Abstract: Systems and methods are provided for identifying, monitoring, and treating migraines and migraineurs. An electroencephalogram (EEG) of a patient is obtained. The EEG comprises a plurality of EEG signals. At least two features are extracted of a network feature across at least one pair of the plurality of EEG signals in the alpha frequency band, a feature derived from a signal decomposition of at least one EEG signal, and a feature representing the power spectrum density of at least one EEG signal. The patient is classified into one of a plurality of classes, each representing one of the presence of migraine symptoms, a response to migraine treatment, a type of migraineur, a current stage of a migraine, and a likelihood that the patient is a migraineur, according to the extracted at least two features.

Abstract: Systems and methods are provided for identifying, monitoring, and treating migraines and migraineurs. An electroencephalogram (EEG) of a patient is obtained. The EEG comprises a plurality of EEG signals. At least two features are extracted of a network feature across at least one pair of the plurality of EEG signals in the alpha frequency band, a feature derived from a signal decomposition of at least one EEG signal, and a feature representing the power spectrum density of at least one EEG signal. The patient is classified into one of a plurality of classes, each representing one of the presence of migraine symptoms, a response to migraine treatment, a type of migraineur, a current stage of a migraine, and a likelihood that the patient is a migraineur, according to the extracted at least two features .

Abstract: A method of segmenting images receives an image (such as a medical image) and a segment in relation to the image, displays them to an observer, receives a modification to the segment from the observer, and generates a second segment in relation to a second image, responsive to the modification. An image segmentation system includes a learning scheme or model to take input from an observer feedback interface and to communicate with a means for drawing an image segment to permit adjustment of at least one image segmentation parameter (such as a threshold value). The learning scheme is provided with a knowledge base which may initially be created by processing offline images. The learning scheme may use any scheme such as a reinforcement learning agent, a fuzzy inference system or a neural network.

Abstract: A method of segmenting images receives an image (such as a medical image) and a segment in relation to the image, displays them to an observer, receives a modification to the segment from the observer, and generates a second segment in relation to a second image, responsive to the modification. An image segmentation system includes a learning scheme or model to take input from an observer feedback interface and to communicate with a means for drawing an image segment to permit adjustment of at least one image segmentation parameter (such as a threshold value). The learning scheme is provided with a knowledge base which may initially be created by processing offline images. The learning scheme may use any scheme such as a reinforcement learning agent, a fuzzy inference system or a neural network.