A study on feature selection using multi-domain feature extraction for automated k-complex detection

Figures

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  Figure 1. The k-complex detection flowchart of the proposed methods.

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  Figure 2. An filtered EEG signal [(A) is EEG signals with k-complex, and (B) represents EEG signals without k-complex].

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  Figure 3. The scheme of ReliefF. The dimension of feature subsets is reduced based feature selected of ReliefF, and the selected features are used for further analysis.

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  Figure 4. The scheme of CFS. The dimension of feature subsets is reduced based feature selected of CFS.

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  Figure 5. The scheme of SFS. The dimension of feature subsets is reduced based feature selected of SFS.

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  Figure 6. The scheme of INTERACT method. The dimension of feature subsets is reduced based feature selected of INTERACT.

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  Figure 7. Correlation coefficient and significance test for the different domain features with features of k-complex and non-k-complex (if the value of p below than 0.005, it is marked with **, and if the p-value is below than 0.05, it is marked with *. (A) is for spectral feature, and (B) is for chaotic features).

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  Figure 8. Comparison of J1 value between different features and subjects for k-complex detection.

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  Figure 9. The accuracy of multi-domain features based on decision trees model for each features. Each box represents the 25–75th percentiles, and central line is the median value, the tiny vertical lines extend to the most extreme data not considering as outliers, which are plotted individually.

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  Figure 10. Comparison of feature selection methods and without feature selection method using three detection algorithm (A) is for LDA algorithm, (B) is for LSVM algorithm, and (C) is for DT algorithm. No selected means that without any feature selection process.