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    • 2. 发明申请
    • WAVELET ANALYSIS IN NEURO DIAGNOSTICS
    • 神经诊断中的波形分析
    • US20160029946A1
    • 2016-02-04
    • US14777030
    • 2014-03-14
    • Adam J. SIMONHashem ASHRAFIUONParham GHORBANIAN
    • Adam J. SIMONHashem ASHRAFIUONParham GHORBANIAN
    • A61B5/00A61B5/04A61B5/0476
    • A61B5/4088A61B5/04012A61B5/0476A61B5/048A61B5/7203A61B5/726
    • A method of extracting brain frequency sub bands corresponding to a medical condition such as Alzheimer's Disease from EEG time series data of a patient includes the steps of applying wavelet transforms to the EEG time series data to generate a continuous wavelet transformation time series at each wavelet scale, calculating Wavelet Entropy (WE) and Sample Entropy (SE) directly from the Continuous Wavelet Transformation time series at each wavelet scale, calculating arithmetic or geometric means and accumulations across scale ranges of interest; and selecting data from major brain frequency sub-bands as candidate sets of extraction features for analysis as a diagnostic signature for the medical condition. Diagnostic signatures for Alzheimer's disease are found when values of WE or SE are in certain ranges when EEG data is collected and analyzed in connection with certain analytical tasks such as an Eyes Open task.
    • 从患者的EEG时间序列数据中提取对应于诸如阿尔茨海默病等医学状况的脑频率子带的方法包括以下步骤:将小波变换应用于EEG时间序列数据,以在每个小波尺度产生连续的小波变换时间序列 ,从每个小波尺度的连续小波变换时间序列直接计算小波熵(WE)和采样熵(SE),计算各种尺度范围内的算术或几何平均值和积分; 并且从主要脑频率子带选择数据作为用于分析的提取特征的候选集合作为医疗状况的诊断签名。 当EEG数据收集和分析与某些分析任务如眼睛打开任务相关联时,可以发现阿尔茨海默病的诊断特征,当WE或SE的值在一定范围内。