Harmonic Parameters with HHT and Wavelet Transform for Automatic Sleep Stages Scoring

Previously, harmonic parameters (HPs) have been selected as features extracted from EEG signals for automatic sleep scoring. However, in previous studies, only one HP parameter was used, which were directly extracted from the whole epoch of EEG signal. In this study, two different transformations were applied to extract HPs from EEG signals: Hilbert-Huang transform (HHT) and wavelet transform (WT). EEG signals are decomposed by the two transformations; and features were extracted from different components. Twelve parameters (four sets of HPs) were extracted. Some of the parameters are highly diverse among different stages. Afterward, HPs from two transformations were used to building a rough sleep stages scoring model using the classifier SVM. The performance of this model is about 78% using the features obtained by our proposed extractions. Our results suggest that these features may be useful for automatic sleep stages scoring.

Authors:

• Wei-Chih Tang
• Shih-Wei Lu
• Chih-Mong Tsai
• Cheng-Yan Kao
• Hsiu-Hui Lee

Keywords:

• EEG
• harmonic parameter
• Hilbert-Huang transform
• sleep stages
• wavelet transform.

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