Anticipating Atrial Fibrillation Signal Using Efficient Algorithm

Mohand Lokman Ahmad Al-dabag, Haider Th. Salim ALRikabi, Raid Rafi Omar Al-Nima


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.


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

Full Text:


International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
Creative Commons License
Scopus logo Clarivate Analyatics ESCI logo IET Inspec logo DOAJ logo DBLP logo EBSCO logo Ulrich's logo Google Scholar logo MAS logo