Topic Editors

Dr. Vidya K. Sudarshan
School of Computer Science and Engineering, Nanyang Technological University, NTU, Singapore 639798, Singapore
Dr. Ru San Tan
Department of Cardiology, National Heart Centre Singapore and Sing Health Duke-NUS Cardiovascular Sciences Academic Clinical Programme, Singapore, Singapore

Research on Deep Neural Networks for Electrocardiogram Classification and Automatic Diagnosis of Arrhythmia

Abstract submission deadline
closed (31 October 2023)
Manuscript submission deadline
31 December 2023
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Topic Information

Dear Colleagues,

Electrocardiograms (ECGs) are non-invasive physiological signals used to assess and monitor cardiac health. Generally, interpretation of ECG waveform to capture the presence of abnormalities or diseases depends exclusively on the experience of clinicians. However, in the past decade, advanced Artificial Intelligence (AI) and Deep Learning (DL) technologies have been gaining importance, and clinicians have been attempting to adopt them in ECG assessment tasks with the intention of obtaining faster results. In the coming years, embedding intelligence in machines and the automation of ECG processes is clearly the preferred approach. Thus, in this Topic on “Research on Deep Neural Networks for Electrocardiogram Classification and Automatic Diagnosis of Arrhythmia”, we seek to understand DL technology and how it can be leveraged to augment human capabilities and potential, for ECG interpretation and diagnosis of different cardiac arrhythmias. Specifically, we invite papers that recognize the potential of DL neural networks in ECG assessment for possible publication in one of five journals: Applied Sciences, Computers, Information, Journal of Imaging, and Mathematics. A possible application area is the design of advanced deep neural-network-based algorithms for ECG signal assessment to detect different types of arrhythmias.

Dr. Vidya K. Sudarshan
Dr. Ru San Tan
Topic Editors


  • deep neural network;
  • electrocardiogram (ECG)
  • cardiac arrhythmias
  • arrhythmia
  • deep learning

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
2.7 4.5 2011 15.8 Days CHF 2300 Submit
2.8 4.7 2012 17.9 Days CHF 1600 Submit
3.1 5.8 2010 22.6 Days CHF 1600 Submit
Journal of Imaging
3.2 4.4 2015 21.9 Days CHF 1600 Submit
2.4 3.5 2013 17.7 Days CHF 2600 Submit

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Published Papers

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