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ECG Images dataset of Cardiac and COVID-19 Patients.
Khan, Ali Haider; Hussain, Muzammil; Malik, Muhammad Kamran.
  • Khan AH; Department of Computer Science, School of System & Technology, University of Management and Technology Lahore, Pakistan.
  • Hussain M; Department of Computer Science, School of System & Technology, University of Management and Technology Lahore, Pakistan.
  • Malik MK; Department of Computer Science, University of the Punjab Lahore, Pakistan.
Data Brief ; 34: 106762, 2021 Feb.
Article in English | MEDLINE | ID: covidwho-1263247
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Data Brief Year: 2021 Document Type: Article Affiliation country: J.dib.2021.106762

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Data Brief Year: 2021 Document Type: Article Affiliation country: J.dib.2021.106762