1996
DOI: 10.1117/12.255307
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<title>Classification of epileptic EEG using neural network and wavelet transform</title>

Abstract: One of the major conthbutions of electroencephalography has been its application in the diagnosis and clinical evaluation of epilepsy [1]. The interpretation of the EEG is achieved through visual inspection by a trained electroencephalographer. However, descriptions of rules used during the visual analysis of data are often subjective and can vary from one reader to another. Computerized methods are a means to standardize this process. In recent years, much effort has been made to develop such methods that can… Show more

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Cited by 8 publications
(7 citation statements)
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References 11 publications
(12 reference statements)
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“…Examples of good results with this method in the EEG context can be found, for example, in some other studies. 17,[23][24][25][26] In our experiment, a wavelets-based extraction was implemented by using the signals coming from the 63 electrodes and sampled at 100 Hz. For each channel, the wavelets expansion in terms of the Morlet family of the total sample length (5.5 seconds) were computed and the coefficients corresponding to a frequencies interval of 1 to 30 Hz with a frequency step of 0.25 Hz and a time step of 0.01 seconds were kept.…”
Section: • • Wavelets-based Technique: This Is Another Standardmentioning
confidence: 99%
“…Examples of good results with this method in the EEG context can be found, for example, in some other studies. 17,[23][24][25][26] In our experiment, a wavelets-based extraction was implemented by using the signals coming from the 63 electrodes and sampled at 100 Hz. For each channel, the wavelets expansion in terms of the Morlet family of the total sample length (5.5 seconds) were computed and the coefficients corresponding to a frequencies interval of 1 to 30 Hz with a frequency step of 0.25 Hz and a time step of 0.01 seconds were kept.…”
Section: • • Wavelets-based Technique: This Is Another Standardmentioning
confidence: 99%
“…This may be a potential bottleneck of the training procedure, particularly when both and are large. While applying the multistream EKF procedure to a variety of real-world tasks, we have, however, experienced no problems [3], [28], [29]. We have found singular-value decomposition best for inverting up to 100 100 elements [9].…”
Section: Recurrent Neural Network and Its Trainingmentioning
confidence: 99%
“…For large data sets, this advantage increases. One-stream training fails for sets of several thousand vectors [28], [29]. We have used multistream EKF training of RNN for predicting trends of several stocks.…”
Section: Recurrent Neural Network and Its Trainingmentioning
confidence: 99%
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“…In order to overcome these difficulties, alternative measures reflecting short-term signal “textural complexity” changes that precede seizures have been employed by a number of investigators within a linear system framework. For example, Petrosian et al (1996, 1997, 2000) have explored the ability of specifically designed and trained recurrent neural networks (RNN) combined with wavelet decomposition preprocessing to predict seizures. The RNN approach is particularly useful for the task of patient specific EEG prediction because it can be trained in a straightforward manner, is able to implement extremely nonlinear decision boundaries and possesses memory of previous states.…”
Section: Introductionmentioning
confidence: 99%