Evidence-based prediction of atrial fibrillation using physiological signals

Abstract

Atrial Fibrillation is an irregularity in heart beats increasing occurrence of heart failure strokes dementia and other serious diseases. The goal of this study is to predict Atrial Fibrillation using physiological signals of patients. Easily measured using non-invasive sensors these signals include Heart Rate Respiratory Rate Peripheral Oxygen Saturation Pulse and Central Venous Pressure. In order to predict the occurrence of Atrial Fibrillation the proposed approach consists of extracting several parameters from signals and then constructing a decision rule that combines evidence based on the belief functions theory. The data used in this study are provided by the MIMIC III medical database. The classifier obtained is able to predict the Atrial Fibrillation with an accuracy of 70.49% a sensitivity of 77.07% and a specificity of 63.9%.