Diagnosis of sleep disorders in optimized time slot based machine learning techniques on EEG energy level patterns

Abstract

A better consciousness depends on how well your sleep is. Study shows that, sleep disorder is become a crucial problem day by day due to work pressure and other different causes. Sleep patterns can be measured through the EEG signals as EEG data has proved to reflect the activities of brain over all the section with respect to human activities. It has been found that the percentage energy level may be considered as parameter to distinguish healthy and defected EEG data due to sleep disorder. S0 is proved to be helpful in diagnosis of RBD sleep disorder because in this stage the percentage energy level is found to be decreased for alpha waves for patients having sleep disorder. The average percentage energy level in RBD patient is 0.1139 while in normal cases it is 0.1530 during alpha activity. We consider an optimized time slot for this experiment because to measure sleep quality through the systems currently available is not only complicated but also to a large extent expensive. To summarize the systems available at present. This work includes to identify Optimized Time Slot towards Frequency based EEG Sleep Disease Signal Patterns Evaluation using Machine Learning Techniques. With the results it is found that the slot 1 (12:00 PM – 1:20 PM) is the most important one in the processes of measuring sleep quality while considering sleep period 07:00 PM – 06:00 AM.