TY - JOUR
T1 - The fusion of Internet of Intelligent Things (IoIT) in remote diagnosis of obstructive Sleep Apnea
T2 - A survey and a new model
AU - Abdel-Basset, Mohamed
AU - Ding, Weiping
AU - Abdel-Fatah, Laila
N1 - Funding Information:
The authors would like to express the sincere appreciation to anonymous reviewers for their insightful comments which greatly improve the quality of this paper. This work was supported in part by the National Natural Science Foundation of China under Grant 61300167 and Grant 61976120 , in part by the Natural Science Foundation of Jiangsu Province under Grant BK20151274 and Grant BK20191445, in part by the Six Talent Peaks Project of Jiangsu Province under Grant XYDXXJS-048, in part by the Jiangsu Provincial Government Scholarship Program under Grant JS-2016–065, and sponsored by Qing Lan Project of Jiangsu Province.
Funding Information:
The authors would like to express the sincere appreciation to anonymous reviewers for their insightful comments which greatly improve the quality of this paper. This work was supported in part by the National Natural Science Foundation of China under Grant 61300167 and Grant 61976120, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20151274 and Grant BK20191445, in part by the Six Talent Peaks Project of Jiangsu Province under Grant XYDXXJS-048, in part by the Jiangsu Provincial Government Scholarship Program under Grant JS-2016?065, and sponsored by Qing Lan Project of Jiangsu Province.
Publisher Copyright:
© 2020
PY - 2020/9
Y1 - 2020/9
N2 - Obstructive Sleep Apnea (OSA) syndrome is one of the most widespread diseases that difficult to be detected and remedied. In particular, the examination of OSA by using the traditional Polysomnography (PSG) is one of formidable complexity as it requires full observation in a laboratory overnight. Meanwhile, the number of available laboratories and beds is minimal comparing to the number of OSA patients. What's more, the unusual environment and restricted mobility of patients may result in deficient diagnosis results. The Internet of Things (IoT) is the most appropriate solution for the previous diagnosis obstacles by allowing doctors to synchronize patient status. Besides, several studies have been introduced to consolidate the performance of IoT interoperability via the fusion with Artificial Intelligence (AI) resulting in the Internet of Intelligent Things (IoIT). This paper presents a literature survey about the intensification of IoT technologies for smart monitoring of sleep quality and OSA diagnosis. Mainly, the most recent enabling IoT and support technologies such as (smart devices, fog computing, cloud, big data, and machine learning) are covered via the discussion of more recent works of literature published from 2016 to 2019. Also, the roles of AI in optimizing the efficiency of OSA smart diagnosis are presented. Besides, a new comprehensive IoIT optimization framework is presented which employing AI for optimizing the performance of intelligent diagnosis of OSA. Finally, the open issues and challenges in this field are argued. This paper is, therefore, a major contributor to the compilation of all IoT innovative and efficient AI methods that improving the quality of OSA diagnosis.
AB - Obstructive Sleep Apnea (OSA) syndrome is one of the most widespread diseases that difficult to be detected and remedied. In particular, the examination of OSA by using the traditional Polysomnography (PSG) is one of formidable complexity as it requires full observation in a laboratory overnight. Meanwhile, the number of available laboratories and beds is minimal comparing to the number of OSA patients. What's more, the unusual environment and restricted mobility of patients may result in deficient diagnosis results. The Internet of Things (IoT) is the most appropriate solution for the previous diagnosis obstacles by allowing doctors to synchronize patient status. Besides, several studies have been introduced to consolidate the performance of IoT interoperability via the fusion with Artificial Intelligence (AI) resulting in the Internet of Intelligent Things (IoIT). This paper presents a literature survey about the intensification of IoT technologies for smart monitoring of sleep quality and OSA diagnosis. Mainly, the most recent enabling IoT and support technologies such as (smart devices, fog computing, cloud, big data, and machine learning) are covered via the discussion of more recent works of literature published from 2016 to 2019. Also, the roles of AI in optimizing the efficiency of OSA smart diagnosis are presented. Besides, a new comprehensive IoIT optimization framework is presented which employing AI for optimizing the performance of intelligent diagnosis of OSA. Finally, the open issues and challenges in this field are argued. This paper is, therefore, a major contributor to the compilation of all IoT innovative and efficient AI methods that improving the quality of OSA diagnosis.
KW - Artificial Intelligence
KW - Internet of Intelligent Things
KW - Internet of Things
KW - Obstructive Sleep Apnea
KW - Optimization
KW - Remote diagnosis
UR - http://www.scopus.com/inward/record.url?scp=85083000042&partnerID=8YFLogxK
U2 - 10.1016/j.inffus.2020.03.010
DO - 10.1016/j.inffus.2020.03.010
M3 - Article
AN - SCOPUS:85083000042
SN - 1566-2535
VL - 61
SP - 84
EP - 100
JO - Information Fusion
JF - Information Fusion
ER -