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One Dimensional Local Binary Patterns of Electroencephalogram Signals for Detecting Alzheimer's Disease

Tirunagari, Santosh, Kouchaki, S, Abasolo, Daniel Emilio and Poh, Norman (2017) One Dimensional Local Binary Patterns of Electroencephalogram Signals for Detecting Alzheimer's Disease In: 22nd International Conference on Digital Signal Processing, August 23 - 25 2017, London, UK.

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Abstract

Alzheimer’s disease (AD) is neurodegenerative, caused by the progressive death of brain cells over time. One non-invasive approach to investigate AD is to use electroencephalogram (EEG) signals. The data are usually non-stationary with a strong background activity and noise which makes the analysis difficult leading to low performance in many real world applications including the detection of AD. In this study, we present a method based on local texture changes of EEG signals to differentiate AD patients from the healthy ones, using one-dimensional local binary patterns (1D-LBPs) coupled with support vector machines (SVM). Our proposed method maps the EEG data into a less detailed representation which is less sensitive to noise. A 10 fold cross validation performed at both the epoch and subject level show the discriminancy power of 1D-LBP feature vectors with application to AD data.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Mechanical Engineering Sciences
Authors :
NameEmailORCID
Tirunagari, Santoshsantosh.tirunagari@surrey.ac.ukUNSPECIFIED
Kouchaki, SUNSPECIFIEDUNSPECIFIED
Abasolo, Daniel EmilioD.Abasolo@surrey.ac.ukUNSPECIFIED
Poh, NormanN.Poh@surrey.ac.ukUNSPECIFIED
Date : 21 December 2017
Copyright Disclaimer : © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Related URLs :
Depositing User : Melanie Hughes
Date Deposited : 21 Sep 2017 09:05
Last Modified : 21 Sep 2017 09:05
URI: http://epubs.surrey.ac.uk/id/eprint/842362

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