Analysis of Sleeping Patterns
Monitoring the brain signals of the human during sleep can provide us a good estimation of physical and mental state of a human. When such data are combined, either with the knowledge of a sleep pathologist or with a special automated diagnosis system, they could help the diagnosis of various types of sleep disorders such as parasomnias, insomnia, and dyspnea. Furthermore, such data could also be useful in diagnosis of various medical conditions, and in quantitative evaluation of the effects of medications that is administered to a patient who is suffering from poor sleep quality.
There are two main broad types of sleep, each with its own distinct physiological, neurological and psychological features: rapid eye movement (REM) sleep and non-rapid eye movement (non-REM or NREM) sleep. REM is the restorative part of sleep and NREM is the stage between wakefulness and sleep, sometimes referred to as somnolence or drowsy sleep, in which the muscles are still quite active and the eyes roll around slowly and may open and close from time to time. REM sleep is a vital component of human sleep patterns, particularly during early childhood development and the lack of REM sleep leads to some negative effects on behavior.
This project is a software implementation and hardware design to monitor and record the Non–Rapid Eye Movement (NREM) and Rapid Eye Movement (REM) stages of the human sleep. The hardware is a real time monitoring system that reads EEG signal and sends it to cloud.