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1. 发明申请

US20170032098A1 STOCHASTIC OSCILLATOR ANALYSIS IN NEURO DIAGNOSITCS 审中-公开
标题翻译：神经元诊断中的STOCHASTIC振荡器分析
公开(公告)号：US20170032098A1
公开(公告)日：2017-02-02
申请号：US15302535
申请日：2015-04-07
申请人： Parham GHORBANIAN , Hashem ASHRAFIUON , Adam J. SIMON
发明人： Parham GHORBANIAN , Hashem ASHRAFIUON , Adam J. SIMON
IPC分类号： G06F19/00 , A61B5/0476 , A61B5/00
CPC分类号： A61B5/0476 , A61B5/0484 , A61B5/4088 , A61B5/7264 , A61B5/7275 , A61B5/7278 , A61B5/7282 , G16H50/50
摘要： A system and method for modeling bio-signals in the form of non-linear stochastic oscillators by extracting time series data from a subject into a series of summary fit parameters and comparing the unknown fit parameters to a set of normative fit parameters to determine whether the subject should be included in a group or not. The method includes data collection, feature extraction and then comparison of fit parameters from a non-linear stochastic oscillator model to a normative standard to make the in or out determination for a particular group. The system includes a processor programmed to perform the steps of the method.
摘要翻译：一种用非线性随机振荡器形式对生物信号进行建模的系统和方法，其通过从受试者提取时间序列数据为一系列概要拟合参数，并将未知拟合参数与一组规范拟合参数进行比较，以确定是否主题应该包括在一个群组中。该方法包括从非线性随机振荡器模型到规范标准的数据采集，特征提取，然后比较拟合参数，以对特定组进行进出确定。该系统包括被编程为执行该方法的步骤的处理器。

2. 发明申请

US20160029946A1 WAVELET ANALYSIS IN NEURO DIAGNOSTICS 审中-公开
标题翻译：神经诊断中的波形分析
公开(公告)号：US20160029946A1
公开(公告)日：2016-02-04
申请号：US14777030
申请日：2014-03-14
申请人： Adam J. SIMON , Hashem ASHRAFIUON , Parham GHORBANIAN
发明人： Adam J. SIMON , Hashem ASHRAFIUON , Parham GHORBANIAN
IPC分类号： A61B5/00 , A61B5/04 , A61B5/0476
CPC分类号： A61B5/4088 , A61B5/04012 , A61B5/0476 , A61B5/048 , A61B5/7203 , A61B5/726
摘要： 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.
摘要翻译：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。

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