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SCCMI Algorithm for Coupling Analysis of Multi-Lead Epileptic EEG Signals
SCCMI Algorithm for Coupling Analysis of Multi-Lead Epileptic EEG Signals
by Lu, Wei , Gao, Meng Qiu , Song, De Bo , Han, Ting Ting , Zou, Chao , Cui, Chao
2013
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SCCMI Algorithm for Coupling Analysis of Multi-Lead Epileptic EEG Signals
by Lu, Wei , Gao, Meng Qiu , Song, De Bo , Han, Ting Ting , Zou, Chao , Cui, Chao
in
2013
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SCCMI Algorithm for Coupling Analysis of Multi-Lead Epileptic EEG Signals
SCCMI Algorithm for Coupling Analysis of Multi-Lead Epileptic EEG Signals
by Lu, Wei , Gao, Meng Qiu , Song, De Bo , Han, Ting Ting , Zou, Chao , Cui, Chao
2013

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SCCMI Algorithm for Coupling Analysis of Multi-Lead Epileptic EEG Signals
SCCMI Algorithm for Coupling Analysis of Multi-Lead Epileptic EEG Signals
Journal Article

SCCMI Algorithm for Coupling Analysis of Multi-Lead Epileptic EEG Signals

Lu, Wei,
Gao, Meng Qiu,
Song, De Bo,
Han, Ting Ting,
Zou, Chao,
Cui, Chao
2013
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Overview
Symbolic-code condition mutual information (SCCMI) algorithm is proposed,which can detect coupling between several systems.SCCMI combines condition mutual information with symbolic-code algorithm. Condition mutual information entropy is used to finding coupling degree between time series .The meaning of symbolic-code algorithm is to retention large scale information of time sequence, whats more ,reduce noise effect. SCCMI algorithm is used to analyze difference of coupling between epileptic EEG signals and normal ones .Hypothesis testing was done by SPSS.It turns out that the difference between epileptic EEG signals and normal ones is significant.SCCMI algorithm is proved to be effective. And coupling degree can be used as a parameter to measure if brain is healthy.
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Publisher
Trans Tech Publications Ltd
ISBN
3037858060, 9783037858066

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