Automatic seizure detection using orthogonal matching pursuit, discrete wavelet transform, and entropy based features of EEG signals
Copyright © 2021 Elsevier Ltd. All rights reserved..
BACKGROUND AND OBJECTIVE: Epilepsy is a prevalent disorder that affects the central nervous system, causing seizures. In the current study, a novel algorithm is developed using electroencephalographic (EEG) signals for automatic seizure detection from the continuous EEG monitoring data.
METHODS: In the proposed methods, the discrete wavelet transform (DWT) and orthogonal matching pursuit (OMP) techniques are used to extract different coefficients from the EEG signals. Then, some non-linear features, such as fuzzy/approximate/sample/alphabet and correct conditional entropy, along with some statistical features are calculated using the DWT and OMP coefficients. Three widely-used EEG datasets were utilized to assess the performance of the proposed techniques.
RESULTS: The proposed OMP-based technique along with the support vector machine classifier yielded an average specificity of 96.58%, an average accuracy of 97%, and an average sensitivity of 97.08% for different types of classification tasks. Moreover, the proposed DWT-based technique provided an average sensitivity of 99.39%, an average accuracy of 99.63%, and an average specificity of 99.72%.
CONCLUSIONS: The experimental findings indicated that the proposed algorithms outperformed other existing techniques. Therefore, these algorithms can be implemented in relevant hardware to help neurologists with seizure detection.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:131 |
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| Enthalten in: |
Computers in biology and medicine - 131(2021) vom: 01. Apr., Seite 104250 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zarei, Asghar [VerfasserIn] |
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| Links: |
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| Themen: |
Discrete wavelet transform |
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| Anmerkungen: |
Date Completed 02.07.2021 Date Revised 02.07.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.compbiomed.2021.104250 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM321379217 |
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| 245 | 1 | 0 | |a Automatic seizure detection using orthogonal matching pursuit, discrete wavelet transform, and entropy based features of EEG signals |
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| 500 | |a Date Revised 02.07.2021 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2021 Elsevier Ltd. All rights reserved. | ||
| 520 | |a BACKGROUND AND OBJECTIVE: Epilepsy is a prevalent disorder that affects the central nervous system, causing seizures. In the current study, a novel algorithm is developed using electroencephalographic (EEG) signals for automatic seizure detection from the continuous EEG monitoring data | ||
| 520 | |a METHODS: In the proposed methods, the discrete wavelet transform (DWT) and orthogonal matching pursuit (OMP) techniques are used to extract different coefficients from the EEG signals. Then, some non-linear features, such as fuzzy/approximate/sample/alphabet and correct conditional entropy, along with some statistical features are calculated using the DWT and OMP coefficients. Three widely-used EEG datasets were utilized to assess the performance of the proposed techniques | ||
| 520 | |a RESULTS: The proposed OMP-based technique along with the support vector machine classifier yielded an average specificity of 96.58%, an average accuracy of 97%, and an average sensitivity of 97.08% for different types of classification tasks. Moreover, the proposed DWT-based technique provided an average sensitivity of 99.39%, an average accuracy of 99.63%, and an average specificity of 99.72% | ||
| 520 | |a CONCLUSIONS: The experimental findings indicated that the proposed algorithms outperformed other existing techniques. Therefore, these algorithms can be implemented in relevant hardware to help neurologists with seizure detection | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Discrete wavelet transform | |
| 650 | 4 | |a EEG signal | |
| 650 | 4 | |a Epilepsy | |
| 650 | 4 | |a Non-linear features | |
| 650 | 4 | |a Orthogonal matching pursuit | |
| 650 | 4 | |a SVM classifier | |
| 700 | 1 | |a Asl, Babak Mohammadzadeh |e verfasserin |4 aut | |
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