Using Echo State Networks for Classification: A Case Study in Parkinson's Disease Diagnosis

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Despite having notable advantages over established machine learning methods
for time series analysis, reservoir computing methods, such as echo state
networks (ESNs), have yet to be widely used for practical data mining applications.
In this paper, we address this deficit with a case study that
demonstrates how ESNs can be trained to predict disease labels when stimulated
with movement data. Since there has been relatively little prior research
into using ESNs for classification, we also consider a number of different
approaches for realising input-output mappings. Our results show
that ESNs can carry out effective classification and are competitive with existing
approaches that have significantly longer training times, in addition
to performing similarly with models employing conventional feature extraction
strategies that require expert domain knowledge. This suggests that
ESNs may prove beneficial in situations where predictive models must be
trained rapidly and without the benefit of domain knowledge, for example
on high-dimensional data produced by wearable medical technologies. This
application area is emphasized with a case study of Parkinson’s Disease patients
who have been recorded by wearable sensors while performing basic
movement tasks.
Original languageEnglish
Pages (from-to)53-59
Number of pages7
JournalArtificial intelligence in medicine
Early online date21 Feb 2018
Publication statusPublished - Mar 2018

Bibliographical note

© Elsevier, 2018. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.


  • Parkinson's Disease, Echo State Networks, neurodegenerative disease

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