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Objective: Epilepsy is a neurological disorder arising from anomalies of the electrical activity in the brain, affecting ~65 million individuals worldwide. Prediction methods, typically based on machine learning methods, require a large amount of data for training, in order to correctly classify seizures with small false alarm rates. Methods: In this work, we present a new database containing EEG scalp signals of 14 epileptic patients acquired at the Unit of Neurology and Neurophysiology of the University of Siena, Italy. Furthermore, a patient-specific seizure prediction method, based on the detection of synchronization patterns in the EEG, is proposed and tested on the data of the database. The use of noninvasive EEG data aims to explore the possibility of developing a noninvasive monitoring/control device for the prediction of seizures. The prediction method employs synchronization measures computed over all channel pairs and a computationally inexpensive threshold-based classification approach. Results and conclusions: The experimental analysis, performed by inspection and by the proposed threshold-based classifier on all the patients of the database, shows that the features extracted by the synchronization measures are able to detect preictal and ictal states and allow the prediction of the seizures few minutes before the seizure onsets.

Epilepsy is a neurological disorder arising from anomalies of the electrical activity in the brain, affecting about 65 millions individuals worldwide. Objective: This work proposes a patient-specific approach for short-term prediction (i.e., within few minutes) of epileptic seizures. Methods: We use noninvasive EEG data, since the aim is exploring the possibility of developing a noninvasive monitoring/control device for the prediction of seizures. Our approach is based on finding synchronization patterns in the EEG that allow to distinguish in real time preictal from interictal states. In practice, we develop easily computable functions over a graph model to capture the variations in the synchronization, and employ a classifier for identifying the preictal state. Results: We compare two state-of-the-art classification algorithms and a simple and computationally inexpensive threshold-based classifier developed ad hoc. Results on publicly available scalp EEG database and on scalp data of the patients of the Unit of Neurology and Neurophysiology at University of Siena show that this simple and computationally viable processing is able to highlight the changes in synchronization when a seizure is approaching. Conclusion and significance:The proposed approach, characterized by low computational requirements and by the use of noninvasive techniques, is a step toward the development of portable and wearable devices for real-life use.

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In this paper we address a problem arising in a manufacturing environment in which the owner of a machining resource, besides its own jobs, must process also jobs belonging to an external party. Due to contract commitments, the jobs of the external party must be processed within a certain due date, that can be considered as a deadline. Moreover, these jobs have a given release date corresponding to their actual availability in the manufacturing system. The external party jobs arrive with an estimate of their durations and the owner of the machining resource, when planning the schedule of its jobs, must take into account possible deviations from the nominal values of the external processing times. It is therefore important to determine a schedule which is robust to any possible changes in the durations of the external jobs. We consider the total completion time objective for the owner of the machining resource. However, due to the uncertainty on the external jobs processing times, we seek for a solution minimizing the maximum absolute deviation from an optimal schedule. We derive some properties of such robust schedules and evaluate the performance two mixed integer linear programming formulations