Abstract
The detection of epileptic seizures plays an important role in patient safety and therapy. Much research has been done in recent years to detect epileptic seizures using mobile devices. Although the variety of symptoms of certain types of seizures is challenging, progress has been made in identifying certain types of seizures. Machine learning is used in most work in an Experimental Environment. However, individual and situational aspects play an important role, especially in the detection of epileptic seizures. The improvement of seizure classification through machine learning in everyday life will play an important role in the further development of the technologies in the next few years. The EPItect project is researching the detection of epileptic seizures using an In-Ear sensor. A framework for machine learning for the Experimental and Real Term Environment was developed in the project. In this paper, we provide a comparative evaluation of different approaches to providing machine learning in the Real Term Environment.