EEG Sleep Detection Algorithms a Comparative Study

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.