Lacy, Stuart Edward orcid.org/0000-0002-8570-7528, Smith, Stephen Leslie orcid.org/0000-0002-6885-2643 and Lones, Michael Adam (2018) Using Echo State Networks for Classification:A Case Study in Parkinson's Disease Diagnosis. Artificial intelligence in medicine. pp. 53-59. ISSN 0933-3657
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Elsevier, 2018. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. |
Keywords: | Parkinson's Disease, Echo State Networks, neurodegenerative disease |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Health Sciences (York) The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Depositing User: | Pure (York) |
Date Deposited: | 08 Feb 2018 15:00 |
Last Modified: | 22 Jan 2025 00:09 |
Published Version: | https://doi.org/10.1016/j.artmed.2018.02.002 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1016/j.artmed.2018.02.002 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:127251 |
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Licence: CC-BY-NC-ND 2.5