Optimal set of EEG features in infant sleep stage classification
This paper evaluates six classification algorithms to
assess the importance of individual EEG rhythms in the context of automatic
classification of infant sleep. EEG features were obtained by Fourier
transform and by a novel technique based on the empirical mode decomposition
and generalized zero crossing method. Of six evaluated classification
algorithms, the best classification results were obtained with the support
vector machine for the combination of all presented features from four EEG
channels. Three methods of attribute ranking were assessed: relief,
principal component analysis, and wrapper-based optimized attribute weights.
The outcomes revealed that the optimal selection of features requires one
feature from every significant frequency band, either a spectral feature or
a frequency dynamic feature. This means that reducing the number of features
will have a minimal impact on the classification accuracy.
Keywords:
Empirical mode decomposition, generalized zero crossing sleep classification, feature selection, support vector machine,
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- ISSN: 1300-0632
- Yayın Aralığı: Yılda 6 Sayı
- Yayıncı: TÜBİTAK