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- W2547524689 abstract "An adaptive biosignal analysis system for the detection of microsleep events is presented. The system was applied to the electroencephalogram and electrooculogram recorded of 23 young volunteers while performing monotonic overnight driving in our real car driving simulation laboratory. Biosignals during clear observable microsleep and non-microsleep events were processed and classified. Besides the commonly applied Periodogram method to estimate power spectral densities we utilized the recently established method of Delay Vector Variance. The obtained feature set was used as input vectors of populations of Learning Vector Quantization networks which were evolved by Genetic Algorithms. The results were compared with results from best performing Support Vector Machines. Fusion of all recorded signals and of both types of features led to empirical test errors down to 11.2 %. It is shown that the proposed methodology is able to detect, but not to predict immediately oncoming events." @default.
- W2547524689 created "2016-11-11" @default.
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- W2547524689 date "2005-01-01" @default.
- W2547524689 modified "2023-09-27" @default.
- W2547524689 title "Microsleep Detection in Electrophysiological Signals" @default.
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- W2547524689 doi "https://doi.org/10.5220/0001195701020109" @default.
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