Relevance of sleep for wellness: New trends in using artificial intelligence and machine learning

Abstract

Sleep and well-being have been intricately linked, and sleep hygiene is paramount for developing mental well-being and resilience. Although widespread, sleep disorders require elaborate polysomnography laboratory and patient-stay with sleep in unfamiliar environments. Current technologies have allowed various devices to diagnose sleep disorders at home. However, these devices are in various validation stages, with many already receiving approvals from competent authorities. This has captured vast patient-related physiologic data for advanced analytics using artificial intelligence through machine and deep learning applications. This is expected to be integrated with patients’ Electronic Health Records and provide individualized prescriptive therapy for sleep disorders in the future.

Keywords: Sleep initiation and maintenance disorders, Sleep apnea, Obstructive, Machine learning, Artificial intelligence, Algorithms

Core Tip: Quality sleep is one of the major determinants of wellness. Insomnia and other sleep disorders are widespread in the society. Increasingly, technology is being used to diagnose sleep disorders through wearable devices and consumer technologies. This has allowed sleep disorders to be diagnosed at home rather than at polysomnography labs. With the advent of artificial intelligence, including machine and deep learning, sleep disorder diagnosis has become highly dynamic based on multiple inputs and complex algorithms analyzing huge quantum of metadata. Similarly, therapy is also becoming highly patient-specific due to available digital tools. However, the ever-expanding knowledge needs further validation to establish patient-centric care.