Marina Ronzhinaa, 
, 
, Oto Janoušeka, d, , Jana Kolárováa, e, , Marie Novákováb, g, , Petr Honzíkc, h, , Ivo Provazníka, f, a Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Kolejní 4, Brno 61200, Czech Republicb Department of Physiology, Faculty of Medicine, Masaryk University, Kamenice 753/5, Brno 62500, Czech Republicc Department of Control and Instrumentation, Faculty of Electrical Engineering and Communication, Brno University of Technology, Kolejní 4, Brno 61200, Czech RepublicReceived 18 March 2011. Revised 30 June 2011. Accepted 30 June 2011. Available online 24 October 2011.View full text Rapid development of computer technologies leads to the intensive automation of many different processes traditionally performed by human experts. One of the spheres characterized by the introduction of new high intelligence technologies substituting analysis performed by humans is sleep scoring. This refers to the classification task and can be solved – next to other classification methods – by use of artificial neural networks (ANN). ANNs are parallel adaptive systems suitable for solving of non-linear problems. Using ANN for automatic sleep scoring is especially promising because of new ANN learning algorithms allowing faster classification without decreasing the performance. Both appropriate preparation of training data as well as selection of the ANN model make it possible to perform effective and correct recognizing of relevant sleep stages. Such an approach is highly topical, taking into consideration the fact that there is no automatic scorer utilizing ANN technology available at present.
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Fig. 1. Schematic representation. (a) Single neuron with vector input. (b) One-layer network with m neurons.View Within ArticleFig. 2. Transfer functions. (a) Log-sigmoid. (b) Tan-sigmoid. (c) Hard limit. (d) Linear.View Within ArticleFig. 3. Extraction of the 4-elements features vector from EEG epoch. PSD – power spectral density, d, ?, a, ß – delta, theta, alpha and beta bands, respectively, drel, ?rel, arel, ßrel – relative power values for delta, theta, alpha and beta bands, respectively.View Within ArticleTable 1. Summary of artificial neural network (ANN) based systems for sleep scoring. BP: backpropagation, EEG: electroencephalogram, EMG: electromyogram, EOG: electrooculogram (LEOG, REOG: left, right EOG, respectively), FC: fully connected, FT: Fourier transform, MLNN: multilayer neural network, MLP: multilayer perceptron, MT: movement time, RatP: ratio power, REM, rapid eye movement, RMS: root mean square, RP: relative power, RUM, LM: Rumelhart (gradient descent without momentum) and Levenberge-Marquardt learning algorithm, respectively, S1, S2, S3 and S4: see section “Polygraphic data and visual sleep scoring” for definitions, SD: standard deviation, SOM: self-organizing map, SWS: slow wave sleep, TP: total power, W: wakefulness, WT: wavelet transform. Description of sleep stages is according to R&K and AASM.
View Within ArticleTable 2. Output neurons of proposed artificial neural network models. REM: rapid eye movement, S*: stage involving four stages of non-REM sleep (other sleep stages are according to R&K), S1, S2, S3 and S4: see section “Polygraphic data and visual sleep scoring” for definitions, W: wakefulness.View Within ArticleTable 3. Results of sleep scoring obtained by proposed artificial neural network (ANN) models. EEG: electroencephalogram, REM: rapid eye movement, RP, relative power, S: stage involving the four stages of non-REM and REM sleep, S*: stage involving the four stages of non-REM sleep (other sleep stages are according to R&K), S1, S2, S3 and S4: see section “Polygraphic data and visual sleep scoring” for definitions, W: wakefulness.View Within ArticleCopyright © 2011 Elsevier Ltd. All rights reserved.prs.rt('data_end');
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