Abstract: The present invention discloses a method and system of providing a real time audification of neonatal EEG signals. The method comprises the steps of: receiving preprocessed neonatal EEG signals; changing a characteristic of the preprocessed signals in a phase vocoder; resampling the output signals from the vocoder to a predetermined audio frequency range; converting the resampled signals into stereo signals; and selecting a plurality of channels from the stereo signals as the output audio signals.

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.

Abstract: The present invention discloses a method and system of providing a real time audification of neonatal EEG signals. The method comprises the steps of: receiving preprocessed neonatal EEG signals; changing a characteristic of the preprocessed signals in a phase vocoder; resampling the output signals from the vocoder to a predetermined audio frequency range; converting the resampled signals into stereo signals; and selecting a plurality of channels from the stereo signals as the output audio signals.

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.