Abstract
Sleep is one of the most basic physiological process essential to physical recovery, cognitive functioning and emotional wellbeing. Throughout this paper, sleep mechanisms and their biological significance e.g., energy restoration, tissue recovery, and neural detoxification are addressed. It explores the role of circadian rhythms, the firing of neurons and the hormone signals of the brain that help maintain the sleep-wake cycle and how disruption in them can throw the sleep-wake cycle off balance, which can result in sleep disorders and other issues. One of the basic methods of sleep research is the Electroencephalogram (EEG) method in which the electrical activity of the brain is recorded at various sleep stages. The paper reviews advancements in improving EEG-based methods for classifying sleep stages and detecting conditions such as epilepsy, Alzheimer’s disease and rapid-eye movement (REM) abnormalities. Recent developments in machine learning and signal processing tools such as time-frequency analysis and neural networks improved the accuracy of diagnosis and prediction of sleep disorders. However, the current contribution is an extensive review of experimental literature and demonstrate thopacuity of combining EEG features with modern computational techniques to provide a better grasp of the microstructure of sleep states and disorders. They facilitate superior automated systems for diagnosis that enhance patient outcomes, and translate to broader applications in health care.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call