Jose Ramon Sepulveda Sanchis⁽¹⁾, Juan Guerrero⁽²⁾, EMILIO SORIA OLIVAS⁽³⁾, Antonio Benetó⁽⁴⁾, Enriqueta Gómez⁽⁵⁾, Antonio J. Serrano López^(6)
(1)universidad de valencia - valencia. Spain
⁽²⁾Departamento de Electrónica. Universidad de Valencia - Burjassot. Spain
⁽³⁾DPTO INGENIERÍA ELECTRONICA. FACULTAD DE FISICAS - BURJASSOT/VALENCIA. Spain
⁽⁴⁾⁽⁵⁾Hospital la Fé - Valencia. Spain
⁽⁶⁾Dpto. Electrónica. Universidad de Valencia - Burjasot. Spain

	[Neuroscience]	[Health Informatics]

INTRODUCTION

In the processing of biosignals most of the problems arise due to incomplete data or a misunderstanding of data, yielding unreliable results and a subjective evaluation. In the type of data that we are concerned with (EEG), the electrical currents generated in the cortex are too small and may be modelled only poorly. Furthermore, the large size of scalp electrodes and the effects of muscle and instrument noise make EEG analysis a complex task.

The aim of this work is to get an automatic method for the detection of arousal by using EEG. Arousal is a phenomenon that normally lasts for a few seconds without causing awakenings or changes in the sleep stage. Arousals can be identified visually in the polysomnographic recordings, and are characterized by abrupt changes in the electroencephalographic(EEG) frequency, suggestive of an awake state and /or brief increases in the electromyographic (EMG) amplitude ⁽¹⁾. Usually, the detection of such phenomena is performed manually by an expert; this method is cumbersome and highly time consuming, which suggests automatization.

The important fact is that the arousals result in fragmented, rather than shortened, sleep. As with shortened sleep, it has been demonstrated that sleep fragmentation can lead to increased sleepiness during the day. Equally important, this tool for the detection of arousals suggests applications to neurophysiological studies regarding the normal level of arousal in a healthy patient, as well as concerning the co-occurrence of arousal patterns with sleep disorders or other neurophisiological pathologies (e.g. apnoea, narcolepsy,catalepsy,…).

A method for the automatic detection of arousals during sleep, is described using neural networks. Due to the non linearity of the phenomenon, neural networks would be a suitable solution.A multilayer perceptron with the backpropagation algorithm is used. For the training, preprocessed data is used to train the neural network, with arousal and nonarousal cases.

Discussion Board

Any Comment to this presentation?

[ABSTRACT] [INTRODUCTION] [AROUSAL DETECTION SYSTEM] [RESULTS] [CONCLUSIONS] [REFERENCES] [Discussion Board]

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	[Neuroscience]	[Health Informatics]

Jose Ramon Sepulveda Sanchis, Juan Guerrero, EMILIO SORIA OLIVAS, Antonio Benetó, Enriqueta Gómez, Antonio J. Serrano López
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Last update: 14/01/00