Abstract
Sleep is an essential element for an individual’s well-being and is considered vital for the overall mental and physical heath of a person. Sleep can be considered as a virtual detachment of an individual from his environment. In normal humans, about 30% of their life-time is spent for sleep. Artificial neural networks (ANNs) or connectionist systems are computing systems inspired by the biological neural networks that constitute animal brains. Sleep scoring is under taken by the examination and visual inspection of polysomnograms (PSG) done by sleep specialist. PSG is specialty test, the conduction of which includes the recording of various physiological signals. The signals obtained are processed using digital processing tools so as to extract information. Soft computing techniques are used to analyze the signals. ANNs are parallel adaptive systems suitable for solving of non-linear problems. Using ANN for automatic sleep scoring is especially promising because of new ANN learning algorithms allowing faster classification without decreasing the performance. Both appropriate preparations of training data as well as selection of the ANN model make it possible to perform effective and correct recognizing of relevant sleep stages. Such an approach is highly topical, taking into consideration the fact that there is no automatic scorer utilizing ANN technology available at present. The high performances observed with systems based onneural networks highlight that these tools may be act new tools in the field of sleep research. In this scenario we are surmised the review regarding the computer assisted automatic scoring of sleep and soft computing technique Artificial Neural Network.
Highlights
Sleep is an essential element for an individual’s well-being and is considered vital for the overall mental and physical heath of a person
The data were recorded through polisomnography device. These collective variant data were first grouped by an expert physician and the software of polisomnography, and used for training and testing the proposed Artificial Neural Network (ANN)
A good scoring was attained through the trained ANN, so it may be put into use in clinics where lacks of specialist physicians
Summary
Sleep is an essential element for an individual’s well-being and is considered vital for the overall mental and physical heath of a person. Prolonged sleep disorders or a continuing sleep insufficiency invites various disease of heart, kidney, etc and increases the risk of contracting long term passive diseases like diabetes, high blood pressure as well as stroke in extreme cases These are a source of discomfort and distress to the individual experiencing them such as insomnia, sleep walking, narcolepsy and nocturnal breathing disorders and they need to be treated [4]. International Journal of Sensors and Sensor Networks 2017; 5(3): 43-47 brain waves generated by cortex and other integrative processing mechanism, Respiration, Electromyogram (EMG):the record of electrical activity that emanates from active muscles and pulse Oximetry [6] All of these signals when recorded for a short period of time called epoch and analysed indicate different stages of sleep. These stages are scored according to the various signals recorded in that particular epoch [7]
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