EEG Sleep Detection Algorithms a Comparative Study

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

doi:10.21276/ijircst.2019.7.3.3

Abstract

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.

Keywords

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

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Cites this article as

A. S. M. Miah, S. Sahadat, D. M. Rashid, "EEG Sleep Detection Algorithms a Comparative Study", International Journal of Innovative Research in Engineering & Management (ijircst), Vol-7, Issue-3, Page No-37-43, 2019. Available from: https://doi.org/10.21276/ijircst.2019.7.3.3

Corresponding Author

Abu Saleh Musa Miah

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

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