Feature Extraction Methods for Electroretinogram Signal Analysis: A Review
Feature extraction is an essential aspect of electroretinogram (ERG) signal analysis. The extracted features are beneficial to analyze the signal further and compress the signal for storage or transmission purposes. Various methods have been widely employed to extract the characteristics of ERG sign...
Ausführliche Beschreibung
Autor*in: |
Soroor Behbahani [verfasserIn] Hamid Ahmadieh [verfasserIn] Sreeraman Rajan [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
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2021 |
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In: IEEE Access - IEEE, 2014, 9(2021), Seite 116879-116897 |
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Übergeordnetes Werk: |
volume:9 ; year:2021 ; pages:116879-116897 |
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DOI / URN: |
10.1109/ACCESS.2021.3103848 |
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Katalog-ID: |
DOAJ004935241 |
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10.1109/ACCESS.2021.3103848 doi (DE-627)DOAJ004935241 (DE-599)DOAJ07dab4c5ddd54b60b44dd949fb6bd9f0 DE-627 ger DE-627 rakwb eng TK1-9971 Soroor Behbahani verfasserin aut Feature Extraction Methods for Electroretinogram Signal Analysis: A Review 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Feature extraction is an essential aspect of electroretinogram (ERG) signal analysis. The extracted features are beneficial to analyze the signal further and compress the signal for storage or transmission purposes. Various methods have been widely employed to extract the characteristics of ERG signals. Methods based on the time-domain, frequency-domain, time-frequency domain and nonlinear and chaotic feature extraction techniques have been used to extract features that characterize ERG signals. This paper reviews several feature extraction methods applied to ERG and compares their performance under different conditions to provide guidance to select the most appropriate feature extraction method based on the performance. Electroretinogram feature extraction frequency-domain analysis time-domain analysis time-frequency domain analysis nonlinear analysis Electrical engineering. Electronics. Nuclear engineering Hamid Ahmadieh verfasserin aut Sreeraman Rajan verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 116879-116897 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:116879-116897 https://doi.org/10.1109/ACCESS.2021.3103848 kostenfrei https://doaj.org/article/07dab4c5ddd54b60b44dd949fb6bd9f0 kostenfrei https://ieeexplore.ieee.org/document/9509508/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 116879-116897 |
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10.1109/ACCESS.2021.3103848 doi (DE-627)DOAJ004935241 (DE-599)DOAJ07dab4c5ddd54b60b44dd949fb6bd9f0 DE-627 ger DE-627 rakwb eng TK1-9971 Soroor Behbahani verfasserin aut Feature Extraction Methods for Electroretinogram Signal Analysis: A Review 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Feature extraction is an essential aspect of electroretinogram (ERG) signal analysis. The extracted features are beneficial to analyze the signal further and compress the signal for storage or transmission purposes. Various methods have been widely employed to extract the characteristics of ERG signals. Methods based on the time-domain, frequency-domain, time-frequency domain and nonlinear and chaotic feature extraction techniques have been used to extract features that characterize ERG signals. This paper reviews several feature extraction methods applied to ERG and compares their performance under different conditions to provide guidance to select the most appropriate feature extraction method based on the performance. Electroretinogram feature extraction frequency-domain analysis time-domain analysis time-frequency domain analysis nonlinear analysis Electrical engineering. Electronics. Nuclear engineering Hamid Ahmadieh verfasserin aut Sreeraman Rajan verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 116879-116897 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:116879-116897 https://doi.org/10.1109/ACCESS.2021.3103848 kostenfrei https://doaj.org/article/07dab4c5ddd54b60b44dd949fb6bd9f0 kostenfrei https://ieeexplore.ieee.org/document/9509508/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 116879-116897 |
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10.1109/ACCESS.2021.3103848 doi (DE-627)DOAJ004935241 (DE-599)DOAJ07dab4c5ddd54b60b44dd949fb6bd9f0 DE-627 ger DE-627 rakwb eng TK1-9971 Soroor Behbahani verfasserin aut Feature Extraction Methods for Electroretinogram Signal Analysis: A Review 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Feature extraction is an essential aspect of electroretinogram (ERG) signal analysis. The extracted features are beneficial to analyze the signal further and compress the signal for storage or transmission purposes. Various methods have been widely employed to extract the characteristics of ERG signals. Methods based on the time-domain, frequency-domain, time-frequency domain and nonlinear and chaotic feature extraction techniques have been used to extract features that characterize ERG signals. This paper reviews several feature extraction methods applied to ERG and compares their performance under different conditions to provide guidance to select the most appropriate feature extraction method based on the performance. Electroretinogram feature extraction frequency-domain analysis time-domain analysis time-frequency domain analysis nonlinear analysis Electrical engineering. Electronics. Nuclear engineering Hamid Ahmadieh verfasserin aut Sreeraman Rajan verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 116879-116897 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:116879-116897 https://doi.org/10.1109/ACCESS.2021.3103848 kostenfrei https://doaj.org/article/07dab4c5ddd54b60b44dd949fb6bd9f0 kostenfrei https://ieeexplore.ieee.org/document/9509508/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 116879-116897 |
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10.1109/ACCESS.2021.3103848 doi (DE-627)DOAJ004935241 (DE-599)DOAJ07dab4c5ddd54b60b44dd949fb6bd9f0 DE-627 ger DE-627 rakwb eng TK1-9971 Soroor Behbahani verfasserin aut Feature Extraction Methods for Electroretinogram Signal Analysis: A Review 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Feature extraction is an essential aspect of electroretinogram (ERG) signal analysis. The extracted features are beneficial to analyze the signal further and compress the signal for storage or transmission purposes. Various methods have been widely employed to extract the characteristics of ERG signals. Methods based on the time-domain, frequency-domain, time-frequency domain and nonlinear and chaotic feature extraction techniques have been used to extract features that characterize ERG signals. This paper reviews several feature extraction methods applied to ERG and compares their performance under different conditions to provide guidance to select the most appropriate feature extraction method based on the performance. Electroretinogram feature extraction frequency-domain analysis time-domain analysis time-frequency domain analysis nonlinear analysis Electrical engineering. Electronics. Nuclear engineering Hamid Ahmadieh verfasserin aut Sreeraman Rajan verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 116879-116897 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:116879-116897 https://doi.org/10.1109/ACCESS.2021.3103848 kostenfrei https://doaj.org/article/07dab4c5ddd54b60b44dd949fb6bd9f0 kostenfrei https://ieeexplore.ieee.org/document/9509508/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 116879-116897 |
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TK1-9971 Feature Extraction Methods for Electroretinogram Signal Analysis: A Review Electroretinogram feature extraction frequency-domain analysis time-domain analysis time-frequency domain analysis nonlinear analysis |
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Feature Extraction Methods for Electroretinogram Signal Analysis: A Review |
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Feature extraction is an essential aspect of electroretinogram (ERG) signal analysis. The extracted features are beneficial to analyze the signal further and compress the signal for storage or transmission purposes. Various methods have been widely employed to extract the characteristics of ERG signals. Methods based on the time-domain, frequency-domain, time-frequency domain and nonlinear and chaotic feature extraction techniques have been used to extract features that characterize ERG signals. This paper reviews several feature extraction methods applied to ERG and compares their performance under different conditions to provide guidance to select the most appropriate feature extraction method based on the performance. |
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Feature extraction is an essential aspect of electroretinogram (ERG) signal analysis. The extracted features are beneficial to analyze the signal further and compress the signal for storage or transmission purposes. Various methods have been widely employed to extract the characteristics of ERG signals. Methods based on the time-domain, frequency-domain, time-frequency domain and nonlinear and chaotic feature extraction techniques have been used to extract features that characterize ERG signals. This paper reviews several feature extraction methods applied to ERG and compares their performance under different conditions to provide guidance to select the most appropriate feature extraction method based on the performance. |
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Feature extraction is an essential aspect of electroretinogram (ERG) signal analysis. The extracted features are beneficial to analyze the signal further and compress the signal for storage or transmission purposes. Various methods have been widely employed to extract the characteristics of ERG signals. Methods based on the time-domain, frequency-domain, time-frequency domain and nonlinear and chaotic feature extraction techniques have been used to extract features that characterize ERG signals. This paper reviews several feature extraction methods applied to ERG and compares their performance under different conditions to provide guidance to select the most appropriate feature extraction method based on the performance. |
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score |
7.3999014 |