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The use of kurtosis de-noising for EEG analysis of patients suffering from Alzheimer’s disease

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 4th International Conference on Biomedical Engineering and Biotechnology, 18–21 August 2015, Shanghai, China

Guest editors: Feng Liu, Dong-Hoon Lee, Ricardo Lagoa and Sandeep Kumar

Article type: Research Article

Authors: Wang, Ganga | Shepherd, Simon J.b | Beggs, Clive B.b | Rao, Ninia | Zhang, Yinga; *

Affiliations: [a] University of Electronic Science and Technology of China, 611731, Chengdu, China | [b] Medical Biophysics Group, School of Engineering, Design and Technology University of Bradford, BD7 1DP, Bradford, UK

Correspondence: [*] Address for correspondence: Ying Zhang, University of Electronic Science and Technology of China, 611731, Chengdu, China. Tel.: +86 28 61831571; Fax: +86 28 61830633; E-mail: zhangyingxf@uestc.edu.cn.

Note: [1] This work was supported by National Science Foundation of China under Grants 61371182 and 41301459, and the State Scholarship Fund of China.

Keywords: Electroencephalogram (EEG), Alzheimer’s disease (AD), kurtosis, sample entropy (SE)

DOI: 10.3233/BME-151410

Journal: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S1135-S1148, 2015

Published: 2015
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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.

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