Contribution of neural networks in the diagnosis and treatment of cardiac arrhythmia.

Journal: Discovery medicine
Published Date: Jan 1, 2020

Abstract

Arrhythmia is a dangerous disease in which the heart rhythm varies and it may be very fast or very slow. Rapid heartbeats can lead to shortness of breath, chest pain, and sudden weakness, whereas slow heartbeats can lead to dizziness, problems with concentration, and constant stress. Finding an effective treatment for arrhythmia has become a very important endeavor for researchers and clinicians. In this article, we review the latest methodologies used in arrhythmia diagnosis and treatment. They include the application of five different types of artificial neural networks trained by machine learning and powered by artificial intelligence: convolutional, recurrent, feedforward, radial basis function, and modular neural network. Some of these methodologies are merged to enhance accuracy and efficacy. This review suggests that more research needs to be carried out in merging neural network types for their application in electrocardiogram (ECG).

Authors

  • Mohamed Abbas
    Electrical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia.
  • Mohammed Alqahtani
    Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia.
  • Saad F Al-Gahtani
    Electrical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia.
  • Ali Algahtani
    Mechanical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia.
  • Amir Kessentini
    Mechanical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia.
  • Hassen Loukil
    Electrical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia.
  • Muneer Parayangat
    Electrical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia.
  • Thafasal Ijyas
    Electrical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia.
  • Abdul Wase Mohammed
    Electrical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia.

Keywords

External Resources

Popular Topics
Recent Journals