Anticipating Atrial Fibrillation Signal Using Efficient Algorithm


  • Mohand Lokman Ahmad Al-dabag Northern Technical University
  • Haider Th. Salim ALRikabi Wasit University
  • Raid Rafi Omar Al-Nima Northern Technical University



ECG signal, Support Vector Machine, Multilayer Perceptron, Atrial Fibrillation, Cross-Correlation


One of the common types of arrhythmia is Atrial Fibrillation (AF), it may cause death to patients. Correct diagnosing of heart problem through examining the Electrocardiogram (ECG) signal will lead to prescribe the right treatment for a patient. This study proposes a system that distinguishes between the normal and AF ECG signals. First, this work provides a novel algorithm for segmenting the ECG signal for extracting a single heartbeat. The algorithm utilizes low computational cost techniques to segment the ECG signal. Then, useful pre-processing and feature extraction methods are suggested. Two classifiers, Support Vector Machine (SVM) and Multilayer Perceptron (MLP), are separately used to evaluate the two proposed algorithms. The performance of the last proposed method with the two classifiers (SVM and MLP) show an improvement of about (19% and 17%, respectively) after using the proposed segmentation method so it became 96.2% and 97.5%, respectively.

Author Biographies

Haider Th. Salim ALRikabi, Wasit University

Electrical Engineering Department, College of Engineering

Raid Rafi Omar Al-Nima, Northern Technical University

Department of Medical Instrumentation Technology EngineeringAnticipating Atrial Fibrillation Signal Using Efficient Algorithm




How to Cite

Al-dabag, M. L. A., Salim ALRikabi, H. T., & Al-Nima, R. R. O. (2021). Anticipating Atrial Fibrillation Signal Using Efficient Algorithm. International Journal of Online and Biomedical Engineering (iJOE), 17(02), pp. 106–120.