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A Comparative Evaluation of Machine Learning Deployment Approaches in Real Term Environments using the Example of the Detection of Epileptic Seizure

 
: Houta, Salima

:
Volltext urn:nbn:de:0011-n-6250282 (372 KByte PDF)
MD5 Fingerprint: 5b18ad5ac3c98fb1867c5d7b049ed06b
(CC) by-nc-nd
Erstellt am: 25.2.2021


54th Hawaii International Conference on System Sciences 2021. Proceedings : Grand Hyatt Kauai, Hawaii, USA
Honolulu/Hawaii: Univ. of Hawaii at Manoa, 2021
ISBN: 978-0-9981331-4-0
S.3390-3397
Hawaii International Conference on System Sciences (HICSS) <54, 2021, Online>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
16SV7482; EPItect
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer ISST ()
Big Data on Healthcare Application; deployment; epileptic seizures; machine learning framework; machine learning models

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.

: http://publica.fraunhofer.de/dokumente/N-625028.html