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Stockwell transform and clustering techniques for efficient detection of vision impairments from single trial VEPs

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Main Authors: Vijean, Vikneswaran, Hariharan, Muthusamy, Dr., Sazali, Yaacob, Prof. Dr., Mohd Nazri, Sulaiman
Other Authors: vicky.86max@gmail.com
Format: Article
Language:English
Published: Inderscience Enterprises Ltd. 2014
Subjects:
ELM
Extreme learning machine
Feature weighting
Levenberg-Marquardt back propagation neural network
LMBP
Stockwell transform
VEP
Vision impairment
Visually evoked potential
Online Access:http://dspace.unimap.edu.my:80/dspace/handle/123456789/34385
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spelling my.unimap-343852014-05-10T18:13:00Z Stockwell transform and clustering techniques for efficient detection of vision impairments from single trial VEPs Vijean, Vikneswaran Hariharan, Muthusamy, Dr. Sazali, Yaacob, Prof. Dr. Mohd Nazri, Sulaiman vicky.86max@gmail.com hari@unimap.edu.my s.yaacob@unimap.edu.my nazri_sulaiman@hotmail.com ELM Extreme learning machine Feature weighting Levenberg-Marquardt back propagation neural network LMBP Stockwell transform VEP Vision impairment Visually evoked potential Link to publisher's homepage at http://www.inderscience.com/index.php Pattern reversal visually evoked potentials (VEPs) provide valuable information about the visual nerves pathways and is a promising field to be explored for the investigation of vision impairments. The conventional method of analysis however, is centred on the detection of amplitude and latency values from the averaged VEP responses. This paper proposes alternative method of analysis using Stockwell transform (ST) for discrimination of vision impairments using single trial VEPs. The pattern reversal VEPs for the research is collected non-invasively from 16 eyes of ten subjects. The signals are decomposed into delta, theta, alpha, beta, gamma1 and gamma2 bands, and five different features are extracted from the ST matrix. The features are weighted using feature weighting method based on clustering centres of k-means clustering (KMC), fuzzy c-means clustering (FMC), and subtractive clustering (SBC) to improve the interclass variations. Extreme learning machine (ELM) and Levenberg-Marquardt back propagation neural network (LMBP) are used to discriminate the vision impairments, and the proposed method is able to achieve a maximum accuracy of 99.95%. 2014-05-10T18:13:00Z 2014-05-10T18:13:00Z 2013 Article International Journal of Medical Engineering and Informatics, vol. 5(4), 2013, pages 352-371 1755-0661 (Online) 1755-0653 (Print) http://www.inderscience.com/info/inarticle.php?artid=57192 http://dspace.unimap.edu.my:80/dspace/handle/123456789/34385 10.1504/IJMEI.2013.057192 en Inderscience Enterprises Ltd.
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic ELM
Extreme learning machine
Feature weighting
Levenberg-Marquardt back propagation neural network
LMBP
Stockwell transform
VEP
Vision impairment
Visually evoked potential
spellingShingle ELM
Extreme learning machine
Feature weighting
Levenberg-Marquardt back propagation neural network
LMBP
Stockwell transform
VEP
Vision impairment
Visually evoked potential
Vijean, Vikneswaran
Hariharan, Muthusamy, Dr.
Sazali, Yaacob, Prof. Dr.
Mohd Nazri, Sulaiman
Stockwell transform and clustering techniques for efficient detection of vision impairments from single trial VEPs
description Link to publisher's homepage at http://www.inderscience.com/index.php
author2 vicky.86max@gmail.com
author_facet vicky.86max@gmail.com
Vijean, Vikneswaran
Hariharan, Muthusamy, Dr.
Sazali, Yaacob, Prof. Dr.
Mohd Nazri, Sulaiman
format Article
author Vijean, Vikneswaran
Hariharan, Muthusamy, Dr.
Sazali, Yaacob, Prof. Dr.
Mohd Nazri, Sulaiman
author_sort Vijean, Vikneswaran
title Stockwell transform and clustering techniques for efficient detection of vision impairments from single trial VEPs
title_short Stockwell transform and clustering techniques for efficient detection of vision impairments from single trial VEPs
title_full Stockwell transform and clustering techniques for efficient detection of vision impairments from single trial VEPs
title_fullStr Stockwell transform and clustering techniques for efficient detection of vision impairments from single trial VEPs
title_full_unstemmed Stockwell transform and clustering techniques for efficient detection of vision impairments from single trial VEPs
title_sort stockwell transform and clustering techniques for efficient detection of vision impairments from single trial veps
publisher Inderscience Enterprises Ltd.
publishDate 2014
url http://dspace.unimap.edu.my:80/dspace/handle/123456789/34385
_version_ 1643797475725672448
score 13.209306

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