IJIRCST

EEG Sleep Detection Algorithms a Comparative Study

Abu Saleh Musa Miah | Samiha Sahadat | Dr. Mamunur Rashid

In this paper it represents a comparison between some machine learning algorithm which is applied to solve the sleep monitoring issues. Sleep detection requires electroencephalogram signal for differentiating purpose. There are some methods & models that are already perused & built with training & testing datasets like- single layer perception, multilayer perception, SVM (support vector machine),boosted tree method. The difference between these models is measured using the Cohen’s index, the true positive & the true negative rate. Cross-validation technique is usually used to weigh the results of the models of monitoring sleep. The models successfully monitors sleep state reaching up to 94% and Cohen’s index successfully reaching up to 0.69.The success rate shows the considerable assurance for future expansion & practices.

EEG signal, Sleep stagine in EEG, feature extraction, EEG signal classification

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[Abu Saleh Musa Miah, Samiha Sahadat, Dr. Mamunur Rashid (2019), EEG Sleep Detection Algorithms a Comparative Study, International Journal of Innovative Research in Computer Science & Technology (IJIRCST), Vol-7, Issue-3, Page No-37-43], (ISSN 2347 - 5552). www.ijircst.org

Abu Saleh Musa Miah

Department of Computer Science and Engineering, Bangladesh Army University of Science and Technology. Rangpur, Bangladesh, musa@baust.edu.bd.

DOI:

DOI: 10.21276/ijircst.2019.7.3.3 DOI URL: https://doi.org/10.21276/ijircst.2019.7.3.3

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)