Automatic Sleep Stages Detection Using Supervised Machine Learning

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dc.contributor.advisorDr. Tanzilur Rahman
dc.contributor.authorSaidul Islam Tanveer
dc.contributor.authorTangim Hossain Akash
dc.contributor.authorRakibul Hoque Foysal
dc.contributor.id1611556043
dc.contributor.id1530092042
dc.contributor.id1431238043
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2024-06-04
dc.date.accessioned2024-06-04T09:29:30Z
dc.date.available2024-06-04T09:29:30Z
dc.date.issued2020
dc.description.abstractSleep is a natural behavior and part and parcel of Human‟s life. Nowadays, sleeping disorder is common question for both man and women. So, sleep related research is accelerated by researcher and heath care community. Sleep research can achieve the better way for diagnosis and treatment of sleeping related complaint. Over the past few decades, sleep classification is introduced. Automatic sleep stages classification is preferable approach for sleep researchers. Manual sleep scoring also visible Sometimes, nowadays. There is lot of difficulty in manual scoring which is very time consuming and prone to Human error. Automatic sleep stages classification using Machine learning model can create a great solution for diagnosis purpose. Different kinds of machine learning algorithms are used by many researchers. Here, in this research we use multiple supervised machine learning model to classify the sleep stages. In this research using EEG patterns of healthy and mild difficulty subjects over 95% of accuracy is obtained by the classifier. Total 31 features (spectral and statistical features) is applied to dataset before that 10 features were taken. For finding the significance nature of features Kruskalwallis anova test is applied. After that using Knn, Decision tree and Bagged tree algorithms evaluated the model accuracy. Bagged tree algorithm take vital role in accuracy which is higher than two other algothoms.So, the model used in this thesis is effective for both healthy and mild difficulty subject.
dc.description.degreeUndergraduate
dc.identifier.cd600000083
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/891
dc.language.isoen_US
dc.publisherNorth South University
dc.rights© NSU Library
dc.subjectTECHNOLOGY::Electrical engineering, electronics and photonics::Electrical engineering
dc.titleAutomatic Sleep Stages Detection Using Supervised Machine Learning
dc.typeProject
oaire.citation.endPage51
oaire.citation.startPage1
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