Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning

Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG), which allows the diagnosis of epilepsy disease. The aim of this study is to create a combined deep learning model that automatically detects epileptic seiz...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Varlı, Muhammet [verfasserIn] Yılmaz, Hakan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Epilepsy Convolutional Neural Network Recurrent Neural Network Combined deep learning Epileptic seizure diagnosis

Übergeordnetes Werk:	Enthalten in: Journal of computational science - Amsterdam [u.a.] : Elsevier, 2010, 67
Übergeordnetes Werk:	volume:67

DOI / URN:	10.1016/j.jocs.2023.101943

Katalog-ID:	ELV009375287

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520			\|a Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG), which allows the diagnosis of epilepsy disease. The aim of this study is to create a combined deep learning model that automatically detects epileptic seizure activity, detection of the epileptic region and classifies EEG signals by using images representing the time-frequency components of the time series EEG signal and numerical values of the raw EEG signals. In the study, 3 different public datasets, CHB-MIT, Bern-Barcelona and Bonn EEG records were used. This study presents a combined model using the time sequence of EEG signals and time-frequency-image transformations of time-dependent EEG signals. CWT and STFT methods were used to convert signals to images. Two models were created separately with the images created by CWT and STFT methods. In the Bonn dataset average accuracy rates of 99.07 %, 99.28 %, respectively, in binary classifications and 97.60 % and 98.56 %, respectively, in multiple classifications were obtained with scalogram and spectrogram images. In the Bern-Barcelona and CHB-MIT datasets, 95.46 % and 96.23 % accuracy rates were obtained, respectively. The data combinations brought together in 3 different combinations with the Bonn dataset were underwent to 8-fold cross validation and average accuracy rates of 99.21 % (± 0.56), 99.50 % (± 0.45), and 98.84 % (± 1.58) were obtained. The model we created can detect whether there is epileptic seizure activity in EEG data, detection of the epileptic region and classify EEG signals with a high success rate.
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650		4	\|a Recurrent Neural Network
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650		4	\|a Epileptic seizure diagnosis
700	1		\|a Yılmaz, Hakan \|e verfasserin \|0 (orcid)0000-0002-8553-388X \|4 aut
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publishDate	2023
allfields	10.1016/j.jocs.2023.101943 doi (DE-627)ELV009375287 (ELSEVIER)S1877-7503(23)00003-0 DE-627 ger DE-627 rda eng 004 DE-600 Varlı, Muhammet verfasserin (orcid)0000-0003-3902-4504 aut Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG), which allows the diagnosis of epilepsy disease. The aim of this study is to create a combined deep learning model that automatically detects epileptic seizure activity, detection of the epileptic region and classifies EEG signals by using images representing the time-frequency components of the time series EEG signal and numerical values of the raw EEG signals. In the study, 3 different public datasets, CHB-MIT, Bern-Barcelona and Bonn EEG records were used. This study presents a combined model using the time sequence of EEG signals and time-frequency-image transformations of time-dependent EEG signals. CWT and STFT methods were used to convert signals to images. Two models were created separately with the images created by CWT and STFT methods. In the Bonn dataset average accuracy rates of 99.07 %, 99.28 %, respectively, in binary classifications and 97.60 % and 98.56 %, respectively, in multiple classifications were obtained with scalogram and spectrogram images. In the Bern-Barcelona and CHB-MIT datasets, 95.46 % and 96.23 % accuracy rates were obtained, respectively. The data combinations brought together in 3 different combinations with the Bonn dataset were underwent to 8-fold cross validation and average accuracy rates of 99.21 % (± 0.56), 99.50 % (± 0.45), and 98.84 % (± 1.58) were obtained. The model we created can detect whether there is epileptic seizure activity in EEG data, detection of the epileptic region and classify EEG signals with a high success rate. Epilepsy Convolutional Neural Network Recurrent Neural Network Combined deep learning Epileptic seizure diagnosis Yılmaz, Hakan verfasserin (orcid)0000-0002-8553-388X aut Enthalten in Journal of computational science Amsterdam [u.a.] : Elsevier, 2010 67 Online-Ressource (DE-627)627614825 (DE-600)2557360-3 (DE-576)324400225 1877-7503 nnns volume:67 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 67
spelling	10.1016/j.jocs.2023.101943 doi (DE-627)ELV009375287 (ELSEVIER)S1877-7503(23)00003-0 DE-627 ger DE-627 rda eng 004 DE-600 Varlı, Muhammet verfasserin (orcid)0000-0003-3902-4504 aut Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG), which allows the diagnosis of epilepsy disease. The aim of this study is to create a combined deep learning model that automatically detects epileptic seizure activity, detection of the epileptic region and classifies EEG signals by using images representing the time-frequency components of the time series EEG signal and numerical values of the raw EEG signals. In the study, 3 different public datasets, CHB-MIT, Bern-Barcelona and Bonn EEG records were used. This study presents a combined model using the time sequence of EEG signals and time-frequency-image transformations of time-dependent EEG signals. CWT and STFT methods were used to convert signals to images. Two models were created separately with the images created by CWT and STFT methods. In the Bonn dataset average accuracy rates of 99.07 %, 99.28 %, respectively, in binary classifications and 97.60 % and 98.56 %, respectively, in multiple classifications were obtained with scalogram and spectrogram images. In the Bern-Barcelona and CHB-MIT datasets, 95.46 % and 96.23 % accuracy rates were obtained, respectively. The data combinations brought together in 3 different combinations with the Bonn dataset were underwent to 8-fold cross validation and average accuracy rates of 99.21 % (± 0.56), 99.50 % (± 0.45), and 98.84 % (± 1.58) were obtained. The model we created can detect whether there is epileptic seizure activity in EEG data, detection of the epileptic region and classify EEG signals with a high success rate. Epilepsy Convolutional Neural Network Recurrent Neural Network Combined deep learning Epileptic seizure diagnosis Yılmaz, Hakan verfasserin (orcid)0000-0002-8553-388X aut Enthalten in Journal of computational science Amsterdam [u.a.] : Elsevier, 2010 67 Online-Ressource (DE-627)627614825 (DE-600)2557360-3 (DE-576)324400225 1877-7503 nnns volume:67 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 67
allfields_unstemmed	10.1016/j.jocs.2023.101943 doi (DE-627)ELV009375287 (ELSEVIER)S1877-7503(23)00003-0 DE-627 ger DE-627 rda eng 004 DE-600 Varlı, Muhammet verfasserin (orcid)0000-0003-3902-4504 aut Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG), which allows the diagnosis of epilepsy disease. The aim of this study is to create a combined deep learning model that automatically detects epileptic seizure activity, detection of the epileptic region and classifies EEG signals by using images representing the time-frequency components of the time series EEG signal and numerical values of the raw EEG signals. In the study, 3 different public datasets, CHB-MIT, Bern-Barcelona and Bonn EEG records were used. This study presents a combined model using the time sequence of EEG signals and time-frequency-image transformations of time-dependent EEG signals. CWT and STFT methods were used to convert signals to images. Two models were created separately with the images created by CWT and STFT methods. In the Bonn dataset average accuracy rates of 99.07 %, 99.28 %, respectively, in binary classifications and 97.60 % and 98.56 %, respectively, in multiple classifications were obtained with scalogram and spectrogram images. In the Bern-Barcelona and CHB-MIT datasets, 95.46 % and 96.23 % accuracy rates were obtained, respectively. The data combinations brought together in 3 different combinations with the Bonn dataset were underwent to 8-fold cross validation and average accuracy rates of 99.21 % (± 0.56), 99.50 % (± 0.45), and 98.84 % (± 1.58) were obtained. The model we created can detect whether there is epileptic seizure activity in EEG data, detection of the epileptic region and classify EEG signals with a high success rate. Epilepsy Convolutional Neural Network Recurrent Neural Network Combined deep learning Epileptic seizure diagnosis Yılmaz, Hakan verfasserin (orcid)0000-0002-8553-388X aut Enthalten in Journal of computational science Amsterdam [u.a.] : Elsevier, 2010 67 Online-Ressource (DE-627)627614825 (DE-600)2557360-3 (DE-576)324400225 1877-7503 nnns volume:67 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 67
allfieldsGer	10.1016/j.jocs.2023.101943 doi (DE-627)ELV009375287 (ELSEVIER)S1877-7503(23)00003-0 DE-627 ger DE-627 rda eng 004 DE-600 Varlı, Muhammet verfasserin (orcid)0000-0003-3902-4504 aut Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG), which allows the diagnosis of epilepsy disease. The aim of this study is to create a combined deep learning model that automatically detects epileptic seizure activity, detection of the epileptic region and classifies EEG signals by using images representing the time-frequency components of the time series EEG signal and numerical values of the raw EEG signals. In the study, 3 different public datasets, CHB-MIT, Bern-Barcelona and Bonn EEG records were used. This study presents a combined model using the time sequence of EEG signals and time-frequency-image transformations of time-dependent EEG signals. CWT and STFT methods were used to convert signals to images. Two models were created separately with the images created by CWT and STFT methods. In the Bonn dataset average accuracy rates of 99.07 %, 99.28 %, respectively, in binary classifications and 97.60 % and 98.56 %, respectively, in multiple classifications were obtained with scalogram and spectrogram images. In the Bern-Barcelona and CHB-MIT datasets, 95.46 % and 96.23 % accuracy rates were obtained, respectively. The data combinations brought together in 3 different combinations with the Bonn dataset were underwent to 8-fold cross validation and average accuracy rates of 99.21 % (± 0.56), 99.50 % (± 0.45), and 98.84 % (± 1.58) were obtained. The model we created can detect whether there is epileptic seizure activity in EEG data, detection of the epileptic region and classify EEG signals with a high success rate. Epilepsy Convolutional Neural Network Recurrent Neural Network Combined deep learning Epileptic seizure diagnosis Yılmaz, Hakan verfasserin (orcid)0000-0002-8553-388X aut Enthalten in Journal of computational science Amsterdam [u.a.] : Elsevier, 2010 67 Online-Ressource (DE-627)627614825 (DE-600)2557360-3 (DE-576)324400225 1877-7503 nnns volume:67 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 67
allfieldsSound	10.1016/j.jocs.2023.101943 doi (DE-627)ELV009375287 (ELSEVIER)S1877-7503(23)00003-0 DE-627 ger DE-627 rda eng 004 DE-600 Varlı, Muhammet verfasserin (orcid)0000-0003-3902-4504 aut Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG), which allows the diagnosis of epilepsy disease. The aim of this study is to create a combined deep learning model that automatically detects epileptic seizure activity, detection of the epileptic region and classifies EEG signals by using images representing the time-frequency components of the time series EEG signal and numerical values of the raw EEG signals. In the study, 3 different public datasets, CHB-MIT, Bern-Barcelona and Bonn EEG records were used. This study presents a combined model using the time sequence of EEG signals and time-frequency-image transformations of time-dependent EEG signals. CWT and STFT methods were used to convert signals to images. Two models were created separately with the images created by CWT and STFT methods. In the Bonn dataset average accuracy rates of 99.07 %, 99.28 %, respectively, in binary classifications and 97.60 % and 98.56 %, respectively, in multiple classifications were obtained with scalogram and spectrogram images. In the Bern-Barcelona and CHB-MIT datasets, 95.46 % and 96.23 % accuracy rates were obtained, respectively. The data combinations brought together in 3 different combinations with the Bonn dataset were underwent to 8-fold cross validation and average accuracy rates of 99.21 % (± 0.56), 99.50 % (± 0.45), and 98.84 % (± 1.58) were obtained. The model we created can detect whether there is epileptic seizure activity in EEG data, detection of the epileptic region and classify EEG signals with a high success rate. Epilepsy Convolutional Neural Network Recurrent Neural Network Combined deep learning Epileptic seizure diagnosis Yılmaz, Hakan verfasserin (orcid)0000-0002-8553-388X aut Enthalten in Journal of computational science Amsterdam [u.a.] : Elsevier, 2010 67 Online-Ressource (DE-627)627614825 (DE-600)2557360-3 (DE-576)324400225 1877-7503 nnns volume:67 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 67
language	English
source	Enthalten in Journal of computational science 67 volume:67
sourceStr	Enthalten in Journal of computational science 67 volume:67
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Epilepsy Convolutional Neural Network Recurrent Neural Network Combined deep learning Epileptic seizure diagnosis
dewey-raw	004
isfreeaccess_bool	false
container_title	Journal of computational science
authorswithroles_txt_mv	Varlı, Muhammet @@aut@@ Yılmaz, Hakan @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	627614825
dewey-sort	14
id	ELV009375287
language_de	englisch
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author	Varlı, Muhammet
spellingShingle	Varlı, Muhammet ddc 004 misc Epilepsy misc Convolutional Neural Network misc Recurrent Neural Network misc Combined deep learning misc Epileptic seizure diagnosis Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning
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topic_title	004 DE-600 Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning Epilepsy Convolutional Neural Network Recurrent Neural Network Combined deep learning Epileptic seizure diagnosis
topic	ddc 004 misc Epilepsy misc Convolutional Neural Network misc Recurrent Neural Network misc Combined deep learning misc Epileptic seizure diagnosis
topic_unstemmed	ddc 004 misc Epilepsy misc Convolutional Neural Network misc Recurrent Neural Network misc Combined deep learning misc Epileptic seizure diagnosis
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title	Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning
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title_full	Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning
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title_sort	multiple classification of eeg signals and epileptic seizure diagnosis with combined deep learning
title_auth	Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning
abstract	Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG), which allows the diagnosis of epilepsy disease. The aim of this study is to create a combined deep learning model that automatically detects epileptic seizure activity, detection of the epileptic region and classifies EEG signals by using images representing the time-frequency components of the time series EEG signal and numerical values of the raw EEG signals. In the study, 3 different public datasets, CHB-MIT, Bern-Barcelona and Bonn EEG records were used. This study presents a combined model using the time sequence of EEG signals and time-frequency-image transformations of time-dependent EEG signals. CWT and STFT methods were used to convert signals to images. Two models were created separately with the images created by CWT and STFT methods. In the Bonn dataset average accuracy rates of 99.07 %, 99.28 %, respectively, in binary classifications and 97.60 % and 98.56 %, respectively, in multiple classifications were obtained with scalogram and spectrogram images. In the Bern-Barcelona and CHB-MIT datasets, 95.46 % and 96.23 % accuracy rates were obtained, respectively. The data combinations brought together in 3 different combinations with the Bonn dataset were underwent to 8-fold cross validation and average accuracy rates of 99.21 % (± 0.56), 99.50 % (± 0.45), and 98.84 % (± 1.58) were obtained. The model we created can detect whether there is epileptic seizure activity in EEG data, detection of the epileptic region and classify EEG signals with a high success rate.
abstractGer	Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG), which allows the diagnosis of epilepsy disease. The aim of this study is to create a combined deep learning model that automatically detects epileptic seizure activity, detection of the epileptic region and classifies EEG signals by using images representing the time-frequency components of the time series EEG signal and numerical values of the raw EEG signals. In the study, 3 different public datasets, CHB-MIT, Bern-Barcelona and Bonn EEG records were used. This study presents a combined model using the time sequence of EEG signals and time-frequency-image transformations of time-dependent EEG signals. CWT and STFT methods were used to convert signals to images. Two models were created separately with the images created by CWT and STFT methods. In the Bonn dataset average accuracy rates of 99.07 %, 99.28 %, respectively, in binary classifications and 97.60 % and 98.56 %, respectively, in multiple classifications were obtained with scalogram and spectrogram images. In the Bern-Barcelona and CHB-MIT datasets, 95.46 % and 96.23 % accuracy rates were obtained, respectively. The data combinations brought together in 3 different combinations with the Bonn dataset were underwent to 8-fold cross validation and average accuracy rates of 99.21 % (± 0.56), 99.50 % (± 0.45), and 98.84 % (± 1.58) were obtained. The model we created can detect whether there is epileptic seizure activity in EEG data, detection of the epileptic region and classify EEG signals with a high success rate.
abstract_unstemmed	Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG), which allows the diagnosis of epilepsy disease. The aim of this study is to create a combined deep learning model that automatically detects epileptic seizure activity, detection of the epileptic region and classifies EEG signals by using images representing the time-frequency components of the time series EEG signal and numerical values of the raw EEG signals. In the study, 3 different public datasets, CHB-MIT, Bern-Barcelona and Bonn EEG records were used. This study presents a combined model using the time sequence of EEG signals and time-frequency-image transformations of time-dependent EEG signals. CWT and STFT methods were used to convert signals to images. Two models were created separately with the images created by CWT and STFT methods. In the Bonn dataset average accuracy rates of 99.07 %, 99.28 %, respectively, in binary classifications and 97.60 % and 98.56 %, respectively, in multiple classifications were obtained with scalogram and spectrogram images. In the Bern-Barcelona and CHB-MIT datasets, 95.46 % and 96.23 % accuracy rates were obtained, respectively. The data combinations brought together in 3 different combinations with the Bonn dataset were underwent to 8-fold cross validation and average accuracy rates of 99.21 % (± 0.56), 99.50 % (± 0.45), and 98.84 % (± 1.58) were obtained. The model we created can detect whether there is epileptic seizure activity in EEG data, detection of the epileptic region and classify EEG signals with a high success rate.
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title_short	Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning
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up_date	2024-07-06T22:56:30.542Z
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Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning

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