Multistage anxiety state recognition based on EEG signal using Safe-Level SMOTE
Anxiety is a complicated emotional condition that has a detrimental effect on people’s physical and mental health. It is critical to accurately recognize anxiety levels in early stage. The anxiety can be detected by pattern of brain signal using brain imaging tools. However, the common problem with...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2023
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| Online Access: | http://eprints.utm.my/107890/1/SyarifahNoorSyakiylla2023_MultistageAnxietyStateRecognition.pdf |
| _version_ | 1817854199297212416 |
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| author | Tee, Wee Shing Sudirman, Rubita Sayed Daud, Syarifah Noor Syakiylla Abdul Razak, Mohd. Azhar Zakaria, Nor Aini Mahmood, Nasrul Humaimi |
| author_facet | Tee, Wee Shing Sudirman, Rubita Sayed Daud, Syarifah Noor Syakiylla Abdul Razak, Mohd. Azhar Zakaria, Nor Aini Mahmood, Nasrul Humaimi |
| author_sort | Tee, Wee Shing |
| collection | ePrints |
| description | Anxiety is a complicated emotional condition that has a detrimental effect on people’s physical and mental health. It is critical to accurately recognize anxiety levels in early stage. The anxiety can be detected by pattern of brain signal using brain imaging tools. However, the common problem with dataset acquired from brain is imbalanced class distribution. Hence, the purpose of this work is to mitigate the imbalanced class distribution issue by removing data outlier and using improved Synthetic Minority Oversampling Technique (SMOTE) for improving the classification performance. This work used of the freely accessible Database for Anxious States based on Psychological stimulation (DASPS) that comprises of 14 channels electroencephalography (EEG) signal. It acquired from 23 subjects when they were exposed to psychological stimuli that elicited fear. The DASPS need to be processed for removing noises, extracting important features and sampling with Safe-level SMOTE method. Then, the processed DASPS was categorized into three types of model: Model A, Model B, and Model C. The feature Model C from enhanced DASPS class distribution obtained the precision of 89.7% and accuracy of 89.5% using optimized k-nearest neighbour (k-NN) algorithm. The proposed method showed outstanding classification performance than others existing methods in recognizing multistage anxiety. |
| first_indexed | 2024-12-08T06:54:20Z |
| format | Conference or Workshop Item |
| id | utm.eprints-107890 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-12-08T06:54:20Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | utm.eprints-1078902024-10-08T06:54:27Z http://eprints.utm.my/107890/ Multistage anxiety state recognition based on EEG signal using Safe-Level SMOTE Tee, Wee Shing Sudirman, Rubita Sayed Daud, Syarifah Noor Syakiylla Abdul Razak, Mohd. Azhar Zakaria, Nor Aini Mahmood, Nasrul Humaimi TK Electrical engineering. Electronics Nuclear engineering Anxiety is a complicated emotional condition that has a detrimental effect on people’s physical and mental health. It is critical to accurately recognize anxiety levels in early stage. The anxiety can be detected by pattern of brain signal using brain imaging tools. However, the common problem with dataset acquired from brain is imbalanced class distribution. Hence, the purpose of this work is to mitigate the imbalanced class distribution issue by removing data outlier and using improved Synthetic Minority Oversampling Technique (SMOTE) for improving the classification performance. This work used of the freely accessible Database for Anxious States based on Psychological stimulation (DASPS) that comprises of 14 channels electroencephalography (EEG) signal. It acquired from 23 subjects when they were exposed to psychological stimuli that elicited fear. The DASPS need to be processed for removing noises, extracting important features and sampling with Safe-level SMOTE method. Then, the processed DASPS was categorized into three types of model: Model A, Model B, and Model C. The feature Model C from enhanced DASPS class distribution obtained the precision of 89.7% and accuracy of 89.5% using optimized k-nearest neighbour (k-NN) algorithm. The proposed method showed outstanding classification performance than others existing methods in recognizing multistage anxiety. 2023 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/107890/1/SyarifahNoorSyakiylla2023_MultistageAnxietyStateRecognition.pdf Tee, Wee Shing and Sudirman, Rubita and Sayed Daud, Syarifah Noor Syakiylla and Abdul Razak, Mohd. Azhar and Zakaria, Nor Aini and Mahmood, Nasrul Humaimi (2023) Multistage anxiety state recognition based on EEG signal using Safe-Level SMOTE. In: 1st International Conference on Electronic and Computer Engineering, ECE 2023, 4 July 2023 - 5 July 2023, Virtual, UTM Johor Bahru, Johor, Malaysia. http://dx.doi.org/10.1088/1742-6596/2622/1/012010 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Tee, Wee Shing Sudirman, Rubita Sayed Daud, Syarifah Noor Syakiylla Abdul Razak, Mohd. Azhar Zakaria, Nor Aini Mahmood, Nasrul Humaimi Multistage anxiety state recognition based on EEG signal using Safe-Level SMOTE |
| title | Multistage anxiety state recognition based on EEG signal using Safe-Level SMOTE |
| title_full | Multistage anxiety state recognition based on EEG signal using Safe-Level SMOTE |
| title_fullStr | Multistage anxiety state recognition based on EEG signal using Safe-Level SMOTE |
| title_full_unstemmed | Multistage anxiety state recognition based on EEG signal using Safe-Level SMOTE |
| title_short | Multistage anxiety state recognition based on EEG signal using Safe-Level SMOTE |
| title_sort | multistage anxiety state recognition based on eeg signal using safe level smote |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utm.my/107890/1/SyarifahNoorSyakiylla2023_MultistageAnxietyStateRecognition.pdf |
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