Abstract: Certain aspects of the present disclosure provide techniques and apparatus for distributing a workload across computing devices within a distributed computing system. An example method generally includes receiving, from at least one respective computing device of a plurality of computing devices in a distributed computing environment, information defining a respective power neural network. The respective power neural network generally is trained to predict power utilization for the respective computing device for a task to be executed on the respective computing device. For one or more computing devices, power utilization is predicted for a workload to be executed within the distributed computing environment based on respective power neural networks associated with the one or more computing devices. Instructions to execute at least a portion of the workload based on the predicted power utilizations for the one or more computing devices are transmitted to the plurality of computing devices.

Abstract: The present invention relates to an integrated system and method for EEG signal acquisition and interpretation, for neonatal seizure detection. The system as per the present invention comprises a control device, an integrated circuit, a wireless communication module, a visual indication means, and a power management module. The control device has a Convolutional Neural Network (CNN) embedded on it, and is programmed to configure the integrated circuit to receive a plurality of channels of EEG data from a plurality of EEG acquisition electrodes, and to amplify and digitize the received EEG data. The integrated circuit is further configured to segment the channels of EEG data to a plurality of sequential epochs of EEG data. The control device is configured to pass the sequential epochs of EEG data through the CNN which is pre-trained to output the probability of a seizure in the EEG data passed through it.

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.