Safe-level SMOTE method for handling the class imbalanced problem in electroencephalography dataset of adult anxious state. (May 2023)

• Record Type: Journal Article
• Authors: Syakiylla Sayed Daud, Syarifah Noor; Sudirman, Rubita; Wee Shing, Tee
• Is Part Of: Biomedical signal processing and control. Volume 83(2023)
• Publication Date: 2023-05
• Subjects: Anxiety -- Features model -- Classifier -- Performance metric -- K-Nearest Neighbor -- Support vector machine -- Decision tree -- Safe-level Synthetic Minority Oversampling Technique; ɑ Alpha band -- β Beta band -- θ Theta band -- ABR Alpha/Beta ratio -- COVID Coronavirus disease -- DASPS Database for anxious states based on psychological stimulation -- db Daubechies -- DEAP Database for emotion analysis using physiological signals -- DWT Discrete wavelet transform -- EEG Electroencephalography -- FIR Finite impulse response -- FNR False negative rate -- Hz Hertz -- ICA Independent component analysis -- k-NN k-Nearest Neighbor -- PSD Power spectral density -- RFECV Recursive feature elimination -- RGB Red, green and blue -- RMS Root mean square -- SAM Self-Assessment Manikin -- SampEn Sample entropy -- SMOTE Synthetic Minority Oversampling Technique -- SVM Support vector machine -- TAR Theta/Alpha ratio -- TPR True positive rate; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2023.104649
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: 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 based on Psychological stimulation with superior classification performance. The synthesized Database for Anxious States based on Psychological stimulation achieved a maximum accuracy of 89.5% and the highest precision of 89.7% using a K-Nearest Neighbor classifier. Abstract: Anxiety disorder is a mental state in which a person experiences excessive worry, fear, nervousness, and apprehension. Measuring brain signals using the electroencephalography (EEG) modality is one of the ways to detect anxiety. However, an imbalanced EEG dataset class distribution among the existing issues with this method degrades the classification performance of the anxiety state. Therefore, the goal of this research is to improve classification performance by balancing the EEG dataset using a Safe-level Synthetic Minority Oversampling Technique (Safe-level SMOTE). In this work, a freely accessible Database for Anxious States based on Psychological stimulation (DASPS) with 14 EEG channels recorded via headset Emotiv Epoc was employed. The raw EEG signals contaminated with noises were filtered with multiple filtration methods before being further processed. The EEG features were extracted in the time domain, frequency domain, and time–frequency domain for model classification. The features model with the most optimal classification performance was then processed using a sampling technique, and a Safe-level SMOTE based nearest neighbor value of 5 before being classified using k-Nearest Neighbor (k-NN), support vector machine (SVM), and decision tree. Finally, the performance of the dataset was validated using k-fold cross-validation and confusion matrix performance metrics as well as recognition of the subject's anxiety state. The proposed model indicated that the k-NN achieved the maximum accuracy of 89.5% and the highest precision of 89.7% for the dataset with the enhanced class distribution. The performance of the suggested method with Safe-level SMOTE demonstrates its superiority in recognizing anxiety states compared to existing methods without Safe-level SMOTE.