Details

Title

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

Journal title

Metrology and Measurement Systems

Yearbook

2017

Volume

vol. 24

Issue

No 2

Authors

Keywords

sleep stage classification ; EEG signal ; power spectral density ; discrete wavelet transform ; empirical mode decomposition ; artificial neural network

Divisions of PAS

Nauki Techniczne

Coverage

229–240

Publisher

Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation

Date

2017.06.30

Type

Artykuły / Articles

Identifier

DOI: 10.1515/mms-2017-0036 ; ISSN 2080-9050, e-ISSN 2300-1941

Source

Metrology and Measurement Systems; 2017; vol. 24; No 2; 229–240

References

Wu (2015), Assess Sleep Stage by Modern Signal Processing Techniques, IEEE Trans Biomed Eng, 62, 1159. ; Mohammadi (2016), Improving time - frequency domain sleep EEG classification via singular spectrum analysis, Neurosci Methods, 273. ; Jabłoński (2013), Modern methods for description of complex couplings in neurophysiology of respiration, IEEE Sensors, 13, 3182. ; Oh (2014), A Novel EEG Feature Extraction Method Using Hjorth Parameter, Electron Electr Eng, 2, 106. ; Hassan (2016), Automatic sleep scoring using statistical features in the EMD domain and ensemble methods, Biocybern Biomed Eng, 36, 248. ; Hwang (2016), Apnea - hypopnea index estimation using quantitative analysis of sleep macrostructure, Physiol Meas, 37, 554. ; Diykh (2016), Complex networks approach for EEG signal sleep stages classification, Expert Syst Appl, 63, 241. ; Sen (2014), A Comparative Study on Classification of Sleep Stage Based on EEG Signals Using Feature Selection and Classification Algorithms, Med Syst, 38, 18. ; Jabłoński (2011), A preliminary study on the accuracy of respiratory input measurement using the interrupter technique Programs Biomed, Comput Methods, 101. ; Peker (2016), A new approach for automatic sleep scoring : Combining Taguchi based complex - valued neural network and complex wavelet transform Programs Biomed, Comput Methods, 129. ; Welch (1967), The Use of Fast Fourier Transform for the Estimation of Power Spectra : A Method Based on Time Averaging Over Short , Modified Periodograms, IEEE Trans Audio Electroacoust, 15, 70. ; Jabłoński (2009), Frequency - domain identification of the respiratory system during airflow interruption, Measurement, 42, 390. ; Abdi (2010), Principal component analysis Wiley Interdiscip, Rev Comput Stat, 2, 433. ; Bajaj (2012), Classification of seizure and nonseizure EEG signals using empirical mode decomposition, IEEE Trans Inf Technol Biomed, 16, 1135. ; Khan (2016), Classification of EEG signal using adaptive time - frequency distributions, Metrol Meas Syst, 2, 251. ; 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. ; Cybenko (1989), Approximation by superpositions of a sigmoidal function Control Signals Syst, Math, 2, 303. ; Ronzhina (2012), Sleep scoring using artificial neural networks, Sleep Med Rev, 16, 251. ; Kleitman (1953), Regularly occurring periods of eye motility , and concomitant phenomena , during sleep, Science, 118. ; Boostani (2017), A comparative rewiev on sleep stage classification methods in patients and healthy individuals Programs Biomed, Comput Methods, 140. ; Subasi (2005), Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients, Expert Syst Appl, 28, 701. ; Yucelbas (2016), Effect of EEG Time Domain Features on the Classification of Sleep Stages, Indian J Sci Technol, 9, 1. ; Hsu (2013), Automatic sleep stage recurrent neural classifier using energy features of EEG signals, Neurocomputing, 104, 105. ; Pinero (2004), Sleep stage classification using fuzzy sets and machine learning techniques, Neurocomputing, 58. ; Adeli (2003), Analysis of EEG records in an epileptic patient using wavelet transform, Neurosci Methods, 123. ; 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. ; Peker (2016), An efficient sleep scoring system based on EEG signal using complex - valued machine learning algorithms, Neurocomputing, 207. ; Ebrahimi (2008), Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients, Conf Proc IEEE Eng Med Biol Soc, 1151.

Open Access Policy

Metrology and Measurement Systems is an open access journal with all content available with no charge in full text version.


The journal content is available under the license CC BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/
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