Deep Learning for ECG Signal Classification in Remote Healthcare Applications

Hashim S. A., BALIK H. H.

2nd International Conference on Advanced Engineering, Technology and Applications, ICAETA 2023, İstanbul, Turkey, 10 - 11 March 2023, vol.1983 CCIS, pp.254-267 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 1983 CCIS
  • Doi Number: 10.1007/978-3-031-50920-9_20
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.254-267
  • Keywords: classification, CNN, ECG, heart arrhythmias
  • Yıldız Technical University Affiliated: Yes


Due to several current medical applications, the significance of Electrocardiogram (ECG) classification has increased significantly. To evaluate and classify ECG data, a variety of machine learning methods are now available. Utilizing deep learning architectures, where the top layers operate as feature extractors and the bottom layers are completely coupled, is one of the solutions that has been suggested. In addition to classification results, this work also proposes a learning architecture for ECG classification utilizing 1D convolutional layers and Fully Convolution Network (FCN) layers. We made several changes to get the best result, getting 98% accuracy and 0.2% loss. A comparison has been made and showed that our work is better than other related work. The problem that we found in the rest of the research is the use of less efficient algorithms, so this thing is the reason for the lack of accuracy of the results and an increase in the loss. We used the most efficient algorithm for this work.