High-performance Diagnosis of Sleep Disorders: A Novel, Accurate and Fast Machine Learning Approach Using Electroencephalographic Data

dc.contributor.author	Buettner, Ricardo
dc.contributor.author	Grimmeisen, Annika
dc.contributor.author	Gotschlich, Anne
dc.date.accessioned	2020-01-04T07:49:27Z
dc.date.available	2020-01-04T07:49:27Z
dc.date.issued	2020-01-07
dc.description.abstract	While diagnosing sleep disorders by physicians using electroencephalographic data is protracted and inaccurate, we report promising results from a novel, fast and reliable machine learning approach. Our approach only needs an electroencephalographic recording snippet of 10 minutes instead of eight hours to correctly classify the disorder with an accuracy of over 90 percent. The Rapid Eye Movement sleep behavior disorder can lead to secondary diseases like Parkinson or Dementia. Therefore, it is important to classify the disorder fast and with a high level of accuracy - which is now possible with our approach.
dc.format.extent	10 pages
dc.identifier.doi	10.24251/HICSS.2020.396
dc.identifier.isbn	978-0-9981331-3-3
dc.identifier.uri	http://hdl.handle.net/10125/64138
dc.language.iso	eng
dc.relation.ispartof	Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri	https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject	Big Data on Healthcare Application
dc.title	High-performance Diagnosis of Sleep Disorders: A Novel, Accurate and Fast Machine Learning Approach Using Electroencephalographic Data
dc.type	Conference Paper
dc.type.dcmi	Text

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