Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/71032

Machine Learning Based Diagnostics of Developmental Coordination Disorder using Electroencephalographic Data

File Size Format  
0337.pdf 904.18 kB Adobe PDF View/Open

Item Summary

Title:Machine Learning Based Diagnostics of Developmental Coordination Disorder using Electroencephalographic Data
Authors:Buettner, Ricardo
Buechele, Michael
Grimmeisen, Benedikt
Ulrich, Patrick
Keywords:Big Data on Healthcare Application
developmental coordination disorder
electroencephalography
healthcare
it
show 1 moremachine learning
show less
Date Issued:05 Jan 2021
Abstract:We report on promising results concerning the fast and accurate diagnosis of developmental coordination disorder (DCD) which heavily impacts the life of affected children with emotional and behavioral issues. Using a machine learning classifier on spectral data of electroencephalography (EEG) recordings and unfolding the traditional frequency bandwidth in a fine-graded equidistant 99-point spectrum we were able to reach an accuracy of over 99.35 percent having only one misclassification. Our machine learning work contributes to healthcare and information systems research. While current diagnostic methods in use are either complicated, time-consuming, or inaccurate, our automated machine-based approach is accurate and reliable. Our results also provide more insights into the relationship between DCD and brain activity which could stimulate future work in medicine.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/71032
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.416
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Big Data on Healthcare Application


Please email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.

This item is licensed under a Creative Commons License Creative Commons