Automatic recognition of sleep spindles in EEG by using artificial neural networks

In this paper, we introduce a two-stage procedure based on artificial neural networks for the automatic recognition of sleep spindles (SSs) in a multi-channel electroencephalographic signal. In the first stage, a discrete perceptron is used to eliminate definite non-SSs. The pre-classification done...

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Vydané v:Expert systems with applications Ročník 27; číslo 3; s. 451 - 458
Hlavní autori: Acir, Nurettin, Guzeli, Cuneyt
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.10.2004
Predmet:
Backpropagation algorithm
EEG
Pattern recognition
Sleep spindle recognition
Support vector machines
Support vector machines
Backpropagation algorithm
Pattern recognition
EEG
Sleep spindle recognition
ISSN:0957-4174, 1873-6793
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Popis
Shrnutí:In this paper, we introduce a two-stage procedure based on artificial neural networks for the automatic recognition of sleep spindles (SSs) in a multi-channel electroencephalographic signal. In the first stage, a discrete perceptron is used to eliminate definite non-SSs. The pre-classification done in the first stage not only reduces the computation time but also increases the overall detection performance of the procedure. In the second stage, the remaining SS candidates after pre-classification procedure are aimed to be separated from each other by an artificial neural network that would function as a post-classifier. Two different networks, i.e. a backpropagation multilayer perceptron and radial basis support vector machine (SVM), are proposed as the post-classifier and compared in terms of their classification performances. Visual evaluation, by two electroencephalographers (EEGers), of 19 channel EEG records of 6 subjects showed that the best performance is obtained with a radial basis SVM providing an average sensitivity of 94.6% and an average false detection rate of 4.0%.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2004.05.007