Classification of Poincaré plots for temporal series of heart rate variability by using machine learning techniques

This article presents two classifiers based on machine learning methods, aiming to detect physiologic anomalies considering Poincare´ plots of heart rate variability. It was developed a preprocessing procedure to encoding the plots, based on the Cellular Features Extraction Method. Simulation of different classifiers, artificial neural networks and support vector machine, has been performed and the performance achieved was about 94%. The study shows attractive, once can be extended for other kind of graphics that represents patterns known in the health field.