An Extended K Nearest Neighbors-Based Classifier for Epilepsy Diagnosis

In the diagnosis of epileptic seizures, classification is an important step that directly affects the results. Visual inspection of Electroencephalogram (EEG) is a relatively common analytic method of epilepsy, but it is costly, time-consuming and relies on the experiences of the doctor. Therefore,...
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Autor*in:	Junying Na [verfasserIn] Zhiping Wang [verfasserIn] Siqi Lv [verfasserIn] Zhaohui Xu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Epileptic seizures EEG signals KNN Hassanat distance representation

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 9(2021), Seite 73910-73923
Übergeordnetes Werk:	volume:9 ; year:2021 ; pages:73910-73923

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2021.3081767

Katalog-ID:	DOAJ056334818

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520			\|a In the diagnosis of epileptic seizures, classification is an important step that directly affects the results. Visual inspection of Electroencephalogram (EEG) is a relatively common analytic method of epilepsy, but it is costly, time-consuming and relies on the experiences of the doctor. Therefore, the development of an efficient and accurate epileptic seizure automatic diagnosis system suitable for clinical diagnosis has become an urgent task. In order to better solve the problem of early diagnosis of epileptic and bring timely treatment to patients, the comprehensive representation of k nearest neighbors for multi-distance decision making (CRMKNN) is proposed in this research. In the proposed scheme, Euclidean distance and Hassanat distance are firstly used to select neighbors. Subsequently, the similarity distance is obtained through the linear representation of the nearest neighbors, and calculate the distribution of nearest neighbors in the category to get the discrete distance. Finally, the distance based on the comprehensive representation of the category is used to determine the category of the query EEG signal. In order to verify the method, we used the EEG signals from Bonn university public database and conducted experiments on six kinds of EEG combinations. Experimental results showed that our method could automatically detect seizure in all situations with an accuracy of not less than 99.50%. At the same time, compared with the classification results of existing methods, this method is more effective.
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spelling	10.1109/ACCESS.2021.3081767 doi (DE-627)DOAJ056334818 (DE-599)DOAJb7a92424254f481985f3532972281724 DE-627 ger DE-627 rakwb eng TK1-9971 Junying Na verfasserin aut An Extended K Nearest Neighbors-Based Classifier for Epilepsy Diagnosis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the diagnosis of epileptic seizures, classification is an important step that directly affects the results. Visual inspection of Electroencephalogram (EEG) is a relatively common analytic method of epilepsy, but it is costly, time-consuming and relies on the experiences of the doctor. Therefore, the development of an efficient and accurate epileptic seizure automatic diagnosis system suitable for clinical diagnosis has become an urgent task. In order to better solve the problem of early diagnosis of epileptic and bring timely treatment to patients, the comprehensive representation of k nearest neighbors for multi-distance decision making (CRMKNN) is proposed in this research. In the proposed scheme, Euclidean distance and Hassanat distance are firstly used to select neighbors. Subsequently, the similarity distance is obtained through the linear representation of the nearest neighbors, and calculate the distribution of nearest neighbors in the category to get the discrete distance. Finally, the distance based on the comprehensive representation of the category is used to determine the category of the query EEG signal. In order to verify the method, we used the EEG signals from Bonn university public database and conducted experiments on six kinds of EEG combinations. Experimental results showed that our method could automatically detect seizure in all situations with an accuracy of not less than 99.50%. At the same time, compared with the classification results of existing methods, this method is more effective. Epileptic seizures EEG signals KNN Hassanat distance representation Electrical engineering. Electronics. Nuclear engineering Zhiping Wang verfasserin aut Siqi Lv verfasserin aut Zhaohui Xu verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 73910-73923 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:73910-73923 https://doi.org/10.1109/ACCESS.2021.3081767 kostenfrei https://doaj.org/article/b7a92424254f481985f3532972281724 kostenfrei https://ieeexplore.ieee.org/document/9435418/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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 73910-73923
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allfieldsSound	10.1109/ACCESS.2021.3081767 doi (DE-627)DOAJ056334818 (DE-599)DOAJb7a92424254f481985f3532972281724 DE-627 ger DE-627 rakwb eng TK1-9971 Junying Na verfasserin aut An Extended K Nearest Neighbors-Based Classifier for Epilepsy Diagnosis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the diagnosis of epileptic seizures, classification is an important step that directly affects the results. Visual inspection of Electroencephalogram (EEG) is a relatively common analytic method of epilepsy, but it is costly, time-consuming and relies on the experiences of the doctor. Therefore, the development of an efficient and accurate epileptic seizure automatic diagnosis system suitable for clinical diagnosis has become an urgent task. In order to better solve the problem of early diagnosis of epileptic and bring timely treatment to patients, the comprehensive representation of k nearest neighbors for multi-distance decision making (CRMKNN) is proposed in this research. In the proposed scheme, Euclidean distance and Hassanat distance are firstly used to select neighbors. Subsequently, the similarity distance is obtained through the linear representation of the nearest neighbors, and calculate the distribution of nearest neighbors in the category to get the discrete distance. Finally, the distance based on the comprehensive representation of the category is used to determine the category of the query EEG signal. In order to verify the method, we used the EEG signals from Bonn university public database and conducted experiments on six kinds of EEG combinations. Experimental results showed that our method could automatically detect seizure in all situations with an accuracy of not less than 99.50%. At the same time, compared with the classification results of existing methods, this method is more effective. Epileptic seizures EEG signals KNN Hassanat distance representation Electrical engineering. Electronics. Nuclear engineering Zhiping Wang verfasserin aut Siqi Lv verfasserin aut Zhaohui Xu verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 73910-73923 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:73910-73923 https://doi.org/10.1109/ACCESS.2021.3081767 kostenfrei https://doaj.org/article/b7a92424254f481985f3532972281724 kostenfrei https://ieeexplore.ieee.org/document/9435418/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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 73910-73923
language	English
source	In IEEE Access 9(2021), Seite 73910-73923 volume:9 year:2021 pages:73910-73923
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institution	findex.gbv.de
topic_facet	Epileptic seizures EEG signals KNN Hassanat distance representation Electrical engineering. Electronics. Nuclear engineering
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container_title	IEEE Access
authorswithroles_txt_mv	Junying Na @@aut@@ Zhiping Wang @@aut@@ Siqi Lv @@aut@@ Zhaohui Xu @@aut@@
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callnumber-first	T - Technology
author	Junying Na
spellingShingle	Junying Na misc TK1-9971 misc Epileptic seizures misc EEG signals misc KNN misc Hassanat distance misc representation misc Electrical engineering. Electronics. Nuclear engineering An Extended K Nearest Neighbors-Based Classifier for Epilepsy Diagnosis
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topic_title	TK1-9971 An Extended K Nearest Neighbors-Based Classifier for Epilepsy Diagnosis Epileptic seizures EEG signals KNN Hassanat distance representation
topic	misc TK1-9971 misc Epileptic seizures misc EEG signals misc KNN misc Hassanat distance misc representation misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Epileptic seizures misc EEG signals misc KNN misc Hassanat distance misc representation misc Electrical engineering. Electronics. Nuclear engineering
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title	An Extended K Nearest Neighbors-Based Classifier for Epilepsy Diagnosis
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title_full	An Extended K Nearest Neighbors-Based Classifier for Epilepsy Diagnosis
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title_sort	extended k nearest neighbors-based classifier for epilepsy diagnosis
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title_auth	An Extended K Nearest Neighbors-Based Classifier for Epilepsy Diagnosis
abstract	In the diagnosis of epileptic seizures, classification is an important step that directly affects the results. Visual inspection of Electroencephalogram (EEG) is a relatively common analytic method of epilepsy, but it is costly, time-consuming and relies on the experiences of the doctor. Therefore, the development of an efficient and accurate epileptic seizure automatic diagnosis system suitable for clinical diagnosis has become an urgent task. In order to better solve the problem of early diagnosis of epileptic and bring timely treatment to patients, the comprehensive representation of k nearest neighbors for multi-distance decision making (CRMKNN) is proposed in this research. In the proposed scheme, Euclidean distance and Hassanat distance are firstly used to select neighbors. Subsequently, the similarity distance is obtained through the linear representation of the nearest neighbors, and calculate the distribution of nearest neighbors in the category to get the discrete distance. Finally, the distance based on the comprehensive representation of the category is used to determine the category of the query EEG signal. In order to verify the method, we used the EEG signals from Bonn university public database and conducted experiments on six kinds of EEG combinations. Experimental results showed that our method could automatically detect seizure in all situations with an accuracy of not less than 99.50%. At the same time, compared with the classification results of existing methods, this method is more effective.
abstractGer	In the diagnosis of epileptic seizures, classification is an important step that directly affects the results. Visual inspection of Electroencephalogram (EEG) is a relatively common analytic method of epilepsy, but it is costly, time-consuming and relies on the experiences of the doctor. Therefore, the development of an efficient and accurate epileptic seizure automatic diagnosis system suitable for clinical diagnosis has become an urgent task. In order to better solve the problem of early diagnosis of epileptic and bring timely treatment to patients, the comprehensive representation of k nearest neighbors for multi-distance decision making (CRMKNN) is proposed in this research. In the proposed scheme, Euclidean distance and Hassanat distance are firstly used to select neighbors. Subsequently, the similarity distance is obtained through the linear representation of the nearest neighbors, and calculate the distribution of nearest neighbors in the category to get the discrete distance. Finally, the distance based on the comprehensive representation of the category is used to determine the category of the query EEG signal. In order to verify the method, we used the EEG signals from Bonn university public database and conducted experiments on six kinds of EEG combinations. Experimental results showed that our method could automatically detect seizure in all situations with an accuracy of not less than 99.50%. At the same time, compared with the classification results of existing methods, this method is more effective.
abstract_unstemmed	In the diagnosis of epileptic seizures, classification is an important step that directly affects the results. Visual inspection of Electroencephalogram (EEG) is a relatively common analytic method of epilepsy, but it is costly, time-consuming and relies on the experiences of the doctor. Therefore, the development of an efficient and accurate epileptic seizure automatic diagnosis system suitable for clinical diagnosis has become an urgent task. In order to better solve the problem of early diagnosis of epileptic and bring timely treatment to patients, the comprehensive representation of k nearest neighbors for multi-distance decision making (CRMKNN) is proposed in this research. In the proposed scheme, Euclidean distance and Hassanat distance are firstly used to select neighbors. Subsequently, the similarity distance is obtained through the linear representation of the nearest neighbors, and calculate the distribution of nearest neighbors in the category to get the discrete distance. Finally, the distance based on the comprehensive representation of the category is used to determine the category of the query EEG signal. In order to verify the method, we used the EEG signals from Bonn university public database and conducted experiments on six kinds of EEG combinations. Experimental results showed that our method could automatically detect seizure in all situations with an accuracy of not less than 99.50%. At the same time, compared with the classification results of existing methods, this method is more effective.
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title_short	An Extended K Nearest Neighbors-Based Classifier for Epilepsy Diagnosis
url	https://doi.org/10.1109/ACCESS.2021.3081767 https://doaj.org/article/b7a92424254f481985f3532972281724 https://ieeexplore.ieee.org/document/9435418/ https://doaj.org/toc/2169-3536
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