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Methods and systems for forecasting seizures

A technology for epilepsy and epilepsy, applied in the direction of epidemic alert systems, applications, diagnostic recording/measurement, etc., can solve problems such as being unsuitable for assessing probability prediction

Pending Publication Date: 2020-03-31
SEER MEDICAL PTY LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Despite recent advances in prediction, traditional rating measures for seizure anticipation continue to be based on class statements—seizures will or will not occur—and are ill-suited for rating probability predictions

Method used

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  • Methods and systems for forecasting seizures
  • Methods and systems for forecasting seizures
  • Methods and systems for forecasting seizures

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Embodiment Construction

[0108] Embodiments of the present disclosure provide a probabilistic approach to seizure prediction that incorporates fundamental patterns of seizure occurrence relative to other variables that affect the probability of a subject or patient having a seizure. test knowledge. In particular, there is substantial evidence that epilepsy follows a periodic pattern that regulates seizures and seizure susceptibility at certain times of day. The time period of highest seizure probability varies widely between patients but remains consistent over many years on an individual level. Tracking and exploiting temporal patterns of seizures presents exciting opportunities for enhanced patient management. This patient-specific temporal information can be used to titrate treatment and improve the performance of seizure prediction systems.

[0109] In addition to utilizing patient-specific time profiles to improve seizure prediction and forecasting systems, the inventors have recognized that we...

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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 probabilisticmodel of future seizure activity; and outputting an estimate of seizure probability in the subject using the weighted probabilistic model.

Description

technical field [0001] The present disclosure relates to methods and systems for predicting the likelihood of seizures in epileptic patients using probabilistic modeling. Background technique [0002] The unpredictability of seizures has profound implications for the safety of people with epilepsy. Accurate seizure prediction would greatly improve an individual's quality of life, potentially enabling pre-emptive therapy or allowing measures to ensure personal safety. [0003] It is recognized that many patients have measurable changes in brain state that precede seizures. Attempts have been made in the past to implement methods for predicting whether a seizure will occur based on these brain state changes. So far, such techniques for predicting seizures have been poorly generalizable due to the relatively short duration of available historical data. [0004] The first human trial of an implantable warning system was in Cook et al., 2013 (Prediction of seizure likelihood w...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/70G16H50/80A61B5/00A61B5/374A61B5/375
CPCA61B5/00A61B5/0205A61B5/4094A61B5/7275G16H50/30A61B5/316A61B5/24A61B5/374A61B5/375A61B5/021A61B5/024A61B5/1126A61B5/4266A61B5/4806A61B5/7267A61B5/742A61B2560/0252A61B2560/0257A61B2562/0219A61B2562/029
Inventor P·卡罗伊D·福利斯斗恩M·库克
Owner SEER MEDICAL PTY LTD
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