Safe-level SMOTE method for handling the class imbalanced problem in electroencephalography dataset of adult anxious state

• Published in: Biomedical signal processing and control Vol. 83; p. 104649
• Main Authors: Syakiylla Sayed Daud, Syarifah Noor, Sudirman, Rubita, Wee Shing, Tee
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2023
• Subjects: Anxiety; Classifier; Decision tree; Features model; K-Nearest Neighbor; Performance metric; Safe-level Synthetic Minority Oversampling Technique; Support vector machine; TAR; Features model; Classifier; Support vector machine; FNR; SVM; Decision tree; COVID; ɑ; ABR; k-NN; SMOTE; TPR; Anxiety; DWT; PSD; RFECV; DASPS; EEG; ICA; K-Nearest Neighbor; RGB; FIR; DEAP; β; Hz; Performance metric; SampEn; θ; RMS; Safe-level Synthetic Minority Oversampling Technique; db; SAM

•An imbalanced Database for Anxious States based on Psychological stimulation classes led to poor classification performance of the anxious state.•The recent work employs the Safe-level Synthetic Minority Oversampling Technique to mitigate the class imbalance issue of the Database for Anxious States...