Non-linear Analysis of the Electroencephalogram in Alzheimer's Disease by Means of Symbolic Sequence Decomposition Method
Tosun, PD, Simons, S, Winsky-Sommerer, R and Abasolo, D (2015) Non-linear Analysis of the Electroencephalogram in Alzheimer's Disease by Means of Symbolic Sequence Decomposition Method International Journal of Applied Mathematics, Electronics and Computers, 3 (1). pp. 14-17.
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Abstract
In this pilot study, a symbolic sequence decomposition method was used in conjunction with Shannon’s entropy to investigate the changes in electroencephalogram signals of 11 patients with Alzheimer’s disease and 11 age-matched control subjects. Results were statistically analysed by student t-test and later classified with receiver operating curves. Statistically significant differences between both groups were found at electrodes Fp1, O2, P3, T4 and T5. Sensitivity (defined as percentages of correctly classified patients) and specificity (defined as correctly classified controls) were evaluated using the receiver operating curves method. Accuracy of the methods was calculated according to sensitivity and specificity measures of electrodes showing statistically significant differences between the control group and Alzheimer's disease patients and ranged between 72.73-77.27%. These accuracy values were in agreement with previously published entropy studies on this data set. Although combining these methods did not provide any greater accuracy over previous findings, using a symbolic sequence decomposition method enhanced the data processing.
Item Type: | Article | |||||||||||||||
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Divisions : | Faculty of Engineering and Physical Sciences > Mechanical Engineering Sciences | |||||||||||||||
Authors : |
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Date : | 7 January 2015 | |||||||||||||||
Additional Information : | © Advanced Technology & Science 2013. This work is licensed under a Creative Commons Attribution 4.0 License. | |||||||||||||||
Depositing User : | Symplectic Elements | |||||||||||||||
Date Deposited : | 12 May 2015 13:45 | |||||||||||||||
Last Modified : | 31 Oct 2017 17:26 | |||||||||||||||
URI: | http://epubs.surrey.ac.uk/id/eprint/807561 |
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