Bioelectric signal classification using a recurrent probabilistic neural network with time-series discriminant component analysis

Hideaki Hayashi, Keisuke Shima, Taro Shibanoki, Yuichi Kurita, Toshio Tsuji

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

3 被引用数 (Scopus)

抄録

This paper outlines a probabilistic neural network developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower-dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model that incorporates a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into a neural network so that parameters can be obtained appropriately as network coefficients according to backpropagation-through-time- based training algorithm. The network is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. In the experiments conducted during the study, the validity of the proposed network was demonstrated for EEG signals.

本文言語英語
ホスト出版物のタイトル2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
ページ5394-5397
ページ数4
DOI
出版ステータス出版済み - 2013
外部発表はい
イベント2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, 日本
継続期間: 7 3 20137 7 2013

出版物シリーズ

名前Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN(印刷版)1557-170X

その他

その他2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
国/地域日本
CityOsaka
Period7/3/137/7/13

All Science Journal Classification (ASJC) codes

  • 信号処理
  • 生体医工学
  • コンピュータ ビジョンおよびパターン認識
  • 健康情報学

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