Atrial Fibrillation Detection using the Stars 2D Convolutional Neural Network

Abstract

Atrial Fibrillation is one of the riskiest potentials of heart failure in the recent escalating prevalence of cardiovascular diseases. Detecting such an irregularly irregular heart rhythm is a paramount challenge in modern biomedical computation. This research contributes to its resolution by introducing an advanced 2D Convolutional Neural Network model trained on the Poincare plot of the differences between consecutive instantaneous heart rates instead of beat-to-beat time intervals. Furthermore, we used the Gaussian Blur technique to enhance the model’s capacity to generalize and augment its accuracy by forming a star-like plot inspired by Van Gogh’s famous Starry Nights canvas. Model development uses different benchmark datasets to train and test the model. Applying the standard Machine Learning window label comparison method, our model achieves an impressive F1 Score of 96.34% and a remarkable F1 Score of 94.85% when evaluated by the duration-based assessment method.