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: 2012/01/01タイトル(英語): Anomaly Detection using Multi-channel FLAC for Supporting Diagnosis of ECG著者名: Jiaxing Ye (Information Technology Research Institute, AIST), Takumi Kobayashi (Information Technology Research Institute, AIST), Masahiro Murakawa (Information Technology Research Institute, AIST), Tetsuya Higuchi (Information Technology Research Institu著者名(英語): Jiaxing Ye (Information Technology Research Institute, AIST), Takumi Kobayashi (Information Technology Research Institute, AIST), Masahiro Murakawa (Information Technology Research Institute, AIST), Tetsuya Higuchi (Information Technology Research Institute, AIST/Department of Computer Science, University of Tsukuba), Nobuyuki Otsu (Information Technology Research Institute, AIST)キーワード: abnormality detection，multi-channel signal processing，time-frequency analysis，local auto-correlation，subspace method要約(英語): In this paper, we propose an approach for abnormality detection in multi-channel ECG signals. This system serves as front end to detect the irregular sections in ECG signals, where symptoms may be observed. Thereby, the doctor can focus on only the detected suspected symptom sections, ignoring the disease-free parts. Hence the workload of the inspection by the doctors is significantly reduced and the diagnosis efficiency can be sharply improved. For extracting the predominant characteristics of multi-channel ECG signals, we propose multi-channel Fourier local auto-correlations (m-FLAC) features on multi-channel complex spectrograms. The method characterizes the amplitude and phase information as well as temporal dynamics of the multi-channel ECG signal. At the anomaly detection stage, we employ complex subspace method for statistically modeling the normal (healthy) ECG patterns as in one-class learning. Then, we investigate the input ECG signals by measuring its deviation distance to the trained subspace. The ECG sections with disordered spectral distributions can be effectively discerned based on such distance metric. To validate the proposed approach, we conducted experiments on ECG dataset. The experimental results demonstrated the effectiveness of the proposed approach including promising performance and high efficiency, compared to conventional methods.本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.132 No.1 （2012） 特集：確率的最適化と機械学習の統計的設計と応用本誌掲載ページ: 111-119 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/132/1/132_1_111/_article/-char/ja/
Related
Related