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

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

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.7	4.5	2011	16.9 Days	CHF 2400
Computers computers	2.8	4.7	2012	17.7 Days	CHF 1800
Information information	3.1	5.8	2010	18 Days	CHF 1600
Journal of Imaging jimaging	3.2	4.4	2015	21.7 Days	CHF 1800
Mathematics mathematics	2.4	3.5	2013	16.9 Days	CHF 2600

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Published Papers