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    Combining Predictive Capabilities of Transcranial Doppler (TCD) with Electrocardiogram (ECG) to Predict Hemorrhagic Shock
    2.
    发明申请
    Combining Predictive Capabilities of Transcranial Doppler (TCD) with Electrocardiogram (ECG) to Predict Hemorrhagic Shock 失效
    将经颅多普勒（TCD）与心电图（ECG）的预测能力结合预测出血性休克

    公开(公告)号：US20120136224A1

    公开(公告)日：2012-05-31

    申请号：US13255549

    申请日：2010-03-17

    申请人： Kayvan Najarian ,  Kevin R. Ward ,  Soo-Yeon Ji ,  Roya Hakimzadeh

    发明人： Kayvan Najarian ,  Kevin R. Ward ,  Soo-Yeon Ji ,  Roya Hakimzadeh

    IPC分类号： A61B5/0402 ,  A61B5/021 ,  A61B5/053 ,  A61B8/06

    CPC分类号： A61B5/0402 ,  A61B5/026 ,  A61B5/0295 ,  A61B5/04014 ,  A61B5/7203 ,  A61B5/726 ,  A61B5/7267 ,  A61B5/7275 ,  A61B8/06 ,  A61B8/0808 ,  G06F19/00 ,  G06K9/00523 ,  G16H50/20

    摘要： A real-time decision-support system predicts hemorrhagic shock of a patient by analysis of electrocardiogram (ECG) signals and transcranial Doppler (TCD) signals from the patient. These signals are subject to signal decomposition using Discrete Wavelet Transform (DWT) to sets of wavelet coefficients and selecting significant signal features. Machine learning is applied to the significant features to evaluate and classify hypovolemia severity based on the input ECG and TCD signals from the patient. The classification of blood loss severity is displayed in real-time. An extension of the decision-support system integrates Arterial Blood Pressure (ABP) signals and thoracic electrical bio-impedance (DZT) signals with the ECG and TCD signals from the patient to evaluate severity of hypovolemia.

    摘要翻译： 实时决策支持系统通过分析来自患者的心电图（ECG）信号和经颅多普勒（TCD）信号来预测患者的出血性休克。 这些信号经过信号分解，使用离散小波变换（DWT）对小波系数的集合和选择显着的信号特征。 机器学习应用于显着特征，以根据患者的输入ECG和TCD信号评估和分类血容量不足严重程度。 失血严重程度分类显示实时显示。 决策支持系统的扩展将动脉血压（ABP）信号和胸部电生物阻抗（DZT）信号与患者的ECG和TCD信号相结合，以评估血容量不足的严重程度。

    Combining predictive capabilities of Transcranial Doppler (TCD) with Electrocardiogram (ECG) to predict hemorrhagic shock
    3.
    发明授权
    Combining predictive capabilities of Transcranial Doppler (TCD) with Electrocardiogram (ECG) to predict hemorrhagic shock 失效
    将经颅多普勒（TCD）与心电图（ECG）的预测能力结合预测出血性休克

    公开(公告)号：US08762308B2

    公开(公告)日：2014-06-24

    申请号：US13255549

    申请日：2010-03-17

    申请人： Kayvan Najarian ,  Kevin R. Ward ,  Soo-Yeon Ji ,  Roya Hakimzadeh

    发明人： Kayvan Najarian ,  Kevin R. Ward ,  Soo-Yeon Ji ,  Roya Hakimzadeh

    IPC分类号： G06N5/00

    CPC分类号： A61B5/0402 ,  A61B5/026 ,  A61B5/0295 ,  A61B5/04014 ,  A61B5/7203 ,  A61B5/726 ,  A61B5/7267 ,  A61B5/7275 ,  A61B8/06 ,  A61B8/0808 ,  G06F19/00 ,  G06K9/00523 ,  G16H50/20

    摘要： A real-time decision-support system predicts hemorrhagic shock of a patient by analysis of electrocardiogram (ECG) signals and transcranial Doppler (TCD) signals from the patient. These signals are subject to signal decomposition using Discrete Wavelet Transform (DWT) to sets of wavelet coefficients and selecting significant signal features. Machine learning is applied to the significant features to evaluate and classify hypovolemia severity based on the input ECG and TCD signals from the patient. The classification of blood loss severity is displayed in real-time. An extension of the decision-support system integrates Arterial Blood Pressure (ABP) signals and thoracic electrical bio-impedance (DZT) signals with the ECG and TCD signals from the patient to evaluate severity of hypovolemia.

    摘要翻译： 实时决策支持系统通过分析来自患者的心电图（ECG）信号和经颅多普勒（TCD）信号来预测患者的出血性休克。 这些信号经过信号分解，使用离散小波变换（DWT）对小波系数的集合和选择显着的信号特征。 机器学习应用于显着特征，以根据患者的输入ECG和TCD信号评估和分类血容量不足严重程度。 失血严重程度分类显示实时显示。 决策支持系统的扩展将动脉血压（ABP）信号和胸部电生物阻抗（DZT）信号与患者的ECG和TCD信号相结合，以评估血容量不足的严重程度。