A Convolutional Neural Network model achieved 98.5% classification accuracy for atrial fibrillation detection, outperforming Support Vector Machine and K-Nearest Neighbor algorithms.
Does a Convolutional Neural Network (CNN) model improve the accuracy of atrial fibrillation detection from ECG signals compared to SVM and KNN algorithms?
A Convolutional Neural Network model achieved 98.5% accuracy in detecting atrial fibrillation from ECG signals, outperforming traditional machine learning algorithms like SVM and KNN.
Atrial fibrillation (AF) is a complex arrhythmia linked to a variety of common cardiovascular illnesses and conventional cardiovascular risk factors. Although awareness and improved detection of AF have improved over the last decade as the incidence and prevalence of AF has increased, current trends in using machine learning approaches to diagnose AF are still lacking in precision. To determine the true nature of the Electrocardiography (ECG) signal segments, a Convolutional Neural Network (CNN) model was employed to discover hidden information. Fully Connected (FC) layers were then utilized to categorize the ECG data segments as normal or abnormal. The suggested algorithm's findings were compared to state-of-the-art arrhythmia identification algorithms in the literature for the MIT-BIH ECG database. The methodology proved not only to yield high classification performance (98.5%) but also low processing computational advantage where the CNN was the most accurate algorithm used for atrial fibrillation detection hence. To conclude the findings of the research, a model was prepared to test the accuracy of the most common ML algorithms used for AF detection. After comparing the results of the experiment, it was clear that CNN algorithm is the best approach compared to Support Vector Machine (SVM) and K-Nearest Neighbor (KNN).
Almazrouei et al. (Tue,) conducted a other in Atrial fibrillation. Convolutional Neural Network (CNN) model vs. Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) was evaluated on Classification performance (accuracy). A Convolutional Neural Network model achieved 98.5% classification accuracy for atrial fibrillation detection, outperforming Support Vector Machine and K-Nearest Neighbor algorithms.
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