A Systematic Review of Literature on Automated Sleep Scoring

Sleep is a period of rest that essential for functional learning ability, mental health, and even the performance normal activities. Insomnia, sleep apnea, restless legs are all examples sleep-related issues growing more widespread. When appropriately analyzed, recording bio-electric signals, such as Electroencephalogram, can tell how well we sleep. Improved analyses possible due to recent improvements in machine feature extraction, they commonly referred automatic analysis distinguish them from data by human expert. This study outlines Systematic Literature Review results it provided assess present state-of-the-art data. A search string was organized according PICO (Population, Intervention, Comparison, Outcome) strategy order determine what extraction approaches used generate an Automatic Scoring System. The American Academy Medicine Rechtschaffen & Kales two main scoring standards contemporary research, report. Other types sensors, Electrooculography, employed addition Electroencephalography automatically score Furthermore, existing research on parameter tuning models examined proved be incomplete. Based our findings, different standards, numerous algorithms with tuning, have high potential developing reliable robust system supporting physicians. In context problem, there evident gaps need investigated terms engineering techniques algorithms.