Fast and high accuracy classification of sleep EEG using PLSR method

Author

Eroglu, K. ; Maleki, Mehdi ; Kayikcioglu, T.

Author_Institution

Elektrik-Elektron. Muhendisligi Bolumu, Karadeniz Teknik Univ., Trabzon, Turkey

fYear

2013

fDate

24-26 April 2013

Firstpage

1

Lastpage

4

Abstract

The aim of this study is to classify the status of sleep from electroencefelography (EEG) data recorded from seven different healthy individuals. The twenty two autoregressive (AR) model coefficent are computed and used as features. Three classification algorithms, namely k-NN, Bayes and PLSR methods are trained and tested. The results show that the PLSR algorithm yielded highest accuracy and short classification times. Furthermore, all utilizies just a single channel. Based on these results we propose that method can be used in clinical applications.

Keywords

electroencephalography; medical signal processing; signal classification; Bayes methods; PLSR method; autoregressive model coefficent; classification algorithms; clinical applications; electroencefelography data; fast accuracy classification; high accuracy classification; k-NN; short classification times; sleep EEG; Brain modeling; Electroencephalography; Electromyography; Electrooculography; Feature extraction; Mathematical model; Sleep; Autoregressive Model; Bayes; EEG; Partial Least Squares Regression; Sleep; k-Nearest Neighbor;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications Applications Conference (SIU), 2013 21st

Conference_Location

Haspolat

Print_ISBN

978-1-4673-5562-9

Electronic_ISBN

978-1-4673-5561-2

Type

conf

DOI

10.1109/SIU.2013.6531317

Filename

6531317