Patents by Inventor Roya Hakimzadeh

Roya Hakimzadeh has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Combining predictive capabilities of Transcranial Doppler (TCD) with Electrocardiogram (ECG) to predict hemorrhagic shock
    Patent number: 8762308
    Abstract: 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.
    Type: Grant
    Filed: March 17, 2010
    Date of Patent: June 24, 2014
    Assignee: Virginia Commonwealth University
    Inventors: Kayvan Najarian, Kevin R. Ward, Soo-Yeon Ji, Roya Hakimzadeh
  • Combining Predictive Capabilities of Transcranial Doppler (TCD) with Electrocardiogram (ECG) to Predict Hemorrhagic Shock
    Publication number: 20120136224
    Abstract: 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.
    Type: Application
    Filed: March 17, 2010
    Publication date: May 31, 2012
    Inventors: Kayvan Najarian, Kevin R. Ward, Soo-Yeon Ji, Roya Hakimzadeh