Investigating a Wearable Solution for Epileptic Seizure Forecasting: A Transdisciplinary Project​ 

Investigators:

  • Amberly Brigden (University of Bristol)
  • Liz Stuart (University of Plymouth)
  • Leandro Junges (University of Birmingham)
  • Rosie Charles (Neuronostics Ltd)
  • Phil Tittensor (Royal Wolverhampton NHS Trust)
  • Aravind Kumar Kamaraj (University of Birmingham)
  • Wessel Woldman (Neuronostics Ltd)

 

Funded: £34,472.43 over 12 months.  

Epilepsy is a chronic and complex neurological disorder affecting around 50 million people globally. The unpredictability of epileptic seizures is a major problem for those living with epilepsy.  

Seizure risk forecasting (seizure risk-assessment evaluating the likelihood of a seizure on a daily basis) could address the issue of unpredictability. There are real dangers of harm during seizure, and seizure forecasting could enable people to structure their environment and safety plan. Further, providing forecasts has the potential to reduce anxiety associated with unpredictability – not only does anxiety impact quality of life, but it is also a known trigger for seizure.  

For a forecasting solution to be workable in the users’ day-to-day life, it must involve devices that are acceptable and feasible to users (i.e., comfortable, non-intrusive). There are technologies which directly monitor brain activity; however, these cannot be used outside of clinical settings, or are intrusive. As an alternative, non-invasive, non-medical grade devices such as smartwatches, can capture data on variables known to be predictive of seizure (e.g., anxiety, sleep deprivation).  The overarching aim of this research is to investigate a seizure forecasting solution. This will involve (1) investigating mathematical models for forecasting seizure risk, (2) identifying off-the-shelf wearables devices/ sensing technologies, which are acceptable to users, and which capture the data needed for forecast modelling, and (3) designing the interface for user-reported data input and forecasting visualisations.

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