Sleep stages classification using wavelettransform & neural network

Author

Jain, Vardhman Pukhraj ; Mytri, V.D. ; Shete, Virendra V. ; Shiragapur, B.K.

Author_Institution

COE, MIT, Pune, India

fYear

2012

fDate

5-7 Jan. 2012

Firstpage

71

Lastpage

74

Abstract

In this paper the feature extraction of the EEG Signal is done by computing the Discrete Wavelet Transform. The wavelet transform coefficients compress the number of data points into few features. Various statistics were used to further reduce the dimensionality. The Classification of the EEG sleep stages is done by using neural network which provides more accurate sleep stage classification compared to other techniques.

Keywords

discrete wavelet transforms; electroencephalography; feature extraction; medical signal processing; neural nets; signal classification; sleep; statistics; EEG signal; EEG sleep stages classification; data point compression; dimensionality reduction; discrete wavelet transform; feature extraction; neural network; statistics; Biographies; Artificial Neural Network (ANN); Back Propagation Neural Network (BPN); DWT (Discrete Wavelet Transform); Electroencephalogram (EEG); Standard Deviation (SD);

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical and Health Informatics (BHI), 2012 IEEE-EMBS International Conference on

Conference_Location

Hong Kong

Print_ISBN

978-1-4577-2176-2

Electronic_ISBN

978-1-4577-2175-5

Type

conf

DOI

10.1109/BHI.2012.6211508

Filename

6211508