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Fuzzy Entropy Analysis of the Electroencephalogram in Patients with Alzheimer’s Disease: Is the Method Superior to Sample Entropy?

Simons, Samantha, Espino, P and Abasolo, Daniel Emilio (2018) Fuzzy Entropy Analysis of the Electroencephalogram in Patients with Alzheimer’s Disease: Is the Method Superior to Sample Entropy? Entropy, 20 (1).

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Abstract

Alzheimer’s disease (AD) is the most prevalent form of dementia in the world, which is characterised by the loss of neurones and the build-up of plaques in the brain, causing progressive symptoms of memory loss and confusion. Although definite diagnosis is only possible by necropsy, differential diagnosis with other types of dementia is still needed. An electroencephalogram (EEG) is a cheap, portable, non-invasive method to record brain signals. Previous studies with non-linear signal processing methods have shown changes in the EEG due to AD, which is characterised reduced complexity and increased regularity. EEGs from 11 AD patients and 11 age-matched control subjects were analysed with Fuzzy Entropy (FuzzyEn), a non-linear method that was introduced as an improvement over the frequently used Approximate Entropy (ApEn) and Sample Entropy (SampEn) algorithms. AD patients had significantly lower FuzzyEn values than control subjects (p < 0.01) at electrodes T6, P3, P4, O1, and O2. Furthermore, when diagnostic accuracy was calculated using Receiver Operating Characteristic (ROC) curves, FuzzyEn outperformed both ApEn and SampEn, reaching a maximum accuracy of 86.36%. These results suggest that FuzzyEn could increase the insight into brain dysfunction in AD, providing potentially useful diagnostic information. However, results depend heavily on the input parameters that are used to compute FuzzyEn.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Mechanical Engineering Sciences
Authors :
NameEmailORCID
Simons, Samanthas.simons@surrey.ac.uk
Espino, P
Abasolo, Daniel EmilioD.Abasolo@surrey.ac.uk
Date : 3 January 2018
Identification Number : 10.3390/e20010021
Copyright Disclaimer : © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Uncontrolled Keywords : Alzheimer’s disease; electroencephalogram; non-linear analysis; complexity; irregularity; Fuzzy Entropy; Sample Entropy
Depositing User : Melanie Hughes
Date Deposited : 04 Jan 2018 14:56
Last Modified : 27 Mar 2018 12:53
URI: http://epubs.surrey.ac.uk/id/eprint/845539

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