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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doaj-b7a92424254f481985f35329722817242021-06-02T23:18:18ZengIEEEIEEE Access2169-35362021-01-019739107392310.1109/ACCESS.2021.30817679435418An Extended K Nearest Neighbors-Based Classifier for Epilepsy DiagnosisJunying Na0https://orcid.org/0000-0003-4782-8836Zhiping Wang1https://orcid.org/0000-0003-4985-9003Siqi Lv2https://orcid.org/0000-0003-0925-3699Zhaohui Xu3https://orcid.org/0000-0003-1429-3281College of Science, Dalian Maritime University, Dalian, ChinaCollege of Science, Dalian Maritime University, Dalian, ChinaCollege of Science, Dalian Maritime University, Dalian, ChinaClinical Laboratory Department, The First Affiliated Hospital, Dalian Medical University, Dalian, ChinaIn 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.https://ieeexplore.ieee.org/document/9435418/Epileptic seizuresEEG signalsKNNHassanat distancerepresentation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Junying Na Zhiping Wang Siqi Lv Zhaohui Xu |
spellingShingle |
Junying Na Zhiping Wang Siqi Lv Zhaohui Xu An Extended K Nearest Neighbors-Based Classifier for Epilepsy Diagnosis IEEE Access Epileptic seizures EEG signals KNN Hassanat distance representation |
author_facet |
Junying Na Zhiping Wang Siqi Lv Zhaohui Xu |
author_sort |
Junying Na |
title |
An Extended K Nearest Neighbors-Based Classifier for Epilepsy Diagnosis |
title_short |
An Extended K Nearest Neighbors-Based Classifier for Epilepsy Diagnosis |
title_full |
An Extended K Nearest Neighbors-Based Classifier for Epilepsy Diagnosis |
title_fullStr |
An Extended K Nearest Neighbors-Based Classifier for Epilepsy Diagnosis |
title_full_unstemmed |
An Extended K Nearest Neighbors-Based Classifier for Epilepsy Diagnosis |
title_sort |
extended k nearest neighbors-based classifier for epilepsy diagnosis |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
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. |
topic |
Epileptic seizures EEG signals KNN Hassanat distance representation |
url |
https://ieeexplore.ieee.org/document/9435418/ |
work_keys_str_mv |
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