Hybrid Bayesian Network in Neural Network based Deep Learning Framework for Detection of Obstructive Sleep Apnea Syndrome

Abstract

This study aimed to develop Bayesian Network model integrated with Deep Learning to help doctors diagnose Obstructive Sleep Apnoea Syndrome (OSAS) more holistically and clearly. The results of this research will produce a useful and beneficial clinical workflow for future support in health care. The model will be developed based on the methods of analysis and the quantitative data used to compromise the developing of Hybrid Bayesian Network in Neural Network using Deep Learning Algorithm. The aim of this study was to apply a hybrid model of convolutional neural network (CNN) that could be used during sleep consultation to determine the need for electrocardiography (ECG) signals stimuli for Polysomnography (PSG).