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体表面心電図における複数の特徴量を用いたサポートベクターマシンに基づく期外収縮検出アルゴリズムの改良

体表面心電図における複数の特徴量を用いたサポートベクターマシンに基づく期外収縮検出アルゴリズムの改良

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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: 2020/12/01

タイトル(英語): Improvement of Detection Algorithm of Extrasystoles Based on Support Vector Machine Using Multiple Features in Surface Electrocardiogram

著者名: 網敷 和樹(信州大学大学院総合理工学研究科),阿部 誠(信州大学工学部)

著者名(英語): Kazuki Amishiki (Graduate School of Science and Technology, Shinshu University), Makoto Abe (Faculty of Engineering, Shinshu University)

キーワード: 体表面心電図,期外収縮,サポートベクターマシン,誤り訂正出力符号  electrocardiogram,extrasystole,support vector machine,error-correcting output cording

要約(英語): The electrocardiograms (ECGs) are often used as barometers of not only the state of the heart but also the state of health. However, due to their high cost and complicated measurement, they have not been used daily at home. Recently, the development of wearable devices has made it possible to easily measure ECGs, so an analysis algorithm of ECGs that can be used as a preventive medicine have been required. With regard to the automatic analysis of ECGs, while there are many studies that use two-category classification for detecting premature ventricular contraction, few studies deal with multiple classification. In this study, a method of four-category classification was proposed: normal heartbeat, premature supraventricular contraction, premature ventricular contraction, and unspecified class. In the proposed method, a model combining the support vector machine and error-correcting output cording was constructed for 13 types of features obtained from ECG signals. The result of the four-category classification shows that classification accuracy was 99.56±0.26%. The result suggests that the proposed method can be used for early detection of diseases and preventive medicine.

本誌: 電気学会論文誌C(電子・情報・システム部門誌) Vol.140 No.12 (2020) 特集:電気・電子・情報関係学会東海支部連合大会

本誌掲載ページ: 1380-1385 p

原稿種別: 論文/日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/140/12/140_1380/_article/-char/ja/

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