Abstract
Sleep is utterly regarded as compulsory component for a person’s prosperity and is an exceedingly important element for wellbeing of a healthy person. It is a condition in which an individual is physically and mentally at rest. The conception of sleep is considered extremely peculiar and is a topic of discussion and researchers all over the world has been attracted by this concept. Sleep analysis and its stages is analyzed to be useful in sleep research and sleep medicine area. By properly analyzing the sleep scoring system and its different stages has proven helpful for diagnosing sleep disorders. As it’s seen, sleep stage classification by manual process is a hectic procedure as it takes sufficient time for sleep experts to perform data analysis. Besides, mistakes and irregularities in between classification of same data can be recurrent. Therefore, the use of automatic scoring system in order to support reliable classification is highly in greater use. The scheduled work provides an insight to use the automatic scheme which is based on real time EMG signals and Artificial neural network. EMG is an electro neurological diagnostic tool which evaluates and records the electrical activity generated by muscle cells. The sleep scoring analysis can be applied by recording Electroencephalogram (EEG), Electromyogram (EMG), and Electrooculogram (EOG) based on epoch and this method is termed as PSG test or polysomnography test. The epoch measured has length segments for a period of 30 seconds. The standard database of EMG records was gathered from various hospitals in sleep laboratory which gives the different stages of sleep. These are Waking, Non-REM1 (stage-1), NonREM2 (stage-2), Non-REM3 (stage-3), REM. The collection of data was done for the period of 30 second known as epoch, for seven hours. The dataset obtained from the biological signal was managed so that necessary data is to be extracted from degenerated signal utilized for the purpose of study. As a matter of fact, it is known electrical signals are distributed throughout the body and is needed to be removed. These unwanted signals are termed as artifacts and they are removed with the help of filters. In this proposed work, the signal is filtered by making use of low-pass filter called Butterworth. The withdrawn characteristics were instructed and categorized by utilizing Artificial Neural Network (ANN). ANN, on the other hand is highly complicated network and utilizing same in the field of biomedical when contracted with electrical signals, acquired from human body is itself a novel. The precision obtained by the help of the procedure was discovered to be satisfactory and hence the process is very useful in clinics of sleep, especially helpful for neuro-scientists for discovering the disturbance in sleep.
Highlights
In front of sleep specialists, different quality of dataset from biological signal was acquired from various sleep laboratories in different hospitals
An effectual proposal has been achieved by consideration of correlation of different stages of sleep and biological signal using highly classified and developed classifier known as Artificial Neural Network
The aim of the study apart from the fact of considering the results acquired by comparativeanalysis, involves a scheme that includes EEG, EMG, EOG signals to be fed to a classifier with a high precision that is Artificial Neural Network
Summary
Sleep is termed as naturally recurring physical, psychological and emotional situation of a human being which is prominent by altering awareness. Sleep includes interference of all volitional muscles and there is a minimum interaction with the surrounding happenings [1]. Sleep is known to be favorably conserved behavior. Sleep is thestate in which an individual experiencesthe condition from conscious to sub conscious phase. Sleep is regarded as necessary phase which includes experience processing as well as memory consolidation. Persons affecting from sleep ailments like sleep narcolepsy, insomnia and sleep nocturnal breathing etc. Do not get sufficient sleep and need to be attended well. Importance of sleep is stressed by the manifestation of processesfor those who experience sleep disorders [2]
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