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Self Aware Epilepsy Monitoring

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Epilepsy is a central nervous system disorder in which brain activity becomes abnormal, causing seizures or periods of unusual behavior, sensations, and sometimes loss of awareness. The golden diagnostic standard is represented by Electroencephalography (EEG) systems, which unfortunately are cumbersome and can make patients uncomfortable because of perceived stigmatization. Thus, both patients and caregivers would benefit from the availability of wearable long-term EEG monitoring devices. These long-term EEG monitoring devices must be robust to different noises or artifacts, which can be either external disturbances or movement of the patient that taints the EEG signal.

Project Description

In this project, the student works on designing and implementing a self-aware machine learning model to be used in a wearable system for real-time detection of epileptic seizures. The self-aware part of the system could for example be implemented in a way where the quality of the data is assessed before feeding it into a classifier and if the system is certain of good quality data that should be easy to classify, a more simple and energy-efficient classifier is used. On the other hand, some unseen data would need to be classified with a more complex classifier.

The resulting model/s would then be implemented on a real microcontroller and tested.

Status: Available

Looking for Semester and Master Project Students
Supervision: Thorir Mar Ingolfsson, Andrea Cossettini, Simone Benatti


  • 20% literature review
  • 80% Implementation


  • [1]F. Forooghifar et. al., A Self-Aware Epilepsy Monitoring System for Real-Time Epileptic Seizure Detection


Luca Benini

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