An Improved Decision Support System for Identification of Abnormal EEG Signals Using a 1D Convolutional Neural Network and Savitzky-Golay Filtering
Medical experts employ electroencephalography (EEG) for analyzing the electrical activity in the brain to infer disorders. However, the time costs of human experts are very high, and the examination of EEGs by such experts, therefore, accounts for a plethora of medical resources. In this study, an i...
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2021-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/9638672/ |
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| author | Unmesh Shukla Geetika Jain Saxena Manish Kumar Anil Singh Bafila Amit Pundir Sanjeev Singh |
| author_facet | Unmesh Shukla Geetika Jain Saxena Manish Kumar Anil Singh Bafila Amit Pundir Sanjeev Singh |
| author_sort | Unmesh Shukla |
| collection | DOAJ |
| description | Medical experts employ electroencephalography (EEG) for analyzing the electrical activity in the brain to infer disorders. However, the time costs of human experts are very high, and the examination of EEGs by such experts, therefore, accounts for a plethora of medical resources. In this study, an improved one-dimensional CNN-only system of 25 layers has been proposed to identify abnormal and normal adult EEG signals using a single EEG montage without using any explicit feature extraction technique. Most of the previous systems based on deep learning, that have been proposed to solve this problem, use extremely deep architectures containing very large numbers of layers. This study also presents an independent preprocessing module that has been exhaustively evaluated for optimal parameters with the target of adult EEG signal classification. The achieved accuracy of the proposed classifier as a part of the decision support system is 82.24%, which is a substantial improvement of ~3% over the previous best reported classifier of comparable depth. The system also exhibits significantly higher F1-score and sensitivity as well as lower loss. The proposed system is intended to be a part of an expert system for overall brain health evaluation. |
| format | Article |
| id | doaj-art-69f6aad583e14f97b432e87a30a7c99e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-69f6aad583e14f97b432e87a30a7c99e2025-08-25T23:11:19ZengIEEEIEEE Access2169-35362021-01-01916349216350310.1109/ACCESS.2021.31333269638672An Improved Decision Support System for Identification of Abnormal EEG Signals Using a 1D Convolutional Neural Network and Savitzky-Golay FilteringUnmesh Shukla0https://orcid.org/0000-0001-6738-8933Geetika Jain Saxena1https://orcid.org/0000-0002-5828-8049Manish Kumar2Anil Singh Bafila3https://orcid.org/0000-0002-3392-2410Amit Pundir4Sanjeev Singh5https://orcid.org/0000-0002-2416-7011Institute of Informatics and Communication, University of Delhi South Campus, New Delhi, IndiaDepartment of Electronics, Maharaja Agrasen College, University of Delhi, New Delhi, IndiaSchool of Computer and Information Sciences, Indira Gandhi National Open University, New Delhi, IndiaInstitute of Informatics and Communication, University of Delhi South Campus, New Delhi, IndiaDepartment of Electronics, Maharaja Agrasen College, University of Delhi, New Delhi, IndiaInstitute of Informatics and Communication, University of Delhi South Campus, New Delhi, IndiaMedical experts employ electroencephalography (EEG) for analyzing the electrical activity in the brain to infer disorders. However, the time costs of human experts are very high, and the examination of EEGs by such experts, therefore, accounts for a plethora of medical resources. In this study, an improved one-dimensional CNN-only system of 25 layers has been proposed to identify abnormal and normal adult EEG signals using a single EEG montage without using any explicit feature extraction technique. Most of the previous systems based on deep learning, that have been proposed to solve this problem, use extremely deep architectures containing very large numbers of layers. This study also presents an independent preprocessing module that has been exhaustively evaluated for optimal parameters with the target of adult EEG signal classification. The achieved accuracy of the proposed classifier as a part of the decision support system is 82.24%, which is a substantial improvement of ~3% over the previous best reported classifier of comparable depth. The system also exhibits significantly higher F1-score and sensitivity as well as lower loss. The proposed system is intended to be a part of an expert system for overall brain health evaluation.https://ieeexplore.ieee.org/document/9638672/Electroencephalographymachine learningdecision support systemsconvolutional neural networksdata preprocessingSavitzky-Golay filtering |
| spellingShingle | Unmesh Shukla Geetika Jain Saxena Manish Kumar Anil Singh Bafila Amit Pundir Sanjeev Singh An Improved Decision Support System for Identification of Abnormal EEG Signals Using a 1D Convolutional Neural Network and Savitzky-Golay Filtering IEEE Access Electroencephalography machine learning decision support systems convolutional neural networks data preprocessing Savitzky-Golay filtering |
| title | An Improved Decision Support System for Identification of Abnormal EEG Signals Using a 1D Convolutional Neural Network and Savitzky-Golay Filtering |
| title_full | An Improved Decision Support System for Identification of Abnormal EEG Signals Using a 1D Convolutional Neural Network and Savitzky-Golay Filtering |
| title_fullStr | An Improved Decision Support System for Identification of Abnormal EEG Signals Using a 1D Convolutional Neural Network and Savitzky-Golay Filtering |
| title_full_unstemmed | An Improved Decision Support System for Identification of Abnormal EEG Signals Using a 1D Convolutional Neural Network and Savitzky-Golay Filtering |
| title_short | An Improved Decision Support System for Identification of Abnormal EEG Signals Using a 1D Convolutional Neural Network and Savitzky-Golay Filtering |
| title_sort | improved decision support system for identification of abnormal eeg signals using a 1d convolutional neural network and savitzky golay filtering |
| topic | Electroencephalography machine learning decision support systems convolutional neural networks data preprocessing Savitzky-Golay filtering |
| url | https://ieeexplore.ieee.org/document/9638672/ |
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