A CNN System for Automatic Classification of ECG Signal Abnormalities


  • Nor Misha Safia Mohd Ariff University Tun Hussein Onn Malaysia
  • Audrey Huong Universiti Tun Hussein Onn Malaysia


Convolutional Neural Network, Electrocardiography


The objective of this research is to build a CNN system that can classify ECG data automatically. The creation of a CNN system for automatic classification of ECG signal abnormalities, which automatically identifies and classifies ECG signal abnormalities utilising, is one effort to build a medical technology concept. CNN is highly sought after to reduce these ECG classification mistakes that happen in this medical field. The medical expert uses a CNN classification system from Matlab to aid in the diagnosis of a heart issue. The development of the project must be monitored closely to make sure everything runs smoothly and according to plan. As a result, the objective of this research is to automatically categorise and describe abnormalities of the ECG signal patterns. The outcome demonstrates the reliability of the findings and information that may be utilised to classify and identify ECG abnormalities.




How to Cite

Mohd Ariff, N. M. S., & Huong, A. (2022). A CNN System for Automatic Classification of ECG Signal Abnormalities. Evolution in Electrical and Electronic Engineering, 3(2), 720–727. Retrieved from https://publisher.uthm.edu.my/periodicals/index.php/eeee/article/view/8860



Biomedical Engineering