Artifactual component classification from MEG data using support vector machine

Montri Phothisonothai, Fang Duan, Hiroyuki Tsubomi, Aki Kondo, Kazuyuki Aihara, Yuko Yoshimura, Mitsuru Kikuchi, Yoshio Minabe, Katsumi Watanabe

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

Recently, an independent component analysis (ICA) has been proven to be an effective method for removing artifacts and noise in multi-channel physiological measures. ICA can extract independent component (IC) which was directly regarded as artifacts. In this paper, we propose an automatic method for classifying physiological artifacts from magnetoencephalogram (MEG) data. The artifactual ICs were classified based on support vector machine (SVM) algorithm. The following parameters: kurtosis (K), probability density (PD), central moment of frequency (CMoF), spectral entropy (SpecEn), and fractal dimension (FD) were used as input vector of SVM. The proposed method showed the average classification rates of 99.18%, 92.33%, and 98.15% for cardiac (EKG), ocular (EOG), and high-amplitude changes (HAM), respectively.

元の言語English
ホスト出版物のタイトル5th 2012 Biomedical Engineering International Conference, BMEiCON 2012
DOI
出版物ステータスPublished - 2012
イベント5th 2012 Biomedical Engineering International Conference, BMEiCON 2012 - Muang, Ubon Ratchathani, Thailand
継続期間: 2012 12 52012 12 7

出版物シリーズ

名前5th 2012 Biomedical Engineering International Conference, BMEiCON 2012

Other

Other5th 2012 Biomedical Engineering International Conference, BMEiCON 2012
Thailand
Muang, Ubon Ratchathani
期間12/12/512/12/7

ASJC Scopus subject areas

  • Biomedical Engineering

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