Khan Ali Haider, Hussain Muzammil, Malik Muhammad Kamran
Department of Computer Science, School of System & Technology, University of Management and Technology Lahore, Pakistan.
Department of Computer Science, University of the Punjab Lahore, Pakistan.
Data Brief. 2021 Jan 18;34:106762. doi: 10.1016/j.dib.2021.106762. eCollection 2021 Feb.
The study contains the dataset of ECG images of Cardiac and COVID-19 patients. This rare dataset contains 1937 distinct patient records, data is collected using ECG Device 'EDAN SERIES-3' installed in Cardiac Care and Isolation Units of different health care institutes across Pakistan. The collected ECG images data were manually reviewed by medical professors using Telehealth ECG diagnostic system, under the supervision of senior medical professionals with experience in ECG interpretation. The manual reviewing process of ECG images took several months to review the five distinct categories (COVID-19, Abnormal Heartbeat, Myocardial Infarction (MI), Previous History of MI, and Normal Person). The collected data contains 12 leads-based ECG images dataset can be used by Data Scientist, IT Professional and Medical Research Institutes to design, compare, fine-tune classical techniques and Deep learning methods in studies focused on COVID-19, Arrhythmia, and other cardiovascular conditions. The dataset contains rare categories of patients that may be used for the development of automatic diagnosis tool for healthcare institutes.
该研究包含心脏疾病患者和新冠肺炎患者的心电图图像数据集。这个罕见的数据集包含1937个不同的患者记录,数据是使用安装在巴基斯坦各地不同医疗机构的心脏护理和隔离病房中的“迈瑞系列-3”心电图设备收集的。收集到的心电图图像数据由医学教授在具有心电图解读经验的高级医学专业人员的监督下,使用远程医疗心电图诊断系统进行人工审核。心电图图像的人工审核过程花了几个月时间来审核五个不同类别(新冠肺炎、心跳异常、心肌梗死、心肌梗死病史、正常人)。收集到的数据包含基于12导联的心电图图像数据集,数据科学家、信息技术专业人员和医学研究机构可以使用该数据集在专注于新冠肺炎、心律失常和其他心血管疾病的研究中设计、比较、微调经典技术和深度学习方法。该数据集包含可能用于为医疗机构开发自动诊断工具的罕见患者类别。