Machine Learning Based Diagnostics of Developmental Coordination Disorder using Electroencephalographic Data

Date
2021-01-05
Authors
Buettner, Ricardo
Buechele, Michael
Grimmeisen, Benedikt
Ulrich, Patrick
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
3426
Ending Page
Alternative Title
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.
Description
Keywords
Big Data on Healthcare Application, developmental coordination disorder, electroencephalography, healthcare, it, machine learning
Citation
Extent
10 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 54th Hawaii International Conference on System Sciences
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.