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Atrial fibrillation (AF), the most common arrhythmia of the heart, affects people worldwide. Common heart arrhythmia AF is difficult to treat and frequently remains undiagnosed. For patient care, early and accurate categorization and detection of AF are critical. Advances in deep learning are drawing more attention to the detection and categorization of atrial fibrillation. This assessment analyzes the literature on the identification and classification of AF using deep learning. The subject matter includes model structures, preprocessing, performance assessment, dataset properties, and future directions in research. The survey discusses the challenges and opportunities associated with deep learning's potential to improve AF diagnosis. This study assesses AF monitoring based on electrocardiogram (ECG) and RR intervals. A computerized approach to managing AF may reduce patient death. The main motivation for developing a service is to use more data to enhance patient outcomes. We offer automated techniques for detecting AF using ECG and RR interval signals and literature analysis. By utilizing one or more of these detection techniques, a cardiovascular disease monitoring service could identify atrial fibrillation and track episodes around the clock. Automated, real-time AF monitoring is necessary to meet public health objectives to prevent AF death. There are technological and legal obstacles to any proposed system. A strong scientific monitoring foundation is necessary to provide effective care to patients and healthcare professionals.
Pawar et al. (Sat,) studied this question.