CGP17Pat: Automated Schizophrenia Detection Based on a Cyclic Group of Prime Order Patterns Using EEG Signals

Figures

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  Figure 1. Schematic diagram of the proposed CGP17Pat-based schizophrenia detection model.

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  Figure 2. The created eight patterns using CGP17, where each pattern is named as P; by using each pattern, 256 features are extracted and our presented CGP17Pat uses these eight patterns together: (a) patterns 1–4; (b) patterns 5–8.

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  Figure 3. The graphical summarization of the presented CGP17Pat. Here, P denotes patterns (see Table 2), and each pattern extracts 256 features. Then, these feature vectors are merged, and 2048 (=256 × 8) features are created.

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  Figure 4. The lengths of the optimal feature vectors chosen by INCA.

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  Figure 5. Classification accuracies of the decision tree (DT), quadratic discriminant (QD), logistic regression (LR), naive Bayes (NB), support vector machine (SVM), Fine kNN (kNN), bagged tree (BT), ensemble subspace kNN (ESkNN), and artificial neural network (ANN) for the Fp2 channel with 10-fold cross-validation.

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  Figure 6. The obtained comparative results according to the validation technique.

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  Figure 7. Voted results: confusion matrices of the presented CGP17Pat-based EEG classification model using (a) 10-fold cross-validation and (b) LOSO cross-validation.