DWT-EMD Feature Level Fusion Based Approach over Multi and Single Channel EEG Signals for Seizure Detection
Brain Computer Interface technology enables a pathway for analyzing EEG signals for seizure detection. EEG signal decomposition, features extraction and machine learning techniques are more familiar in seizure detection. However, selecting decomposition technique and concatenation of their features...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-01-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/12/2/324 |
| _version_ | 1797481275900035072 |
|---|---|
| author | Gopal Chandra Jana Anupam Agrawal Prasant Kumar Pattnaik Mangal Sain |
| author_facet | Gopal Chandra Jana Anupam Agrawal Prasant Kumar Pattnaik Mangal Sain |
| author_sort | Gopal Chandra Jana |
| collection | DOAJ |
| description | Brain Computer Interface technology enables a pathway for analyzing EEG signals for seizure detection. EEG signal decomposition, features extraction and machine learning techniques are more familiar in seizure detection. However, selecting decomposition technique and concatenation of their features for seizure detection is still in the state-of-the-art phase. This work proposes DWT-EMD Feature level Fusion-based seizure detection approach over multi and single channel EEG signals and studied the usability of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) feature fusion with respect to individual DWT and EMD features over classifiers SVM, SVM with RBF kernel, decision tree and bagging classifier for seizure detection. All classifiers achieved an improved performance over DWT-EMD feature level fusion for two benchmark seizure detection EEG datasets. Detailed quantification results have been mentioned in the Results section. |
| first_indexed | 2024-03-09T22:12:26Z |
| format | Article |
| id | doaj.art-86c3b87628b0475892ab21f237e87399 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T22:12:26Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-86c3b87628b0475892ab21f237e873992023-11-23T19:30:09ZengMDPI AGDiagnostics2075-44182022-01-0112232410.3390/diagnostics12020324DWT-EMD Feature Level Fusion Based Approach over Multi and Single Channel EEG Signals for Seizure DetectionGopal Chandra Jana0Anupam Agrawal1Prasant Kumar Pattnaik2Mangal Sain3Interactive Technologies & Multimedia Research Lab, Department of Information Technology, CC-II, Indian Institute of Information Technology-Allahabad, Prayagraj 211015, IndiaInteractive Technologies & Multimedia Research Lab, Department of Information Technology, CC-II, Indian Institute of Information Technology-Allahabad, Prayagraj 211015, IndiaSchool of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, IndiaDivision of Computer Engineering, Dongseo University, 47 Jurye-Ro, Sasang-Gu, Busan 47011, KoreaBrain Computer Interface technology enables a pathway for analyzing EEG signals for seizure detection. EEG signal decomposition, features extraction and machine learning techniques are more familiar in seizure detection. However, selecting decomposition technique and concatenation of their features for seizure detection is still in the state-of-the-art phase. This work proposes DWT-EMD Feature level Fusion-based seizure detection approach over multi and single channel EEG signals and studied the usability of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) feature fusion with respect to individual DWT and EMD features over classifiers SVM, SVM with RBF kernel, decision tree and bagging classifier for seizure detection. All classifiers achieved an improved performance over DWT-EMD feature level fusion for two benchmark seizure detection EEG datasets. Detailed quantification results have been mentioned in the Results section.https://www.mdpi.com/2075-4418/12/2/324discrete wavelet transformempirical mode decompositionelectroencephalogramEEG classificationseizure detection |
| spellingShingle | Gopal Chandra Jana Anupam Agrawal Prasant Kumar Pattnaik Mangal Sain DWT-EMD Feature Level Fusion Based Approach over Multi and Single Channel EEG Signals for Seizure Detection Diagnostics discrete wavelet transform empirical mode decomposition electroencephalogram EEG classification seizure detection |
| title | DWT-EMD Feature Level Fusion Based Approach over Multi and Single Channel EEG Signals for Seizure Detection |
| title_full | DWT-EMD Feature Level Fusion Based Approach over Multi and Single Channel EEG Signals for Seizure Detection |
| title_fullStr | DWT-EMD Feature Level Fusion Based Approach over Multi and Single Channel EEG Signals for Seizure Detection |
| title_full_unstemmed | DWT-EMD Feature Level Fusion Based Approach over Multi and Single Channel EEG Signals for Seizure Detection |
| title_short | DWT-EMD Feature Level Fusion Based Approach over Multi and Single Channel EEG Signals for Seizure Detection |
| title_sort | dwt emd feature level fusion based approach over multi and single channel eeg signals for seizure detection |
| topic | discrete wavelet transform empirical mode decomposition electroencephalogram EEG classification seizure detection |
| url | https://www.mdpi.com/2075-4418/12/2/324 |
| work_keys_str_mv | AT gopalchandrajana dwtemdfeaturelevelfusionbasedapproachovermultiandsinglechanneleegsignalsforseizuredetection AT anupamagrawal dwtemdfeaturelevelfusionbasedapproachovermultiandsinglechanneleegsignalsforseizuredetection AT prasantkumarpattnaik dwtemdfeaturelevelfusionbasedapproachovermultiandsinglechanneleegsignalsforseizuredetection AT mangalsain dwtemdfeaturelevelfusionbasedapproachovermultiandsinglechanneleegsignalsforseizuredetection |