Why the study?
Atrial fibrillation is a common arrhythmia that can cause death, and correct ECG diagnosis is needed to prescribe appropriate treatment.
A novel low-computational-cost ECG segmentation algorithm significantly improves the accuracy of machine learning classifiers in detecting atrial fibrillation.
May aid low-cost AF screening on ECG; leaves open prospective validation before clinical use.
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.
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Al-Dabag et al. (2021) studied this question.