eprintid: 11263
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/12/63
datestamp: 2023-11-10 03:25:47
lastmod: 2023-11-10 03:25:47
status_changed: 2023-11-10 01:14:50
type: article
metadata_visibility: show
creators_name: Farouk, F.N.B.M.
creators_name: Anwar, T.
creators_name: Zakaria, N.B.
title: Hybrid bayesian network in neural network based deep learning framework for detection of obstructive sleep apnea syndrome
ispublished: pub
note: cited By 1
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). © BEIESP.
date: 2019
publisher: Blue Eyes Intelligence Engineering and Sciences Publication
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074574216&doi=10.35940%2fijeat.A2077.109119&partnerID=40&md5=66432646ddaa6767c0fb016014478d12
id_number: 10.35940/ijeat.A2077.109119
full_text_status: none
publication: International Journal of Engineering and Advanced Technology
volume: 9
number: 1
pagerange: 4922-4926
refereed: TRUE
issn: 22498958
citation:   Farouk, F.N.B.M. and Anwar, T. and Zakaria, N.B.  (2019) Hybrid bayesian network in neural network based deep learning framework for detection of obstructive sleep apnea syndrome.  International Journal of Engineering and Advanced Technology, 9 (1).  pp. 4922-4926.  ISSN 22498958