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EEG based detection of conductive and sensorineural hearing loss using artificial neural networks

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Main Authors: Pandiyan, Paulraj Murugesa , Prof. Dr., Subramaniam, Kamalraj, Sazali, Yaacob, Prof. Dr., Abdul Hamid, Adom, Prof. Dr., Hema, C. R.
Other Authors: paul@unimap.edu.my
Format: Article
Language:English
Published: Advanced Institute of Convergence IT 2014
Subjects:
Auditory evoked potential
EEG
Neural network
Power spectral density
Online Access:http://dspace.unimap.edu.my:80/dspace/handle/123456789/34877
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spelling my.unimap-348772014-05-29T09:13:01Z EEG based detection of conductive and sensorineural hearing loss using artificial neural networks Pandiyan, Paulraj Murugesa , Prof. Dr. Subramaniam, Kamalraj Sazali, Yaacob, Prof. Dr. Abdul Hamid, Adom, Prof. Dr. Hema, C. R. paul@unimap.edu.my kamalrajece@gmail.com s.yaacob@unimap.edu.my abdhamid@unimap.edu.my Auditory evoked potential EEG Neural network Power spectral density Link to publisher's homepage at http://www.aicit.org/ In this paper, a simple method has been proposed to distinguish the normal and abnormal hearing subjects (conductive or sensorineural hearing loss) using acoustically stimulated EEG signals. Auditory Evoked Potential (AEP) signals are unilaterally recorded with monaural acoustical stimulus from the normal and abnormal hearing subjects with conductive or sensorineural hearing loss. Spectral power and spectral entropy features of gamma rhythms are extracted from the recorded AEP signals. The extracted features are applied to machine-learning algorithms to categorize the AEP signal dynamics into their hearing threshold states (normal hearing, abnormal hearing) of the subjects. Feed forward and feedback neural network models are employed with gamma band features and their performances are analyzed in terms of specificity, sensitivity and classification accuracy for the left and right ears across 9 subjects. The maximum classification accuracy of the developed neural network was observed as 96.75 per cent in discriminating the normal and hearing loss (conductive or sensorineural) subjects. From the neural network models, it has been inferred that network models were able to classify the normal hearing and abnormal hearing subjects with conductive or sensorineural hearing loss. Further, this study proposed a feature band-score index to explore the feasibility of using fewer electrode channels to detect the type of hearing loss for newborns, infants, and multiple handicaps, person who lacks verbal communication and behavioral response to the auditory stimulation. 2014-05-29T09:13:01Z 2014-05-29T09:13:01Z 2013-05 Article Journal of Next Generation Information Technology, vol. 4(3), 2013, pages 204-212 2092-8637 http://www.aicit.org/jnit/global/paper_detail.html?jname=JNIT&q=168 http://dspace.unimap.edu.my:80/dspace/handle/123456789/34877 en Advanced Institute of Convergence IT
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 Auditory evoked potential
EEG
Neural network
Power spectral density
spellingShingle Auditory evoked potential
EEG
Neural network
Power spectral density
Pandiyan, Paulraj Murugesa , Prof. Dr.
Subramaniam, Kamalraj
Sazali, Yaacob, Prof. Dr.
Abdul Hamid, Adom, Prof. Dr.
Hema, C. R.
EEG based detection of conductive and sensorineural hearing loss using artificial neural networks
description Link to publisher's homepage at http://www.aicit.org/
author2 paul@unimap.edu.my
author_facet paul@unimap.edu.my
Pandiyan, Paulraj Murugesa , Prof. Dr.
Subramaniam, Kamalraj
Sazali, Yaacob, Prof. Dr.
Abdul Hamid, Adom, Prof. Dr.
Hema, C. R.
format Article
author Pandiyan, Paulraj Murugesa , Prof. Dr.
Subramaniam, Kamalraj
Sazali, Yaacob, Prof. Dr.
Abdul Hamid, Adom, Prof. Dr.
Hema, C. R.
author_sort Pandiyan, Paulraj Murugesa , Prof. Dr.
title EEG based detection of conductive and sensorineural hearing loss using artificial neural networks
title_short EEG based detection of conductive and sensorineural hearing loss using artificial neural networks
title_full EEG based detection of conductive and sensorineural hearing loss using artificial neural networks
title_fullStr EEG based detection of conductive and sensorineural hearing loss using artificial neural networks
title_full_unstemmed EEG based detection of conductive and sensorineural hearing loss using artificial neural networks
title_sort eeg based detection of conductive and sensorineural hearing loss using artificial neural networks
publisher Advanced Institute of Convergence IT
publishDate 2014
url http://dspace.unimap.edu.my:80/dspace/handle/123456789/34877
_version_ 1643797642444013568
score 13.222552

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