A Survey of Emerging Applications of Machine Learning in the Diagnosis and Management of Sleep Hygiene and Health in the Elderly Population

Abstract

Sleep hygiene is essential for maintaining a healthy lifestyle. Insomnia is the inability to sleep or to stay asleep at night. While several factors such as medication, stress, anxiety and work cycle may affect sleep patterns, it is a fact that aging also becomes a factor of disturbed sleep. Apart from facing the societal, physical and psychological challenges that arrive with aging, seniors also have to face the challenges posed by sleep inadequacy. Hence, timely assessment and self-management of sleep are essential. This chapter introduces the etiology of sleep and sleep disorders to introduce the readers to the rhythmic nature of sleep and the complexity involved in its testing. The chapter further describes the significant sleep disorders found in aging population and their diagnostic techniques. The chapter then presents a survey of the significant and emerging applications of machine learning algorithms to objectively assess the sleep through signals acquired from conventional equipments and sophisticated equipments with reduced complexity. Further, the recent technological advances in non-contact and unobtrusive monitoring of sleep are presented. The chapter finally proposes the need for smart bedding solutions and controlled environment for inducing sleep hygiene.