EpihunterDetects, logs and signals
silent, difficult to notice
epileptic absence seizures.

Absence seizures are the silent, often unnoticed epilepsy seizures which pauses the person’s brain and causes the affected person to not register anything in their vicinity for a short period heime. These seizures occur at random and their frequency can vary significantly from a couple of seizures per day to a couple of seizures per month. They have a tremendous impact on the quality of life of 150,000 young children. Unawareness of the person’s environment results in low self-esteem and frustration.

Using electroencephalogram (EEG) data, it is possible to record and detect these seizures. In combination with a headset, Epihunter developed an Android application capable of monitoring live EEG-data and showing feedback if a seizure was detected.

All recorded data gets sent to an online web platform, which the users can access using a dashboard.

The solution Sentigrate worked out consisted of 3 steps:

1. Seizure detection:
   • Labeling data with a self-developed annotation tool
   • Data preprocessing and handling different kinds of noise
   • Developing a suitable machine learning model with Tensorflow
   • Evaluating relevant metrics and monitoring the selected model’s results
2. Running the algorithm: in order to detect the seizures, the algorithm should process the data and detect the seizures in real-time. Therefore Sentigrate co-developed an Android application capable of running the Tensforflow model and providing appropriate audiovisual feedback to the user.
3. Data visualization: an online secure dashboard was created allowing users to gain insight in their seizure frequency and at what time seizures typically occur. The dashboard was developed with Angular 4+.