Progress in Detection of Insomnia Sleep Disorder: A Comprehensive Review

Md Belal Bin Heyat; Faijan Akhtar; M A Ansari; Asif Khan; Fahed Alkahtani; Haroon Khan; Dakun Lai

doi:10.2174/1389450121666201027125828

Progress in Detection of Insomnia Sleep Disorder: A Comprehensive Review

Curr Drug Targets. 2021;22(6):672-684. doi: 10.2174/1389450121666201027125828.

Authors

Md Belal Bin Heyat¹, Faijan Akhtar², M A Ansari³, Asif Khan², Fahed Alkahtani⁴, Haroon Khan⁵, Dakun Lai¹

Affiliations

¹ School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China.
² School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China.
³ Department of Electrical Engineering, Gautam Buddha Technical University, Gr. Noida, UP 201312, India.
⁴ Department of Electrical Engineering, Najran University, Najran 1988, Saudi Arabia.
⁵ Department of Pharmacy, Abdul Wali Khan University, Mardan, KPK 23200, Pakistan.

PMID: 33109045
DOI: 10.2174/1389450121666201027125828

Abstract

Lack of adequate sleep is a major source of many harmful diseases related to heart, brain, psychological changes, high blood pressure, diabetes, weight gain, etc. 40 to 50% of the world's population is suffering from poor or inadequate sleep. Insomnia is a sleep disorder in which an individual complaint of difficulties in starting/continuing sleep at least four weeks regularly. It is estimated that 70% of heart diseases are generated during insomnia sleep disorder. The main objective of this study is to determine all work conducted on insomnia detection and to make a database. We used two procedures including network visualization techniques on two databases including PubMed and Web of Science to complete this study. We found 169 and 36 previous publications of insomnia detection in the PubMed and the Web of Science databases, respectively. We analyzed 10 datasets, 2 databases, 21 genes, and 23 publications with 30105 subjects of insomnia detection. This work has revealed the future way and gap so far directed on insomnia detection and has also tried to provide objectives for the future work to be proficient in a scientific and significant manner.

Keywords: Insomnia; detection; diagnostic; machine learning; network visualization; prognostic; pubmed; sleep; sleep disorder; web of science.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

Humans
Sleep
Sleep Initiation and Maintenance Disorders* / diagnosis
Sleep Wake Disorders* / diagnosis