AIoT and Big Data Analytics for Smart Healthcare Applications

A Monitoring System for the Recognition of Sleeping Disorders in Patients with Cognitive Impairment

Author(s): Priya Dev* and Abhishek Pathak

Pp: 67-84 (18)

DOI: 10.2174/9789815196054123050007

* (Excluding Mailing and Handling)


Sleep is one of the most important biological processes acknowledged as a vital determinant of human performance and health. Sleep has been acknowledged to promote healing, restore energy, improve the immune system through interactions, and affect human behaviour and brain functions. To this end, even the transient alteration of sleeping patterns, including severe sleep deprivation, can impair one's cognitive performance and judgment, even as prolonged aberrations have been associated with the development of disease. The existing global sleep trends indicate a decrement in average sleep durations. Owing to such trends and the various implications of sleep on human well-being and health, enhanced characterisation of the sleep attributes indicates a public health priority.

Further, the advancement and use of multi-modal sensors with technologies to monitor physical activity, sleep, and circadian rhythms have increased dramatically in recent years. For the first time, accurate sleep monitoring on a large scale is now possible. However, there is a need to overcome several significant challenges to realise the full potential of these technologies for individuals, medicine, and research. In this chapter, a review of the present levels of the sleep-monitoring technologies in patients with cognitive impairments, in addition to assessing the difficulties and potentials lying ahead, from data gathering through the ultimate execution of findings within the consumer and clinical contexts.. Further, the chapter will review the advantages and disadvantages of the extant and novel sensing technologies, focusing on new datadriven technologies that include Artificial Intelligence. 

Keywords: Advanced sleep phase disorder, Artificial Intelligence, Big data, Circadian rhythms, Cognitive Impairment, Consumer sleep technology, COVID19, Data-driven technologies, Dementia, Deep learning model, Delayed sleep phase disorder, Healthcare, Polysomnography, Psychomotor vigilance test, Sleep, Sleep Deprivation, Sleeping patterns, Sleep-monitoring technologies, Sleep monitoring system, Sleep-Wake Homoeostasis.

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