Fast and accurate PLS-based classification of EEG sleep using single channel data
作者： Temel Kayikcioglu , Masoud Maleki , Kubra Eroglu
DOI: 10.1016/J.ESWA.2015.06.010
关键词: Partial least squares regression 、 Speech recognition 、 Linear discriminant analysis 、 Artificial intelligence 、 Classifier (UML) 、 Pattern recognition 、 Cross-validation 、 Bayes' theorem 、 Channel data 、 Electroencephalography 、 Computer science 、 Sleep eeg
摘要: Fast classification of sleep and wake stages using a single EEG channel is proposed.The dataset was provided by Physionet.Speed accuracy PLS were compared with those k-NN, Bayes LDC classifiers.Results indicated that the Pz-Cz had better than Fpz-Cz channel.We achieved 91% selecting as classifier. Since speed important to real-time applications, this study proposed fast electroencephalograph (EEG) channel. Changes in are accompanied changes frequency spectrum signals; so, features extracted from 5-s epoch auto-regressive (AR) coefficients used represent signals different stages. The method based on partial least squares regression (PLS), which classify these finding an optimum beta K-fold cross validation. Physionet database confirm system. This system could be implementations because its high rate, capability implemented hardware owing very comfortable. Finally, results other classifiers such k-nearest neighborhood (k-NN), linear discriminant classifier (LDC) Bayes. We These comparisons revealed algorithm recognize emergency situation less 1s accuracy.
elsevier.com
sci-hub.se 参考文章(15)
P. G. Stoica, Randolph L. Moses, Introduction to spectral analysis ,(1997)
Jack Smith, Michael Negin, Arnold Nevis, Automatic Analysis of Sleep Electroencephalograms by Hybrid Computation IEEE Transactions on Systems Science and Cybernetics. ,vol. 5, pp. 278- 284 ,(1969) , 10.1109/TSSC.1969.300220
Kristína Šušmáková, Anna Krakovská, Discrimination ability of individual measures used in sleep stages classification Artificial Intelligence in Medicine. ,vol. 44, pp. 261- 277 ,(2008) , 10.1016/J.ARTMED.2008.07.005
Jack R Smith, Ismet Karacan, EEG sleep stage scoring by an automatic hybrid system Electroencephalography and Clinical Neurophysiology. ,vol. 31, pp. 231- 237 ,(1971) , 10.1016/0013-4694(71)90092-7
H. Kuwahara, H. Higashi, Y. Mizuki, S. Matsunari, M. Tanaka, K. Inanaga, Automatic real-time analysis of human sleep stages by an interval histogram method Electroencephalography and Clinical Neurophysiology. ,vol. 70, pp. 220- 229 ,(1988) , 10.1016/0013-4694(88)90082-X
R. Agarwal, J. Gotman, Computer-assisted sleep staging IEEE Transactions on Biomedical Engineering. ,vol. 48, pp. 1412- 1423 ,(2001) , 10.1109/10.966600
Fazil Duman, Aykut Erdamar, Osman Erogul, Ziya Telatar, Sinan Yetkin, Efficient sleep spindle detection algorithm with decision tree Expert Systems With Applications. ,vol. 36, pp. 9980- 9985 ,(2009) , 10.1016/J.ESWA.2009.01.061
Peter Anderer, Georg Gruber, Silvia Parapatics, Michael Woertz, Tatiana Miazhynskaia, Gerhard Klösch, Bernd Saletu, Josef Zeitlhofer, Manuel J. Barbanoj, Heidi Danker-Hopfe, Sari-Leena Himanen, Bob Kemp, Thomas Penzel, Michael Grözinger, Dieter Kunz, Peter Rappelsberger, Alois Schlögl, Georg Dorffner, An E-health solution for automatic sleep classification according to Rechtschaffen and Kales: validation study of the Somnolyzer 24 x 7 utilizing the Siesta database. Neuropsychobiology. ,vol. 51, pp. 115- 133 ,(2005) , 10.1159/000085205
Sheng-Fu Liang, Chin-En Kuo, Yu-Han Hu, Yu-Hsiang Pan, Yung-Hung Wang, Automatic Stage Scoring of Single-Channel Sleep EEG by Using Multiscale Entropy and Autoregressive Models IEEE Transactions on Instrumentation and Measurement. ,vol. 61, pp. 1649- 1657 ,(2012) , 10.1109/TIM.2012.2187242
K. Muller, C.W. Anderson, G.E. Birch, Linear and nonlinear methods for brain-computer interfaces international conference of the ieee engineering in medicine and biology society. ,vol. 11, pp. 165- 169 ,(2003) , 10.1109/TNSRE.2003.814484