SLEEPING ABNORMALITIES DETECTION USING DEEP LEARING TECHNIQUE
Mr.K.SATHEESHKUMAR MCA, M.Phil,
SANTHOSH S, ANANTHANAYAKI S, SWATHIPRIYA K
Department of Computer Science and Engineering
University College of Engineering, Thirukkuvalai
(A constituent College Of Anna University::Chennai and Approved by AICTE, New Delhi)
-------------------------------------------------------------------------------------------------------------------
ABSTRACT
Maintaining proper health and mental stability is critical for overall health and wellbeing. Despite a good deal of research investment, sleep quality continues to be a crucial public challenge. Nowadays, people of all age groups are affected by improper sleep quality. Poor sleep can lead to a variety of neurological disorders. Sleep disorders are common in all subsets of the population, independently of gender. This public health challenge greatly affects quality of life in terms of both physical and mental health. Insomnia, parasomnias, sleep-related breathing difficulties, hypersomnia, bruxism, narcolepsy, and circadian rhythm disorders are some common examples of sleep-related disorders. Some of these disorders can be treated with proper analysis of early symptoms; in such cases, adequate sleep quality is essential for the patient’s recovery. Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleep disorders, and population health. While still in its nascent stage, there are several challenges which preclude AI’s generalizability and wide-reaching clinical applications. Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms and deep learning algorithm prior to its inclusion in the sleep clinic.
KEYWORDS: Artificial intelligence, Insomnia, Machine learning, Deep learning, Sleeping stage
