What is it about?

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.

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Why is it important?

Despite the success of insomnia work on the detection and genes, there remain sufficient gaps that necessitate to be comprehensive to reach the maturity stage as related with other sleep disorder research. This section helps to identify interconnected but possible areas of study that may build future prospects. This work offers numerous useful features to consider in the plan of study on insomnia detection. Some limitations of this study are, that we used two databases such as PubMed and WoS databases, because these databases are authentic than others. In a further study, we will use Scopus and other databases to find the exact closest terms for the research of insomnia detection.

Perspectives

To create a complete database for insomnia detection using physiological signals for the deep research on this field. To develop software for the monitoring of insomnia based on big data techniques. To find closer genes related to insomnia for the development of biomarkers and drug design. To use quantum computing, block chain, and internet of things (IoT) for the early prediction of insomnia. To develop smart house using AI for better sleep.

Dr. Md Belal Bin Heyat
Westlake University

Read the Original

This page is a summary of: Progress in Detection of Insomnia Sleep Disorder: A Comprehensive Review, Current Drug Targets, October 2020, Bentham Science Publishers,
DOI: 10.2174/1389450121666201027125828.
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