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

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 existin...

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Main Authors: Sayed Daud, Syarifah Noor Syakiylla, Sudirman, Rubita, Tee, Wee Shing
Format: Article
Published: Elsevier Ltd 2023
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/105512/
http://dx.doi.org/10.1016/j.bspc.2023.104649
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spelling my.utm.1055122024-04-30T08:08:52Z http://eprints.utm.my/105512/ Safe-level SMOTE method for handling the class imbalanced problem in electroencephalography dataset of adult anxious state Sayed Daud, Syarifah Noor Syakiylla Sudirman, Rubita Tee, Wee Shing TK Electrical engineering. Electronics Nuclear engineering 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. Elsevier Ltd 2023 Article PeerReviewed Sayed Daud, Syarifah Noor Syakiylla and Sudirman, Rubita and Tee, Wee Shing (2023) Safe-level SMOTE method for handling the class imbalanced problem in electroencephalography dataset of adult anxious state. Biomedical Signal Processing and Control, 83 (NA). NA-NA. ISSN 1746-8094 http://dx.doi.org/10.1016/j.bspc.2023.104649 DOI : 10.1016/j.bspc.2023.104649
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Sayed Daud, Syarifah Noor Syakiylla
Sudirman, Rubita
Tee, Wee Shing
Safe-level SMOTE method for handling the class imbalanced problem in electroencephalography dataset of adult anxious state
description 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.
format Article
author Sayed Daud, Syarifah Noor Syakiylla
Sudirman, Rubita
Tee, Wee Shing
author_facet Sayed Daud, Syarifah Noor Syakiylla
Sudirman, Rubita
Tee, Wee Shing
author_sort Sayed Daud, Syarifah Noor Syakiylla
title Safe-level SMOTE method for handling the class imbalanced problem in electroencephalography dataset of adult anxious state
title_short Safe-level SMOTE method for handling the class imbalanced problem in electroencephalography dataset of adult anxious state
title_full Safe-level SMOTE method for handling the class imbalanced problem in electroencephalography dataset of adult anxious state
title_fullStr Safe-level SMOTE method for handling the class imbalanced problem in electroencephalography dataset of adult anxious state
title_full_unstemmed Safe-level SMOTE method for handling the class imbalanced problem in electroencephalography dataset of adult anxious state
title_sort safe-level smote method for handling the class imbalanced problem in electroencephalography dataset of adult anxious state
publisher Elsevier Ltd
publishDate 2023
url http://eprints.utm.my/105512/
http://dx.doi.org/10.1016/j.bspc.2023.104649
_version_ 1797906035873153024
score 13.211869

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