title: The use of kurtosis de-noising for EEG analysis of patients suffering from Alzheimer's disease.
abstract: The use of electroencephalograms (EEGs) to diagnose and analyses Alzheimer's disease (AD) has received much attention in recent years. The sample entropy (SE) has been widely applied to the diagnosis of AD. In our study, nine EEGs from 21 scalp electrodes in 3 AD patients and 9 EEGs from 3 age-matched controls are recorded. The calculations show that the kurtoses of the AD patients' EEG are positive and much higher than that of the controls. This finding encourages us to introduce a kurtosis-based de-noising method. The 21-electrode EEG is first decomposed using independent component analysis (ICA), and second sort them using their kurtoses in ascending order. Finally, the subspace of EEG signal using back projection of only the last five components is reconstructed. SE will be calculated after the above de-noising preprocess. The classifications show that this method can significantly improve the accuracy of SE-based diagnosis. The kurtosis analysis of EEG may contribute to increasing the understanding of brain dysfunction in AD in a statistical way.
authors.affiliation: University of Electronic Science and Technology of China, 611731, Chengdu, China.
authors.forename: Gang
authors.initials: G
authors.lastname: Wang
authors.affiliation: Medical Biophysics Group, School of Engineering, Design and Technology University of Bradford, BD7 1DP, Bradford, UK.
authors.forename: Simon J
authors.initials: SJ
authors.lastname: Shepherd
authors.affiliation: Medical Biophysics Group, School of Engineering, Design and Technology University of Bradford, BD7 1DP, Bradford, UK.
authors.forename: Clive B
authors.initials: CB
authors.lastname: Beggs
authors.affiliation: University of Electronic Science and Technology of China, 611731, Chengdu, China.
authors.forename: Nini
authors.initials: N
authors.lastname: Rao
authors.affiliation: University of Electronic Science and Technology of China, 611731, Chengdu, China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Zhang
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000503:physiopathology; D016477:Artifacts; D001921:Brain / Q000503:physiopathology; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D059629:Signal-To-Noise Ratio
keywords: Alzheimer’s disease (AD); Electroencephalogram (EEG); kurtosis; sample entropy (SE)