{"product_id":"ieej-mbe17018","title":"Autoencoder error: a new feature for seizure detection","description":"\u003cp\u003e\u003cstrong\u003eカテゴリ: \u003c\/strong\u003e研究会(論文単位)\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e論文No: \u003c\/strong\u003eMBE17018\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eグループ名: \u003c\/strong\u003e【C】電子・情報・システム部門 医用・生体工学研究会\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e発行日: \u003c\/strong\u003e2017\/03\/20\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eタイトル(英語): \u003c\/strong\u003eAutoencoder error: a new feature for seizure detection\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e著者名: \u003c\/strong\u003eZiaratnia Sayyed Ali(東京大学),松尾 健(NTT東日本関東病院),川合 謙介(自治医科大学),高橋 宏知(東京大学)\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e著者名(英語): \u003c\/strong\u003eSayyed Ali Ziaratnia(The University of Tokyo),Takeshi Matsuo(NTT Medical Center Tokyo),Kensuke Kawai(Jichi Medical University),Hirokazu Takahashi(The University of Tokyo)\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eキーワード: \u003c\/strong\u003eEpilepsy|Seizure|Diagnose|EEG|Autoencoder\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e要約(日本語): \u003c\/strong\u003eTypical diagnoses of epilepsy based on long-term electroencephalogram (EEG) are time consuming, and thereby, automatic seizure detection system can be a helpful tool to diagnoses EEG for epileptologists. Conventional seizure detection was based on manually created features, none of which can be applicable for all of patients. In this study, hypothesizing that multi-channel EEG signals are compressible due to spatio-temporal coupling in a state-dependent manner, i.e., seizure or non-seizure, we attempted to use autoencoder (AE) error for seizure detection. Consequently, the AE error was able to classify the seizure and non-seizure states with an accuracy of 85% or more in our data, suggesting that AE error is a candidate of a universal feature for seizure detection.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e要約(英語): \u003c\/strong\u003eTypical diagnoses of epilepsy based on long-term electroencephalogram (EEG) are time consuming, and thereby, automatic seizure detection system can be a helpful tool to diagnoses EEG for epileptologists. Conventional seizure detection was based on manually created features, none of which can be applicable for all of patients. In this study, hypothesizing that multi-channel EEG signals are compressible due to spatio-temporal coupling in a state-dependent manner, i.e., seizure or non-seizure, we attempted to use autoencoder (AE) error for seizure detection. Consequently, the AE error was able to classify the seizure and non-seizure states with an accuracy of 85% or more in our data, suggesting that AE error is a candidate of a universal feature for seizure detection.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e原稿種別: \u003c\/strong\u003e英語\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePDFファイルサイズ: \u003c\/strong\u003e1,492 Kバイト\u003c\/p\u003e","brand":"IEEJ-PDF","offers":[{"title":"PDFダウンロード（一般価格330円\/会員価格220円） \/ A4 \/ 6","offer_id":46388652933359,"sku":"IEEJ-MBE17018-PDF","price":330.0,"currency_code":"JPY","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0718\/9512\/2159\/files\/IEEJ-PDF_0499b309-5a40-4e71-b74c-50a35695affd.png?v=1744513389","url":"https:\/\/ieej.bookpark.ne.jp\/products\/ieej-mbe17018","provider":"電気学会 電子図書館","version":"1.0","type":"link"}