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+.