Literature DB >> 20703927

Estimation of sleep stages by an artificial neural network employing EEG, EMG and EOG.

M Emin Tagluk1, Necmettin Sezgin, Mehmet Akin.   

Abstract

Analysis and classification of sleep stages is essential in sleep research. In this particular study, an alternative system which estimates sleep stages of human being through a multi-layer neural network (NN) that simultaneously employs EEG, EMG and EOG. The data were recorded through polisomnography device for 7 h for each subject. These collective variant data were first grouped by an expert physician and the software of polisomnography, and then used for training and testing the proposed Artificial Neural Network (ANN). A good scoring was attained through the trained ANN, so it may be put into use in clinics where lacks of specialist physicians.

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Year:  2009        PMID: 20703927     DOI: 10.1007/s10916-009-9286-5

Source DB:  PubMed          Journal:  J Med Syst        ISSN: 0148-5598            Impact factor:   4.460


  14 in total

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2.  Automatic differentiation of multichannel EEG signals.

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Review 3.  Artificial neural networks: fundamentals, computing, design, and application.

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6.  Automatic recognition of alertness level by using wavelet transform and artificial neural network.

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7.  Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients.

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Journal:  J Neurosci Methods       Date:  2005-07-28       Impact factor: 2.390

8.  Multiclass support vector machines for EEG-signals classification.

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9.  Sleep staging automaton based on the theory of evidence.

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Journal:  IEEE Trans Biomed Eng       Date:  1989-05       Impact factor: 4.538

10.  Video analysis of gross body movements during sleep.

Authors:  M Shimohira; T Shiiki; J Sugimoto; Y Ohsawa; M Fukumizu; T Hasegawa; Y Iwakawa; Y Nomura; M Segawa
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  5 in total

1.  Time frequency analysis for automated sleep stage identification in fullterm and preterm neonates.

Authors:  Luay Fraiwan; Khaldon Lweesy; Natheer Khasawneh; Mohammad Fraiwan; Heinrich Wenz; Hartmut Dickhaus
Journal:  J Med Syst       Date:  2009-12-10       Impact factor: 4.460

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Review 5.  Methodologies and Wearable Devices to Monitor Biophysical Parameters Related to Sleep Dysfunctions: An Overview.

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  5 in total

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