Research on Deep Neural Networks for Electrocardiogram Classification and Automatic Diagnosis of Arrhythmia
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
- deep neural network;
- electrocardiogram (ECG)
- cardiac arrhythmias
- deep learning
|Journal Name||Impact Factor||CiteScore||Launched Year||First Decision (median)||APC|
|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 Imagingjimaging
|3.2||4.4||2015||21.9 Days||CHF 1600||Submit|
|2.4||3.5||2013||17.7 Days||CHF 2600||Submit|
Preprints is a platform dedicated to making early versions of research outputs permanently available and citable. MDPI journals allow posting on preprint servers such as Preprints.org prior to publication. For more details about reprints, please visit https://www.preprints.org.