Szczegóły

Tytuł artykułu

Effect of Feature Extraction on Automatic Sleep Stage Classification by Artificial Neural Network

Tytuł czasopisma

Metrology and Measurement Systems

Rocznik

2017

Wolumin

vol. 24

Numer

No 2

Autorzy

Wydział PAN

Nauki Techniczne

Wydawca

Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation

Data

2017

Identyfikator

ISSN 0860-8229

Referencje

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Loomis (1937), Cerebral states during sleep , as studied by human brain potentials, Exp Psychol, 21, 127. ; Hassan (2016), Computer - aided sleep staging using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and bootstrap aggregating, Biomed Signal Process Control, 24, 1. ; Polak (2009), Development of a telemedical system for monitoring patients with chronic respiratory diseases In World Congress on Medical Physics and Biomedical Engineering, Dossel and Schlegel IFMBE Proceedings, 51. ; Sanders (2014), Sleep Stage Classification with Cross Frequency Coupling, Conf Proc IEEE Eng Med Biol Soc, 2014. ; Goldberger (2000), PhysioBank and PhysioNet : Components of a New Research Resource for Complex Physiologic Signals, Circulation, 101. ; Güneş (2009), A novel data pre - processing method on automatic determining of sleep stages : K - means clustering based feature weighting Complex Syst, Appl ICCSA, 112. ; Silveira (2017), Single channel EEG sleep stage classification based on a streamlined set of statistical features in wavelet domain, Med Biol Eng Comput, 55, 343. ; 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Lee (2013), Electroencephalography Analysis Using Neural Network and Support Vector Machine during Sleep, Engineering, 5, 88. ; Hassan (2017), Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting Programs Biomed, Comput Methods, 140. ; Tsinalis (2016), Automatic Sleep Stage Scoring with Single - Channel EEG Using Convolutional Neural Networks, Ann Biomed Eng, 44, 1587. ; Becq (2005), Comparison Between Five Classifiers for Automatic Scoring of Human Sleep Recordings, Stud Comput Intell, 4, 113. ; Malinowska (2006), Micro - and Macrostructure of Sleep EEG, IEEE Eng Med Biol Mag, 25, 26. ; Djemili (2016), Application of empirical mode decomposition and artificial neural network for the classification of normal and epileptic EEG signals, Biocybern Biomed Eng, 36, 285. ; Huang (1998), The empirical mode decomposition and the Hilbert spectrum for nonlinear and non - stationary time series analysis, Proc, 454. ; 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DOI

10.1515/mms-2017-0036

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