NDLI: Sleep stages classification using shallow classifiers

Content Provider	IEEE Xplore Digital Library
Author	Giri, Endang Purnama Arymurthy, Aniati Murni Fanany, Mohammad Ivan Wijaya, Sastra Kusuma
Copyright Year	2015
Description	Author affiliation: Department of Physics, Faculty of Mathematics and Natural Sciences, University of Indonesia, Depok, West Java, Indonesia (Wijaya, Sastra Kusuma) \|\| Faculty of Computer Science, University of Indonesia, Depok, West Java, Indonesia (Giri, Endang Purnama; Arymurthy, Aniati Murni; Fanany, Mohammad Ivan)
Abstract	A person with sleep disorder such as apnea will stop breathing for a while during sleep. If frequently occurs, sleep disorder is dangerous for health. An early step for diagnosing apnea is by classifying the sleep stages during sleep. This study explores some shallow classifiers and their feasibility applied to sleep data. Recently, a sleep stages classification system that use deep unsupervised features learning representations have been proposed [9]. In our view, an adequate study on this problem using shallow classifiers still need to be investigated. This study, using some of the data on [9], focuses on evaluating some shallow classifier to the sleep stages classification problem. This study evaluates five classifiers: SVM, Neural Network, Classification Tree, k-Nearest Neighborhood (k-NN), and Naive Bayes. Experiment result shows that neural network gives best performance for sleep stage classification problem. Compared to the SVM (the 2-nd rank of accuracy on S000 data), the neural network is also more efficient than SVM in term of computational time and memory requirement.
Starting Page	297
Ending Page	301
File Size	565111
Page Count	5
File Format	PDF
e-ISBN	9781509003631
DOI	10.1109/ICACSIS.2015.7415162
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-10
Publisher Place	Indonesia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Electrooculography Sleep stage classification Neural networks EEG Silicon Robustness Electromyography Bayes methods Classifier Neural network SVM
Content Type	Text
Resource Type	Article

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