Research Information System
International Conference on Computer Science and Engineering, Antalya, Turkey, 5 - 08 October 2017, pp.348-353, (Full Text)
Sleep staging is one of the important areas which
is used to diagnose several diseases. People try to obtain models
to carry out this operation without human interaction due to
the time-consuming and complex nature of classification process.
Most of the prior studies use concatenation of the extracted
features from the electroencephalography (EEG) signals to obtain
a single classifier. However, concatenating different feature views
may not always yield better classification performance. This pa-
per proposes a combination of kernels using the genetic algorithm
based weight optimization process for sleep stage classification
instead of concatenation. Unlike the previous works, our novelty
is combining different feature views in a new structure with
optimized kernel weights which are obtained from the genetic
algorithm. In the proposed model SVM classifiers are trained
by distinct feature views namely wavelet decomposition(DWT),
autoregressive model based and frequency based energy features.
Weighted linear combination of the single kernels is used to
construct a new kernel and the performance of the model
is compared with traditional kernel function. Experiments are
carried out on 10 different patients. The average accuracy of
the experiments is considered as final accuracy. The results
show that the proposed architecture increases the performance
up to approximately 86 % on average. The proposed structure
fits better for multi-source data, unlike traditional single kernel
methods.