: 10.1161/circ.142.suppl_3.16883.- [2] A. L. P. Ribeiro et al., “Tele-electrocardiography and bigdata: The CODE (Clinical Outcomes in Digital Electrocardiography) study,” Journal of Electrocardiology, Sep. 2019, doi: 10/gf7pwg.- [3] D. M. Oliveira, A. H. Ribeiro, J. A. O. Pedrosa, G. M. M. Paixao, A. L. P. Ribeiro, and W. Meira Jr, “Explaining end-to-end ECG automated diagnosis using contextual features,” in Machine Learning and Knowledge Discovery in Databases. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Ghent, Belgium, Sep. 2020, vol. 12461, pp. 204--219. doi: 10.1007/978-3-030-67670-4_13.- [4] D. M. Oliveira, A. H. Ribeiro, J. A. O. Pedrosa, G. M. M. Paixao, A. L. Ribeiro, and W. M. Jr, “Explaining black-box automated electrocardiogram classification to cardiologists,” in 2020 Computing in Cardiology (CinC), 2020, vol. 47. doi: 10.22489/CinC.2020.452.- [5] G. M. M. Paixão et al., “Evaluation of mortality in bundle branch block patients from an electronic cohort: Clinical Outcomes in Digital Electrocardiography (CODE) study,” Journal of Electrocardiology, Sep. 2019, doi: 10/dcgk.- [6] G. M. M. Paixão et al., “Evaluation of Mortality in Atrial Fibrillation: Clinical Outcomes in Digital Electrocardiography (CODE) Study,” Global Heart, vol. 15, no. 1, p. 48, Jul. 2020, doi: 10.5334/gh.772.- [7] G. M. M. Paixão et al., “Electrocardiographic Predictors of Mortality: Data from a Primary Care Tele-Electrocardiography Cohort of Brazilian Patients,” Hearts, vol. 2, no. 4, Art. no. 4, Dec. 2021, doi: 10.3390/hearts2040035.- [8] G. M. Paixão et al., “ECG-AGE FROM ARTIFICIAL INTELLIGENCE: A NEW PREDICTOR FOR MORTALITY? THE CODE (CLINICAL OUTCOMES IN DIGITAL ELECTROCARDIOGRAPHY) STUDY,” Journal of the American College of Cardiology, vol. 75, no. 11 Supplement 1, p. 3672, 2020, doi: 10.1016/S0735-1097(20)34299-6.- [9] E. M. Lima et al., “Deep neural network estimated electrocardiographic-age as a mortality predictor,” Nature Communications, vol. 12, 2021, doi: 10.1038/s41467-021-25351-7.- [10] W. Meira Jr, A. L. P. Ribeiro, D. M. Oliveira, and A. H. Ribeiro, “Contextualized Interpretable Machine Learning for Medical Diagnosis,” Communications of the ACM, 2020, doi: 10.1145/3416965.- [11] A. H. Ribeiro et al., “Automatic diagnosis of the 12-lead ECG using a deep neural network,” Nature Communications, vol. 11, no. 1, p. 1760, 2020, doi: 10/drkd.- [12] A. H. Ribeiro et al., “Automatic Diagnosis of Short-Duration 12-Lead ECG using a Deep Convolutional Network,” Machine Learning for Health (ML4H) Workshop at NeurIPS, 2018.- [13] A. H. Ribeiro et al., “Automatic 12-lead ECG classification using a convolutional network ensemble,” 2020. doi: 10.22489/CinC.2020.130.- [14] V. Sangha et al., “Automated Multilabel Diagnosis on Electrocardiographic Images and Signals,” medRxiv, Sep. 2021, doi: 10.1101/2021.09.22.21263926.- [15] S. Biton et al., “Atrial fibrillation risk prediction from the 12-lead ECG using digital biomarkers and deep representation learning,” European Heart Journal - Digital Health, 2021, doi: 10.1093/ehjdh/ztab071.<br><b>Code:</b><br>The following github repositories perform analysis that use this dataset:<br>- https://github.com/antonior92/automatic-ecg-diagnosis- https://github.com/antonior92/ecg-age-prediction<br><b>Related Datasets:</b><br>- CODE-test: An annotated 12-lead ECG dataset (https://doi.org/10.5281/zenodo.3765780)- CODE-15%: a large scale annotated dataset of 12-lead ECGs (https://doi.org/10.5281/zenodo.4916206)- Sami-Trop: 12-lead ECG traces with age and mortality annotations (https://doi.org/10.5281/zenodo.4905618)<br><b>Ethics declarations</b><br>The CODE Study was approved by the Research Ethics Committee of the Universidade Federal de Minas Gerais, protocol 49368496317.7.0000.5149. <br><br>

本数据集包含标注版12导联心电图（12-lead ECG）记录。相关检查于2010年至2016年间，由巴西米纳斯吉拉斯州远程医疗网络（Telehealth Network of Minas Gerais, TNMG）在该州811个县开展，并由临床数字心电图结局（Clinical Outcomes in Digital Electrocardiography, CODE）研究组整理。<br><b>申请访问</b><br>教育或研究机构附属的研究人员可提交申请以获取本数据集。申请将按个案逐一审核，所需材料包括：首席研究员（Principal Investigator, PI）及其所属机构名称、联系方式（含申请人姓名与电子邮箱）以及数据访问申请的科学用途。<br>若申请获批，将通过申请人提供的电子邮箱发送数据使用协议。在申请人或其所属研究机构签署协议后，即可获得数据集访问权限。<br><b>公开可用子集：</b><br>本数据集包含15%患者的子集已公开可用，详见："CODE-15%: a large scale annotated dataset of 12-lead ECGs"，链接：https://doi.org/10.5281/zenodo.4916206。<br><b>数据集内容</b><br>数据集文件夹包含：存储患者基本属性的列分隔格式文件，以及wfdb格式的心电图波形文件。<br><b>附加参考文献</b><br>本数据集的相关描述发表于论文"Automatic diagnosis of the 12-lead ECG using a deep neural network"，链接：https://www.nature.com/articles/s41467-020-15432-4。其他使用本数据集的相关发表成果包括：<br>- [1] G. Paixão等，《验证基于深度神经网络的心电图年龄作为死亡预测因子：CODE研究》，《循环》（Circulation），第142卷，增刊3，第A16883–A16883页，2020年11月，DOI: 10.1161/circ.142.suppl_3.16883。<br>- [2] A. L. P. Ribeiro等，《远程心电图与大数据：CODE（临床数字心电图结局）研究》，《心电学杂志》（Journal of Electrocardiology），2019年9月，DOI: 10/gf7pwg。<br>- [3] D. M. Oliveira等，《基于上下文特征的端到端心电图自动诊断可解释性研究》，收录于《机器学习与数据库中的知识发现：欧洲机器学习与数据库知识发现原理与实践会议（ECML-PKDD）》，比利时根特，2020年9月，第12461卷，第204–219页，DOI: 10.1007/978-3-030-67670-4_13。<br>- [4] D. M. Oliveira等，《向心脏病学家解释黑箱式心电图自动分类模型》，收录于2020年计算与心脏病学会议（CinC），2020年，第47卷，DOI: 10.22489/CinC.2020.452。<br>- [5] G. M. M. Paixão等，《电子队列中束支传导阻滞患者的死亡率评估：CODE研究》，《心电学杂志》（Journal of Electrocardiology），2019年9月，DOI: 10/dcgk。<br>- [6] G. M. M. Paixão等，《心房颤动患者的死亡率评估：CODE（临床数字心电图结局）研究》，《全球心脏》（Global Heart），第15卷第1期，第48页，2020年7月，DOI: 10.5334/gh.772。<br>- [7] G. M. M. Paixão等，《心电图死亡预测因子：巴西基层医疗远程心电图队列数据》，《Hearts》，第2卷第4期，第4篇文章，2021年12月，DOI: 10.3390/hearts2040035。<br>- [8] G. M. Paixão等，《人工智能心电图年龄：一种新的死亡预测因子？CODE研究》，《美国心脏病学会杂志》（Journal of the American College of Cardiology），第75卷第11期增刊1，第3672页，2020年，DOI: 10.1016/S0735-1097(20)34299-6。<br>- [9] E. M. Lima等，《基于深度神经网络的心电图年龄作为死亡预测因子》，《自然-通讯》（Nature Communications），第12卷，2021年，DOI: 10.1038/s41467-021-25351-7。<br>- [10] W. Meira Jr等，《面向医疗诊断的上下文可解释机器学习》，《ACM通讯》（Communications of the ACM），2020年，DOI: 10.1145/3416965。<br>- [11] A. H. Ribeiro等，《基于深度神经网络的12导联心电图自动诊断》，《自然-通讯》（Nature Communications），第11卷第1期，第1760页，2020年，DOI: 10/drkd。<br>- [12] A. H. Ribeiro等，《基于深度卷积网络的短时12导联心电图自动诊断》，NeurIPS机器学习与健康（ML4H）研讨会，2018年。<br>- [13] A. H. Ribeiro等，《基于卷积网络集成的12导联心电图自动分类》，2020年，DOI: 10.22489/CinC.2020.130。<br>- [14] V. Sangha等，《心电图图像与信号的多标签自动诊断》，medRxiv，2021年9月，DOI: 10.1101/2021.09.22.21263926。<br>- [15] S. Biton等，《基于数字生物标志物与深度表征学习的12导联心电图房颤风险预测》，《欧洲心脏杂志-数字健康》（European Heart Journal - Digital Health），2021年，DOI: 10.1093/ehjdh/ztab071。<br><b>相关代码仓库</b><br>以下GitHub仓库使用本数据集开展分析工作：<br>- https://github.com/antonior92/automatic-ecg-diagnosis<br>- https://github.com/antonior92/ecg-age-prediction<br><b>相关数据集</b><br>- CODE-test：标注版12导联心电图数据集，链接：https://doi.org/10.5281/zenodo.3765780<br>- CODE-15%：大规模标注12导联心电图数据集，链接：https://doi.org/10.5281/zenodo.4916206<br>- Sami-Trop：带有年龄与死亡结局标注的12导联心电图轨迹数据集，链接：https://doi.org/10.5281/zenodo.4905618<br><b>伦理声明</b><br>本CODE研究已通过米纳斯吉拉斯联邦大学（Universidade Federal de Minas Gerais）研究伦理委员会审批，审批编号：49368496317.7.0000.5149。