The Comparison of Wavelet and Empirical Mode Decomposition Method in Prediction of Sleep Stages from EEG Signals

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Tarih

2017

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Ieee

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

The aim of this study was to detect sleep stages of human by using EEG signals. In accordance with this purpose, discrete wavelet transforms (DWT) and empirical mode decomposition (EMD) were separately used for feature extraction. Subcomponents of EEG signals obtained by the two methods were assumed as feature vectors. Statistical parameters were used to reduce dimension of feature vectors. The same statistical parameters were used to compare performance of methods related to DWT and EMD. K nearest neighborhood (kNN) algorithm was used in classification final feature vectors that obtained EEG segments related to different sleep stages. The classification accuracies for feature vectors based on DWT and EMD were obtained as 100% and 88.13%, respectively.

Açıklama

2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEY

Anahtar Kelimeler

Eeg, Classification, Empirical Mode Decomposition, Discrete Wavelet Transform

Kaynak

2017 International Artificial Intelligence and Data Processing Symposium (Idap)

WoS Q Değeri

N/A

Scopus Q Değeri

N/A

Cilt

Sayı

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