A Novel Modified Marine Predator Algorithm (MMPA) based Automated Atrial Fibrillation Detection (AAFD) System using ECG Signals
Keywords:
Atrial Fibrillation (AF), Electrocardiogram (ECG), Modified Marine Predator Algorithm (MMPA), Support Vector Machine (SVM), Heart rate variability (HRV), Spectral Features.Abstract
The most common type of arrhythmia that can modify the heart’s rhythms and potentially impact the morphology of ECG tracings is called Atrial Fibrillation (AF). The recent statistical reports indicate that nearly 1% of people around the world are affected by AF. The major goal of this article is to develop a method for automatically detecting AF using transient single lead ECG readings. Heart rate variability (HRV) and frequency analysis are used for feature extraction. This study’s innovative contribution is the use of a Modified Marine Predator Algorithm (MMPA) to identify AF in brief ECG data. After feature selection, the AF classification is performed with the use of Support Vector Machine (SVM) classifier. It segregates the classes into the types of Normal & Arrhythmia (NA), Others & Arrhythmia (OA), Normal & Others (NO), and Normal, Others & Arrhythmia (NOA). The outcomes were verified and compared by using a publicly accessible data set made up of brief ECG recordings generated by the existing, SVM, Genetic Algorithm (GA-SVM), and Modified Moth-Flame (MMF-SVM) techniques. Under noise levels between 0 and 30 dB, classification accuracy for N versus A ranges from 96% to 99%. The maximum accuracy of 99.8% is attained for N versus A versus O. The acquired experimental results indicate that, for relatively brief ECG recordings, HRV is effective and reliable for AF identification.
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