Abstract: A method of estimating the probability of a seizure in a subject, the method comprising: receiving historical data associated with epileptic events experienced by the subject over a first time period, the historical data comprising physiological data associated with each epileptic event and a time at which each epileptic event occurred; generating a temporal probability model of future epileptic events based on the time of each of the epileptic events, the temporal probability model representing a probability of a future seizure occurrence in each of a plurality of time windows; generating a probabilistic model based on the physiological data associated with each epileptic event; weighting the probabilistic model based on the temporal probability model to generate a weighted probabilistic model of future seizure activity; and outputting an estimate of seizure probability in the subject using the weighted probabilistic model.

Abstract: A method of estimating the probability of a seizure in a subject, the method comprising: receiving historical data associated with epileptic events experienced by the subject over a first time period, the historical data comprising physiological data associated with each epileptic event and a time at which each epileptic event occurred; generating a temporal probability model of future epileptic events based on the time of each of the epileptic events, the temporal probability model representing a probability of a future seizure occurrence in each of a plurality of time windows; generating a probabilistic model based on the physiological data associated with each epileptic event; weighting the probabilistic model based on the temporal probability model to generate a weighted probabilistic model of future seizure activity; and outputting an estimate of seizure probability in the subject using the weighted probabilistic model.

Abstract: A method comprises determining historical data associated with a subject experiencing epileptic events over a first time period, the historical data comprising non-EEG physiological data recorded over the first time period, and a time at which epileptic events occurred during the first time period. The method further comprises extracting from the non-EEG physiological data, one or more temporal models indicative of a subject specific cycle; and generating one or more temporal probabilistic models based on the respective one or more temporal models, the non-EEG physiological data, and the times at which each epileptic event occurred, wherein each temporal probabilistic model is representative of a probability of future seizure activity in each of a plurality of time windows. The method further comprises providing the one or more temporal probabilistic models for determining an estimate of seizure probability in the subject for one or more of the plurality of time windows.

Abstract: A method of estimating the probability of a seizure in a subject, the method comprising: receiving historical data associated with epileptic events experienced by the subject over a first time period, the historical data comprising physiological data associated with each epileptic event and a time at which each epileptic event occurred; generating a temporal probability model of future epileptic events based on the time of each of the epileptic events, the temporal probability model representing a probability of a future seizure occurrence in each of a plurality of time windows; generating a probabilistic model based on the physiological data associated with each epileptic event; weighting the probabilistic model based on the temporal probability model to generate a weighted probabilistic model of future seizure activity; and outputting an estimate of seizure probability in the subject using the weighted probabilistic model.

Abstract: A method of estimating the probability of a seizure in a subject, the method comprising: receiving historical data associated with epileptic events experienced by the subject over a first time period, the historical data comprising physiological data associated with each epileptic event and a time at which each epileptic event occurred; generating a temporal probability model of future epileptic events based on the time of each of the epileptic events, the temporal probability model representing a probability of a future seizure occurrence in each of a plurality of time windows; generating a probabilistic model based on the physiological data associated with each epileptic event; weighting the probabilistic model based on the temporal probability model to generate a weighted probabilistic model of future seizure activity; and outputting an estimate of seizure probability in the subject using the weighted probabilistic model.

Abstract: A method of estimating the probability of a seizure in a subject, the method comprising: receiving historical data associated with epileptic events experienced by the subject over a first time period, the historical data comprising physiological data associated with each epileptic event and a time at which each epileptic event occurred; generating a temporal probability model of future epileptic events based on the time of each of the epileptic events, the temporal probability model representing a probability of a future seizure occurrence in each of a plurality of time windows; generating a probabilistic model based on the physiological data associated with each epileptic event; weighting the probabilistic model based on the temporal probability model to generate a weighted probabilistic model of future seizure activity; and outputting an estimate of seizure probability in the subject using the weighted probabilistic model.