Patents by Inventor Geraldine Bernadette Boylan

Geraldine Bernadette Boylan 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).

  • Method for the real-time identification of seizures in an electroencephalogram (EEG) signal
    Patent number: 10433752
    Abstract: The present invention relates to a method for the real-time identification of seizures in an Electroencephalogram (EEG) signal. The method provides for patient-independent seizure identification by use of a multi-patient trained generic Support Vector Machine (SVM) classifier. The SVM classifier is operates on a large feature vector combining features from a wide variety of signal processing and analysis techniques. The method operates sufficiently accurately to be suitable for use in a clinical environment. The method may also be combined with additional classifiers, such a Gaussian Mixture Model (GMM) classifier, for improved robustness, and one or more dynamic classifiers such as an SVM using sequential kernels for improved temporal analysis of the EEG signal.
    Type: Grant
    Filed: April 7, 2010
    Date of Patent: October 8, 2019
    Assignee: National University of Ireland
    Inventors: Stephen Daniel Faul, Andriy Temko, William Peter Marnane, Gordon Lightbody, Geraldine Bernadette Boylan
  • METHOD FOR THE REAL-TIME IDENTIFICATION OF SEIZURES IN AN ELECTROENCEPHALOGRAM (EEG) SIGNAL
    Publication number: 20120101401
    Abstract: The present invention relates to a method for the real-time identification of seizures in an Electroencephalogram (EEG) signal. The method provides for patient-independent seizure identification by use of a multi-patient trained generic Support Vector Machine (SVM) classifier. The SVM classifier is operates on a large feature vector combining features from a wide variety of signal processing and analysis techniques. The method operates sufficiently accurately to be suitable for use in a clinical environment. The method may also be combined with additional classifiers, such a Gaussian Mixture Model (GMM) classifier, for improved robustness, and one or more dynamic classifiers such as an SVM using sequential kernels for improved temporal analysis of the EEG signal.
    Type: Application
    Filed: April 7, 2010
    Publication date: April 26, 2012
    Applicant: National University of Ireland
    Inventors: Stephen Daniel Faul, Andrly Temko, William Peter Marnane, Gordon Lightbody, Geraldine Bernadette Boylan