Cargando…

Ensemble deep model for continuous estimation of Unified Parkinson’s Disease Rating Scale III

BACKGROUND: Unified Parkinson Disease Rating Scale-part III (UPDRS III) is part of the standard clinical examination performed to track the severity of Parkinson’s disease (PD) motor complications. Wearable technologies could be used to reduce the need for on-site clinical examinations of people wit...

Descripción completa

Detalles Bibliográficos
Autores principales: Hssayeni, Murtadha D., Jimenez-Shahed, Joohi, Burack, Michelle A., Ghoraani, Behnaz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8010504/
https://www.ncbi.nlm.nih.gov/pubmed/33789666
http://dx.doi.org/10.1186/s12938-021-00872-w
_version_ 1783673078866247680
author Hssayeni, Murtadha D.
Jimenez-Shahed, Joohi
Burack, Michelle A.
Ghoraani, Behnaz
author_facet Hssayeni, Murtadha D.
Jimenez-Shahed, Joohi
Burack, Michelle A.
Ghoraani, Behnaz
author_sort Hssayeni, Murtadha D.
collection PubMed
description BACKGROUND: Unified Parkinson Disease Rating Scale-part III (UPDRS III) is part of the standard clinical examination performed to track the severity of Parkinson’s disease (PD) motor complications. Wearable technologies could be used to reduce the need for on-site clinical examinations of people with Parkinson’s disease (PwP) and provide a reliable and continuous estimation of the severity of PD at home. The reported estimation can be used to successfully adjust the dose and interval of PD medications. METHODS: We developed a novel algorithm for unobtrusive and continuous UPDRS-III estimation at home using two wearable inertial sensors mounted on the wrist and ankle. We used the ensemble of three deep-learning models to detect UPDRS-III-related patterns from a combination of hand-crafted features, raw temporal signals, and their time–frequency representation. Specifically, we used a dual-channel, Long Short-Term Memory (LSTM) for hand-crafted features, 1D Convolutional Neural Network (CNN)-LSTM for raw signals, and 2D CNN-LSTM for time–frequency data. We utilized transfer learning from activity recognition data and proposed a two-stage training for the CNN-LSTM networks to cope with the limited amount of data. RESULTS: The algorithm was evaluated on gyroscope data from 24 PwP as they performed different daily living activities. The estimated UPDRS-III scores had a correlation of [Formula: see text] and a mean absolute error of 5.95 with the clinical examination scores without requiring the patients to perform any specific tasks. CONCLUSION: Our analysis demonstrates the potential of our algorithm for estimating PD severity scores unobtrusively at home. Such an algorithm could provide the required motor-complication measurements without unnecessary clinical visits and help the treating physician provide effective management of the disease. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12938-021-00872-w.
format Online
Article
Text
id pubmed-8010504
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-80105042021-03-31 Ensemble deep model for continuous estimation of Unified Parkinson’s Disease Rating Scale III Hssayeni, Murtadha D. Jimenez-Shahed, Joohi Burack, Michelle A. Ghoraani, Behnaz Biomed Eng Online Research BACKGROUND: Unified Parkinson Disease Rating Scale-part III (UPDRS III) is part of the standard clinical examination performed to track the severity of Parkinson’s disease (PD) motor complications. Wearable technologies could be used to reduce the need for on-site clinical examinations of people with Parkinson’s disease (PwP) and provide a reliable and continuous estimation of the severity of PD at home. The reported estimation can be used to successfully adjust the dose and interval of PD medications. METHODS: We developed a novel algorithm for unobtrusive and continuous UPDRS-III estimation at home using two wearable inertial sensors mounted on the wrist and ankle. We used the ensemble of three deep-learning models to detect UPDRS-III-related patterns from a combination of hand-crafted features, raw temporal signals, and their time–frequency representation. Specifically, we used a dual-channel, Long Short-Term Memory (LSTM) for hand-crafted features, 1D Convolutional Neural Network (CNN)-LSTM for raw signals, and 2D CNN-LSTM for time–frequency data. We utilized transfer learning from activity recognition data and proposed a two-stage training for the CNN-LSTM networks to cope with the limited amount of data. RESULTS: The algorithm was evaluated on gyroscope data from 24 PwP as they performed different daily living activities. The estimated UPDRS-III scores had a correlation of [Formula: see text] and a mean absolute error of 5.95 with the clinical examination scores without requiring the patients to perform any specific tasks. CONCLUSION: Our analysis demonstrates the potential of our algorithm for estimating PD severity scores unobtrusively at home. Such an algorithm could provide the required motor-complication measurements without unnecessary clinical visits and help the treating physician provide effective management of the disease. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12938-021-00872-w. BioMed Central 2021-03-31 /pmc/articles/PMC8010504/ /pubmed/33789666 http://dx.doi.org/10.1186/s12938-021-00872-w Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Hssayeni, Murtadha D.
Jimenez-Shahed, Joohi
Burack, Michelle A.
Ghoraani, Behnaz
Ensemble deep model for continuous estimation of Unified Parkinson’s Disease Rating Scale III
title Ensemble deep model for continuous estimation of Unified Parkinson’s Disease Rating Scale III
title_full Ensemble deep model for continuous estimation of Unified Parkinson’s Disease Rating Scale III
title_fullStr Ensemble deep model for continuous estimation of Unified Parkinson’s Disease Rating Scale III
title_full_unstemmed Ensemble deep model for continuous estimation of Unified Parkinson’s Disease Rating Scale III
title_short Ensemble deep model for continuous estimation of Unified Parkinson’s Disease Rating Scale III
title_sort ensemble deep model for continuous estimation of unified parkinson’s disease rating scale iii
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8010504/
https://www.ncbi.nlm.nih.gov/pubmed/33789666
http://dx.doi.org/10.1186/s12938-021-00872-w
work_keys_str_mv AT hssayenimurtadhad ensembledeepmodelforcontinuousestimationofunifiedparkinsonsdiseaseratingscaleiii
AT jimenezshahedjoohi ensembledeepmodelforcontinuousestimationofunifiedparkinsonsdiseaseratingscaleiii
AT burackmichellea ensembledeepmodelforcontinuousestimationofunifiedparkinsonsdiseaseratingscaleiii
AT ghoraanibehnaz ensembledeepmodelforcontinuousestimationofunifiedparkinsonsdiseaseratingscaleiii