Neural Network Architectures Comparison for Atrial Fibrillation Detection

Atrial fibrillation (AF) is the most common cardiac arrhythmia affecting about 50,000 new people each year in Latin America. AF is characterized by irregular and rapid heartbeats that can lead to serious complications, such as stroke, heart failure, and all-cause mortality. Traditional methods for AF detection are time consuming and can be prone to human error. Therefore, this work reports the results from two methods using machine learning techniques to assist the diagnosis of AF through 2 hybrid models of neural networks: The ID- CNN with BILSTM model and the MobileNetV2 with BILSTM model which reached 81 and 75% accuracy respectively.