EEG based automated detection of auditory loss: A pilot study

Sriraam, N.

doi:10.1016/j.eswa.2011.07.064

Cited by 24 publications

(10 citation statements)

References 16 publications

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“…Being the feedback network, REN ensures effective training with early convergence and then ensuring the low computational requirement. With the MATLAB R13 environment, the network further yielded less computational time compared to the work reported earlier (Srinivasan et al 2007;Pravin et al 2010;Sriraam 2012Sriraam , 2013. It can be inferred from the results shown in Fig.…”

Section: Discussionmentioning

confidence: 91%

Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier

Raghu¹,

Kumar

2016

Cogn Neurodyn

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Electroencephalogram shortly termed as EEG is considered as the fundamental segment for the assessment of the neural activities in the brain. In cognitive neuroscience domain, EEG-based assessment method is found to be superior due to its non-invasive ability to detect deep brain structure while exhibiting superior spatial resolutions. Especially for studying the neurodynamic behavior of epileptic seizures, EEG recordings reflect the neuronal activity of the brain and thus provide required clinical diagnostic information for the neurologist. This specific proposed study makes use of wavelet packet based log and norm entropies with a recurrent Elman neural network (REN) for the automated detection of epileptic seizures. Three conditions, normal, pre-ictal and epileptic EEG recordings were considered for the proposed study. An adaptive Weiner filter was initially applied to remove the power line noise of 50 Hz from raw EEG recordings. Raw EEGs were segmented into 1 s patterns to ensure stationarity of the signal. Then wavelet packet using Haar wavelet with a five level decomposition was introduced and two entropies, log and norm were estimated and were applied to REN classifier to perform binary classification. The nonlinear Wilcoxon statistical test was applied to observe the variation in the features under these conditions. The effect of log energy entropy (without wavelets) was also studied. It was found from the simulation results that the wavelet packet log entropy with REN classifier yielded a classification accuracy of 99.70 % for normal-pre-ictal, 99.70 % for normal-epileptic and 99.85 % for pre-ictalepileptic.

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Section: Discussionmentioning

confidence: 91%

Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier

Raghu¹,

Kumar

2016

Cogn Neurodyn

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“…Auditory evoked potential response reflects the hearing perception level of an individual. The most of the reviewed authors [ 28 - 34 ] investigated in determining the hearing threshold level by identifying the ABR peaks and few authors [ 47 - 51 ] had analyzed entire AEP signal (see Table 1 ).…”

Section: Hearing Threshold Estimation From Aepmentioning

confidence: 99%

Auditory Evoked Potential Response and Hearing Loss: A Review

Paulraj¹,

Subramaniam²,

Yaccob³

et al. 2015

TOBEJ

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Hypoacusis is the most prevalent sensory disability in the world and consequently, it can lead to impede speech in human beings. One best approach to tackle this issue is to conduct early and effective hearing screening test using Electroencephalogram (EEG). EEG based hearing threshold level determination is most suitable for persons who lack verbal communication and behavioral response to sound stimulation. Auditory evoked potential (AEP) is a type of EEG signal emanated from the brain scalp by an acoustical stimulus. The goal of this review is to assess the current state of knowledge in estimating the hearing threshold levels based on AEP response. AEP response reflects the auditory ability level of an individual. An intelligent hearing perception level system enables to examine and determine the functional integrity of the auditory system. Systematic evaluation of EEG based hearing perception level system predicting the hearing loss in newborns, infants and multiple handicaps will be a priority of interest for future research.

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“…ABR hearing screening test has been implemented to detect the presence or absence of hearing loss in infants and children without estimating hearing threshold level [8][9]. In recent years, researchers have approached the problem of determining the hearing ability level by analyzing the complete waveform of AEP signal instead of searching and identifying the specific peaks [10][11] The term 'fractal' is used to indicate the objects whose complex irregularities cannot be characterized by traditional geometrical methods such examples as a snowflake curve and coastline curve. The fractional dimension (FD) refers to the dimension used to represent fractal characterization [12].…”

Section: Introductionmentioning

confidence: 99%

EEG based hearing perception level classification using fractal features

Paulraj

Subramaniam

Yaccob

et al. 2013

2013 International Conference on Advanced Computing and Communication Systems

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The primary focus of this study is to develop a hearing ability level assessment system using auditory evoked potential signals (AEP). AEP signal is a non-invasive tool that provides auditory pathway information content and its stimulated interactions with neurons. To record the hearing perception levels at different sound intensity levels, namely, 20 dB, 30 dB, 40 dB, 50 dB and 60 dB in both the ears of a normal subject, a simple AEP based hearing perception level protocol has been proposed. The detrended fluctuation analysis (DFA) has been used to estimate the fractal values of different hearing perception levels of the recorded AEP signals. The extracted fractal features were then associated to different hearing perception levels of a subject and neural network models were developed. The maximum classification accuracy of the developed neural network models for the left and right ears are observed as 78.57% and 80.71% respectively. From the classification accuracy, it has been inferred that the neural network models are able to discriminate the five distinct hearing perception levels of a normal hearing person.

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EEG based automated detection of auditory loss: A pilot study

Cited by 24 publications

References 16 publications

Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier

Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier

Auditory Evoked Potential Response and Hearing Loss: A Review

EEG based hearing perception level classification using fractal features

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