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>

带注释的12导联心电图（Electrocardiogram，ECG）记录数据集。这些检查由米纳斯吉拉斯州远程医疗网络（Telehealth Network of Minas Gerais，TNMG）于2010年至2016年间在巴西米纳斯吉拉斯州的811个县完成，并由CODE（数字心电图临床结局研究组，Clinical Outcomes in Digital Electrocardiography）组织整理。<br><b>访问申请</b><br>隶属于教育或研究机构的研究人员可申请访问本数据集。申请将逐一审核，需包含以下内容：首席研究员（Principal Investigator，PI）姓名及所在机构；联系方式（含申请人姓名及邮箱）；以及数据访问的科学目的。若申请获批，数据使用协议将发送至申请人（通过所提供的邮箱）。协议经申请人或其研究机构签署后，将授予数据集访问权限。<br><b>公开可用子集</b><br>本数据集的一个子集（含15%的患者数据）可公开获取。详见：《CODE-15%：大规模带注释12导联心电图数据集》https://doi.org/10.5281/zenodo.4916206。<br><b>内容</b><br>文件夹包含：一个列分隔文件（含患者基本属性）；wfdb格式的心电图波形文件。<br><b>补充参考文献</b><br>本数据集详见论文《基于深度神经网络的12导联心电图自动诊断》（https://www.nature.com/articles/s41467-020-15432-4）。使用本数据集的相关研究如下：<br>- [1] G. Paixao等，《深度学习网络心电图年龄作为死亡预测因子的验证：CODE研究》，《循环》，第142卷，增刊3，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（数字心电图临床结局）研究》，《心电学期刊》，2019年9月，doi:10/gf7pwg.<br>- [3] D. M. Oliveira等，《基于上下文特征解释端到端心电图自动诊断》，《机器学习与数据库知识发现：欧洲机器学习与知识发现原理与实践会议（ECML-PKDD）》，比利时根特，2020年9月，卷12461，204-219页，doi:10.1007/978-3-030-67670-4_13.<br>- [4] D. M. Oliveira等，《向 cardiologists 解释黑箱心电图自动分类模型》，《2020年心血管计算会议（CinC）》，2020年，卷47，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风险评估：CODE（数字心电图临床结局）研究》，《心电学期刊》，2019年9月，doi:10/dcgk.<br>- [6] G. M. M. Paixão等，《心房颤动患者的死亡风险评估：CODE（数字心电图临床结局）研究》，《全球心脏》，第15卷第1期，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：巴西基层远程心电图队列数据》，《心脏杂志》，第2卷第4期，第4篇，2021年12月，doi:10.3390/hearts2040035.<br>- [8] G. M. M. Paixão等，《人工智能心电图年龄：新型死亡预测因子？CODE（数字心电图临床结局）研究》，《美国心脏病学会杂志》，第75卷第11期增刊1，3672页，2020年，doi:10.1016/S0735-1097(20)34299-6.<br>- [9] E. M. Lima等，《深度神经网络估算的心电图年龄作为死亡预测因子》，《自然通讯》，第12卷，2021年，doi:10.1038/s41467-021-25351-7.<br>- [10] W. Meira Jr等，《医疗诊断的情境化可解释机器学习》，《ACM通讯》，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》，第11卷第1期，1760页，2020年，doi:10/drkd.<br>- [12] A. H. Ribeiro等，《基于深度卷积网络的短时长12导联心电图自动诊断》，《神经信息处理系统大会（NeurIPS）医疗机器学习研讨会（ML4H）》，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自动多标签诊断》，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导联心电图心房颤动风险预测》，《欧洲心脏杂志-数字健康》，2021年，doi:10.1093/ehjdh/ztab071.<br><b>代码资源</b><br>以下GitHub仓库包含使用本数据集的分析代码：<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：带注释的12导联心电图数据集（https://doi.org/10.5281/zenodo.3765780）<br>- CODE-15%：大规模带注释12导联心电图数据集（https://doi.org/10.5281/zenodo.4916206）<br>- Sami-Trop：含年龄及死亡注释的12导联心电图轨迹数据集（https://doi.org/10.5281/zenodo.4905618）<br><b>伦理声明</b><br>CODE研究已获米纳斯吉拉斯联邦大学生物伦理委员会批准，协议编号49368496317.7.0000.5149。