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Comparative Study of Fuzzy Entropy with Relative Spike Amplitude Features for Recognizing Wake-Sleep Stage 1 EEGs

Comparative Study of Fuzzy Entropy with Relative Spike Amplitude Features for Recognizing Wake-Sleep Stage 1 EEGs

Natarajan Sriraam, B. R. Purnima, Uma Maheswari Krishnaswamy
Copyright: © 2015 |Volume: 4 |Issue: 2 |Pages: 14
ISSN: 2161-1610|EISSN: 2161-1629|EISBN13: 9781466680487|DOI: 10.4018/IJBCE.2015070102
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MLA

Sriraam, Natarajan, et al. "Comparative Study of Fuzzy Entropy with Relative Spike Amplitude Features for Recognizing Wake-Sleep Stage 1 EEGs." IJBCE vol.4, no.2 2015: pp.12-25. http://doi.org/10.4018/IJBCE.2015070102

APA

Sriraam, N., Purnima, B. R., & Krishnaswamy, U. M. (2015). Comparative Study of Fuzzy Entropy with Relative Spike Amplitude Features for Recognizing Wake-Sleep Stage 1 EEGs. International Journal of Biomedical and Clinical Engineering (IJBCE), 4(2), 12-25. http://doi.org/10.4018/IJBCE.2015070102

Chicago

Sriraam, Natarajan, B. R. Purnima, and Uma Maheswari Krishnaswamy. "Comparative Study of Fuzzy Entropy with Relative Spike Amplitude Features for Recognizing Wake-Sleep Stage 1 EEGs," International Journal of Biomedical and Clinical Engineering (IJBCE) 4, no.2: 12-25. http://doi.org/10.4018/IJBCE.2015070102

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Abstract

Electroencephalogram (EEG) based sleep stage analysis considered to be the gold standard method for assessment of sleep architecture. Of importance, transition between the first two stages, wake-sleep stage 1 found to be reliable quantitative tool for drowsiness and fatigue detection. The selection of appropriate feature pattern for EEGs is a quite challenging task due to its non-linear and non-stationary nature of the EEG signals. This research work attempts to provide a comparative study of time influence of time domain feature, relative spike amplitude (RSA) with entropy feature, fuzzy entropy(FE) for recognizing the transition between wake and sleep stage 1. EEGs extracted from offline polysomnography database is used and the extracted RSA and FE wake-sleep stage 1 derived EEG features are further classified using a feedback recurrent Elman neural network (REN) classifier. EEGs are segmented into 1s pattern. Simulation of the REN classifier revealed that the FE feature with REN yields a CA of 99.6% compared to that of with RSA feature.

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