Context-related data processing in artificial neural networks for higher reliability of telerehabilitation systems

Maciej Huk , Jolanta Mizera-Pietraszko

Abstract

Classification is a data processing technique of a great significance both for native eHealth systems and web telemedicine solutions. In this sense, artificial neural networks have been widely applied in telerehabilitation as powerful tools to process information and acquire a new medical knowledge. But effective analysis of multidimensional heterogeneous medical data, still poses considerable difficulties. It was shown that processing too many data features simultaneously is costly and has some adverse effects on the resulting models classification properties. Therefore, there is a strong need to develop new techniques for selecting features from the very large data sets that include many irrelevant, or redundant features. This work addresses the context-related feature selection problem from medical data by proposing utility of Sigma-if neural network being an effective model of neurology patients's low-level distributed selective attention mechanisms. Our experiments indicate that a context-aware technique can reduce the average cost of medical data acquisition and data processing as well as it can decrease classification error probability resulting in increasing the overall eHealth systems reliability.
Author Maciej Huk - [Politechnika Wrocławska (PWr)]
Maciej Huk,,
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- Politechnika Wrocławska
, Jolanta Mizera-Pietraszko (FMPCS / IoMaI)
Jolanta Mizera-Pietraszko,,
- Institute of Mathematics and Informatics
Pages217-221
Publication size in sheets0.5
Book 17th International Conference on E-health Networking, Application & Services (HealthCom 2015), Boston, Massachussetts, USA, 14-17 October 2015, 2015, Institute of Electrical and Electronics Engineers ( IEEE ), ISBN 978-1-4673-8325-7, [978-1-4673-8326-4], 679 p.
DOIDOI:10.1109/HealthCom.2015.7454501
URL http://ieeexplore.ieee.org/document/7454501/
Languageen angielski
Score (nominal)15
ScoreMinisterial score = 15.0, 11-10-2019, BookChapterMatConfByIndicator
Ministerial score (2013-2016) = 15.0, 11-10-2019, BookChapterMatConfByIndicator
Publication indicators WoS Citations = 0
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