An automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems
Recent advancements in machine learning and deep learning models find them helpful in designing effective complex measurement systems. At the same time, examining the brain’s activities using Electroencephalography (EEG) is essential to determine the mental state or thought of a person. It is essent...
- Autores:
-
Escorcia-Gutierrez, Jose
BELEÑO SAENZ, KELVIN
Jiménez-Cabas, Javier
Elhoseny, Mohamed
Alshehri, Dr. Mohammad Dahman
Selim, Mahmoud M.
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2022
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/9144
- Acceso en línea:
- https://hdl.handle.net/11323/9144
https://doi.org/10.1016/j.measurement.2022.111226
https://repositorio.cuc.edu.co/
- Palabra clave:
- Brain signals
Classification
Complex measurement
Deep learning
Epileptic seizure
EEG signals
- Rights
- embargoedAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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| dc.title.eng.fl_str_mv |
An automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems |
| title |
An automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems |
| spellingShingle |
An automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems Brain signals Classification Complex measurement Deep learning Epileptic seizure EEG signals |
| title_short |
An automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems |
| title_full |
An automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems |
| title_fullStr |
An automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems |
| title_full_unstemmed |
An automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems |
| title_sort |
An automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems |
| dc.creator.fl_str_mv |
Escorcia-Gutierrez, Jose BELEÑO SAENZ, KELVIN Jiménez-Cabas, Javier Elhoseny, Mohamed Alshehri, Dr. Mohammad Dahman Selim, Mahmoud M. |
| dc.contributor.author.spa.fl_str_mv |
Escorcia-Gutierrez, Jose BELEÑO SAENZ, KELVIN Jiménez-Cabas, Javier Elhoseny, Mohamed Alshehri, Dr. Mohammad Dahman Selim, Mahmoud M. |
| dc.subject.proposal.eng.fl_str_mv |
Brain signals Classification Complex measurement Deep learning Epileptic seizure EEG signals |
| topic |
Brain signals Classification Complex measurement Deep learning Epileptic seizure EEG signals |
| description |
Recent advancements in machine learning and deep learning models find them helpful in designing effective complex measurement systems. At the same time, examining the brain’s activities using Electroencephalography (EEG) is essential to determine the mental state or thought of a person. It is essential in several application areas, such as Brain-Computer Interface (BCI), emotion recognition, and mental disease diagnosis. The proper brain signal classification using EEG finds helpful diagnose epileptic seizures. Since the traditional seizure detection process is a lengthy and challenging task, the automated identification of epilepsy is a significant problem. In order to resolve the issues that exist in the traditional brain signal classification models, this study designs Automated Deep Learning-Enabled Brain Signal Classification for Epileptic Seizure Detection (ADLBSC-ESD). The proposed ADLBSC-ESD technique aims to classify the brain signals to determine the existence of seizures or not. In addition, the presented model involves the design of the Improved Teaching and Learning-Enabled Optimization (ITLBO) technique for selecting features from EEG signals. Moreover, the Deep Belief Network (DBN) model is used for an effectual classification of EEG signals, and the hyperparameters of the DBN model are optimally tuned using the Swallow Swarm Optimization Algorithm (SSA). In order to ensure the improved brain signal classification performance of the ADLBSC-ESD technique, a series of simulations take place, and the outcomes are investigated concerning different measures. The experimental values highlighted the better performance of the ADLBSC-ESD technique over the current state of art techniques with maximum accuracy of 0.8316 and 0.8609 under binary and multiple classes, respectively. |
| publishDate |
2022 |
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2022-05-03T17:33:43Z |
| dc.date.available.none.fl_str_mv |
2022-05-03T17:33:43Z 2024 |
| dc.date.issued.none.fl_str_mv |
2022 |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/coar/resource_type/c_6501 |
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Text |
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info:eu-repo/semantics/article |
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http://purl.org/redcol/resource_type/ART |
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0263-2241 |
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https://hdl.handle.net/11323/9144 |
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https://doi.org/10.1016/j.measurement.2022.111226 |
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10.1016/j.measurement.2022.111226 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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0263-2241 10.1016/j.measurement.2022.111226 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/9144 https://doi.org/10.1016/j.measurement.2022.111226 https://repositorio.cuc.edu.co/ |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
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Measurement: Journal of the International Measurement Confederation |
| dc.relation.references.spa.fl_str_mv |
[1] S. Taran, V. Bajaj, D. Sharma, S. Siuly, A. Sengur, Features based on analytic IMF for classifying motor imagery EEG signals in BCI applications, Measurement 116 (2018) 68–76. [2] M. Diykh, F.S. Miften, S. Abdulla, R.C. Deo, S. Siuly, J.H. Green, A.Y. Oudahb, Texture analysis based graph approach for automatic detection of neonatal seizure from multi-channel EEG signals, Measurement 190 (2022) 110731, https://doi.org/10.1016/j.measurement.2022.110731. [3] J.R. Wolpaw, D.J. McFarland, Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans, In: Proceedings of the National Academy of Sciences of the United States of America 101.51 (2004), pp. 17849–54. [4] C. Lyu, Y. Chen, Z. Chen, Y. Liu, Z. Wang, Automatic epilepsy detection based on generalized convolutional prototype learning, Measurement 184 (2021) 109954, https://doi.org/10.1016/j.measurement.2021.109954. [5] H. Berger. Uber das Elektrenkephalogramm des Menschen, in:¨Archiv fur¨ Psychiatrie und Nervenkrankheiten 99.1, 1933, pp. 555–574. doi: 10.1007/BF01814320 (cit. on p. 3). [6] J. Sekar, P. Aruchamy, H. Sulaima Lebbe Abdul, A.S. Mohammed, S. Khamuruddeen, An efficient clinical support system for heart disease prediction using TANFIS classifier, Comput. Intell. (2021). [7] S. Lavanya, A. Prasanth, S. Jayachitra, A. Shenbagarajan, A Tuned classification approach for efficient heterogeneous fault diagnosis in IoT-enabled WSN applications, Measurement 183 (2021) 109771, https://doi.org/10.1016/j.measurement.2021.109771. [8] S. Jayachitra, A. Prasanth, Multi-feature analysis for automated brain stroke classification using weighted Gaussian naïve Bayes classifier, J. Circuits Syst. Comput. 30 (10) (2021) 2150178, https://doi.org/10.1142/S0218126621501784. [9] S. Jang, S.E. Moon, J.S. Lee, Brain signal classification via learning connectivity structure, 2019. arXiv preprint arXiv:1905.11678. [10] L. Zhu, Q. Hu, J. Yang, J. Zhang, P. Xu, N. Ying, F. Schwenker, EEG signal classification using manifold learning and matrix-variate Gaussian model, Computat. Intell. Neurosci. 2021 (2021) 1–12. [11] P. Fergus, D. Hignett, A. Hussain, D. Al-Jumeily, K. Abdel-Aziz, Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques, Biomed Res. Int. 2015 (2015) 1–17. [12] W. Zhao, W. Zhao, W. Wang, X. Jiang, X. Zhang, Y. Peng, B. Zhang, G. Zhang, A novel deep neural network for robust detection of seizures using EEG signals, Comput. Math. Methods Med. 2020 (2020) 1–9. [13] D. Zhou, X. Li, Epilepsy EEG signal classification algorithm based on improved RBF, Front. Neurosci. 14 (2020) 606. [14] BSuguna Nanthini, B. Santhi, Electroencephalogram signal classification for automated epileptic seizure detection using genetic algorithm, J. Nat. Sci. Biol. Med. 8 (2) (2017) 159, https://doi.org/10.4103/jnsbm.JNSBM_285_16. [15] M. Radman, M. Moradi, A. Chaibakhsh, M. Kordestani, M. Saif, Multi-feature fusion approach for epileptic seizure detection from EEG signals, IEEE Sens. J. 21 (3) (2021) 3533–3543. [16] Aayesha, M.B. Qureshi, M. Afzaal, M.S. Qureshi, M. Fayaz, Machine learning-based EEG signals classification model for epileptic seizure detection, Multimedia Tools Appl. 80 (12) (2021) 17849–17877. [17] H. Al-Hadeethi, S. Abdulla, M. Diykh, R.C. Deo, J.H. Green, Adaptive boost LS-SVM classification approach for time-series signal classification in epileptic seizure diagnosis applications, Expert Syst. Appl. 161 (2020) 113676, https://doi.org/10.1016/j.eswa.2020.113676. [18] G. Manocha, H. Rustagi, S.P. Singh, R. Jain, P. Nagrath, Epilepsy Seizure Classification Using One-Dimensional Convolutional Neural Networks, in: Data Management, Analytics and Innovation, Springer, Singapore, 2022, pp. 155–168. [19] M. Sameer, B. Gupta, CNN based framework for detection of epileptic seizures, Multimedia Tools Appl. (2022) 1–14. [20] S. Pattnaik, N. Rout, S. Sabut, Machine learning approach for epileptic seizure detection using the tunable-Q wavelet transform based time–frequency features, Int. J. Inform. Technol. (2022) 1–11. [21] M. Allam, M. Nandhini, Optimal feature selection using binary teaching learning based optimization algorithm, J. King Saud Univ.-Comput. Inform. Sci., 2018. [22] J. Yu, G. Liu, Knowledge-based deep belief network for machining roughness prediction and knowledge discovery, Comput. Ind. 121 (2020), 103262. [23] Y. Jia, J. Wu, M. Xu, Traffic flow prediction with rainfall impact using a deep learning method, J. Adv. Transport. 2017 (2017) 1–10. [24] M. Neshat, G. Sepidname, A new hybrid optimization method inspired from swarm intelligence: Fuzzy adaptive swallow swarm optimization algorithm (FASSO), Egypt. Inform. J. 16 (3) (2015) 339–350. [25] https://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition. [26] P. Suguna, B. Kirubagari, R. Umamaheswari, Epileptic seizure detection using simulated annealing based optimal feature subset selection with kernel extreme learning machine classification model, IJATCSE 9 (4) (2020) 6464–6470, https://doi.org/10.30534/ijatcse/2020/331942020. |
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Escorcia-Gutierrez, JoseBELEÑO SAENZ, KELVINJiménez-Cabas, JavierElhoseny, MohamedAlshehri, Dr. Mohammad DahmanSelim, Mahmoud M.2022-05-03T17:33:43Z20242022-05-03T17:33:43Z20220263-2241https://hdl.handle.net/11323/9144https://doi.org/10.1016/j.measurement.2022.11122610.1016/j.measurement.2022.111226Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Recent advancements in machine learning and deep learning models find them helpful in designing effective complex measurement systems. At the same time, examining the brain’s activities using Electroencephalography (EEG) is essential to determine the mental state or thought of a person. It is essential in several application areas, such as Brain-Computer Interface (BCI), emotion recognition, and mental disease diagnosis. The proper brain signal classification using EEG finds helpful diagnose epileptic seizures. Since the traditional seizure detection process is a lengthy and challenging task, the automated identification of epilepsy is a significant problem. In order to resolve the issues that exist in the traditional brain signal classification models, this study designs Automated Deep Learning-Enabled Brain Signal Classification for Epileptic Seizure Detection (ADLBSC-ESD). The proposed ADLBSC-ESD technique aims to classify the brain signals to determine the existence of seizures or not. In addition, the presented model involves the design of the Improved Teaching and Learning-Enabled Optimization (ITLBO) technique for selecting features from EEG signals. Moreover, the Deep Belief Network (DBN) model is used for an effectual classification of EEG signals, and the hyperparameters of the DBN model are optimally tuned using the Swallow Swarm Optimization Algorithm (SSA). In order to ensure the improved brain signal classification performance of the ADLBSC-ESD technique, a series of simulations take place, and the outcomes are investigated concerning different measures. The experimental values highlighted the better performance of the ADLBSC-ESD technique over the current state of art techniques with maximum accuracy of 0.8316 and 0.8609 under binary and multiple classes, respectively.9 páginasapplication/pdfengElsevierNetherlandsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)© 2022 Elsevier Ltd. All rights reserved.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfAn automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systemsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionhttps://www.sciencedirect.com/science/article/pii/S0263224122004766#!Measurement: Journal of the International Measurement Confederation[1] S. Taran, V. Bajaj, D. Sharma, S. Siuly, A. Sengur, Features based on analytic IMF for classifying motor imagery EEG signals in BCI applications, Measurement 116 (2018) 68–76.[2] M. Diykh, F.S. Miften, S. Abdulla, R.C. Deo, S. Siuly, J.H. Green, A.Y. Oudahb, Texture analysis based graph approach for automatic detection of neonatal seizure from multi-channel EEG signals, Measurement 190 (2022) 110731, https://doi.org/10.1016/j.measurement.2022.110731.[3] J.R. Wolpaw, D.J. McFarland, Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans, In: Proceedings of the National Academy of Sciences of the United States of America 101.51 (2004), pp. 17849–54.[4] C. Lyu, Y. Chen, Z. Chen, Y. Liu, Z. Wang, Automatic epilepsy detection based on generalized convolutional prototype learning, Measurement 184 (2021) 109954, https://doi.org/10.1016/j.measurement.2021.109954.[5] H. Berger. Uber das Elektrenkephalogramm des Menschen, in:¨Archiv fur¨ Psychiatrie und Nervenkrankheiten 99.1, 1933, pp. 555–574. doi: 10.1007/BF01814320 (cit. on p. 3).[6] J. Sekar, P. Aruchamy, H. Sulaima Lebbe Abdul, A.S. Mohammed, S. Khamuruddeen, An efficient clinical support system for heart disease prediction using TANFIS classifier, Comput. Intell. (2021).[7] S. Lavanya, A. Prasanth, S. Jayachitra, A. Shenbagarajan, A Tuned classification approach for efficient heterogeneous fault diagnosis in IoT-enabled WSN applications, Measurement 183 (2021) 109771, https://doi.org/10.1016/j.measurement.2021.109771.[8] S. Jayachitra, A. Prasanth, Multi-feature analysis for automated brain stroke classification using weighted Gaussian naïve Bayes classifier, J. Circuits Syst. Comput. 30 (10) (2021) 2150178, https://doi.org/10.1142/S0218126621501784.[9] S. Jang, S.E. Moon, J.S. Lee, Brain signal classification via learning connectivity structure, 2019. arXiv preprint arXiv:1905.11678.[10] L. Zhu, Q. Hu, J. Yang, J. Zhang, P. Xu, N. Ying, F. Schwenker, EEG signal classification using manifold learning and matrix-variate Gaussian model, Computat. Intell. Neurosci. 2021 (2021) 1–12.[11] P. Fergus, D. Hignett, A. Hussain, D. Al-Jumeily, K. Abdel-Aziz, Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques, Biomed Res. Int. 2015 (2015) 1–17.[12] W. Zhao, W. Zhao, W. Wang, X. Jiang, X. Zhang, Y. Peng, B. Zhang, G. Zhang, A novel deep neural network for robust detection of seizures using EEG signals, Comput. Math. Methods Med. 2020 (2020) 1–9.[13] D. Zhou, X. Li, Epilepsy EEG signal classification algorithm based on improved RBF, Front. Neurosci. 14 (2020) 606.[14] BSuguna Nanthini, B. Santhi, Electroencephalogram signal classification for automated epileptic seizure detection using genetic algorithm, J. Nat. Sci. Biol. Med. 8 (2) (2017) 159, https://doi.org/10.4103/jnsbm.JNSBM_285_16.[15] M. Radman, M. Moradi, A. Chaibakhsh, M. Kordestani, M. Saif, Multi-feature fusion approach for epileptic seizure detection from EEG signals, IEEE Sens. J. 21 (3) (2021) 3533–3543.[16] Aayesha, M.B. Qureshi, M. Afzaal, M.S. Qureshi, M. Fayaz, Machine learning-based EEG signals classification model for epileptic seizure detection, Multimedia Tools Appl. 80 (12) (2021) 17849–17877.[17] H. Al-Hadeethi, S. Abdulla, M. Diykh, R.C. Deo, J.H. Green, Adaptive boost LS-SVM classification approach for time-series signal classification in epileptic seizure diagnosis applications, Expert Syst. Appl. 161 (2020) 113676, https://doi.org/10.1016/j.eswa.2020.113676.[18] G. Manocha, H. Rustagi, S.P. Singh, R. Jain, P. Nagrath, Epilepsy Seizure Classification Using One-Dimensional Convolutional Neural Networks, in: Data Management, Analytics and Innovation, Springer, Singapore, 2022, pp. 155–168.[19] M. Sameer, B. Gupta, CNN based framework for detection of epileptic seizures, Multimedia Tools Appl. (2022) 1–14.[20] S. Pattnaik, N. Rout, S. Sabut, Machine learning approach for epileptic seizure detection using the tunable-Q wavelet transform based time–frequency features, Int. J. Inform. Technol. (2022) 1–11.[21] M. Allam, M. Nandhini, Optimal feature selection using binary teaching learning based optimization algorithm, J. King Saud Univ.-Comput. Inform. Sci., 2018.[22] J. Yu, G. Liu, Knowledge-based deep belief network for machining roughness prediction and knowledge discovery, Comput. Ind. 121 (2020), 103262.[23] Y. Jia, J. Wu, M. Xu, Traffic flow prediction with rainfall impact using a deep learning method, J. Adv. Transport. 2017 (2017) 1–10.[24] M. Neshat, G. Sepidname, A new hybrid optimization method inspired from swarm intelligence: Fuzzy adaptive swallow swarm optimization algorithm (FASSO), Egypt. Inform. J. 16 (3) (2015) 339–350.[25] https://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition.[26] P. Suguna, B. Kirubagari, R. Umamaheswari, Epileptic seizure detection using simulated annealing based optimal feature subset selection with kernel extreme learning machine classification model, IJATCSE 9 (4) (2020) 6464–6470, https://doi.org/10.30534/ijatcse/2020/331942020.91196Brain signalsClassificationComplex measurementDeep learningEpileptic seizureEEG signalsPublicationORIGINALAn automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems.pdfAn automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems.pdfapplication/pdf2479112https://repositorio.cuc.edu.co/bitstreams/bdf1d7c4-e0c9-436d-9f81-9033c75762f1/download189ca667ccd2e7be18b7be080d6cf987MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/c15cdd92-47db-4a8e-aa7b-e48fbda3aa48/downloade30e9215131d99561d40d6b0abbe9badMD52TEXTAn automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems.pdf.txtAn automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems.pdf.txttext/plain41323https://repositorio.cuc.edu.co/bitstreams/67a147e2-53a7-48d1-9b3d-f66fb9753a74/download45a91253126c027a3b90aaf5fb2550f9MD53THUMBNAILAn automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems.pdf.jpgAn automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems.pdf.jpgimage/jpeg14456https://repositorio.cuc.edu.co/bitstreams/4d1bb185-f444-431c-90d2-3863a27f5e80/download93c6a66cadfde091f9ad24f0de1b12c4MD5411323/9144oai:repositorio.cuc.edu.co:11323/91442024-09-16 16:33:41.149https://creativecommons.org/licenses/by-nc-nd/4.0/Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 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