カテゴリ: 論文誌(論文単位)グループ名: 【C】電子・情報・システム部門発行日: 2019/09/01タイトル(英語): Complexity Analysis in Focal Epilepsy Using Entropy Methods著者名: Prajna Upadhyaya (Tokyo Institute of Technology), G. Muralidhar Bairy (Department of Biomedical Engineering Manipal Institute of Technology), Tohru Yagi (Tokyo Institute of Technology)著者名(英語): Prajna Upadhyaya (Tokyo Institute of Technology), G. Muralidhar Bairy (Department of Biomedical Engineering Manipal Institute of Technology), Tohru Yagi (Tokyo Institute of Technology)キーワード: EEG，Focal Epilepsy，Entropy，Computer-aided Diagnosis (CADx)要約(英語): Epilepsy is a neurological disorder characterized by abnormal electrical activity of neurons affecting a small or large area of the brain. Epilepsy is categorized into focal epilepsy and generalized epilepsy. Focal epilepsy is due to abnormal electrical activity that effects the smaller brain sections, whereas in generalized epilepsy the larger section of the brain is affected. Epilepsy affects the patient's personality, behavior, and emotions, making daily routines difficult. Early detection is therefore essential. In this study, we use electroencephalogram (EEG) signals to detect the focal epilepsy. However, due to the random and non-stationary characteristics it is difficult to analyze the subtle changes in the EEG by visual inspection. Hence, in this study we proposed a system that classifies focal epileptic and normal signals accurately. In order to analyze the difference in the randomness of the focal epileptic and the normal signals we have used fuzzy approximation entropy and spectral entropy. The system first decomposes the signal, and then features are extracted using fuzzy approximation entropy and spectral entropy. We obtain classification accuracies of 95% and 85% using 4-nearest neighbor and support vector machine classifiers, respectively.本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.139 No.9 （2019） 特集：知能メカトロニクス分野と連携する知覚情報技術本誌掲載ページ: 1027-1032 p原稿種別: 論文／英語電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/139/9/139_1027/_article/-char/ja/
