Cargando…

Objective assessment of bradykinesia in Parkinson’s disease using evolutionary algorithms: clinical validation

BACKGROUND: There is an urgent need for developing objective, effective and convenient measurements to help clinicians accurately identify bradykinesia. The purpose of this study is to evaluate the accuracy of an objective approach assessing bradykinesia in finger tapping (FT) that uses evolutionary...

Descripción completa

Detalles Bibliográficos
Autores principales: Gao, Chao, Smith, Stephen, Lones, Michael, Jamieson, Stuart, Alty, Jane, Cosgrove, Jeremy, Zhang, Pingchen, Liu, Jin, Chen, Yimeng, Du, Juanjuan, Cui, Shishuang, Zhou, Haiyan, Chen, Shengdi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6094893/
https://www.ncbi.nlm.nih.gov/pubmed/30147869
http://dx.doi.org/10.1186/s40035-018-0124-x
_version_ 1783347883887558656
author Gao, Chao
Smith, Stephen
Lones, Michael
Jamieson, Stuart
Alty, Jane
Cosgrove, Jeremy
Zhang, Pingchen
Liu, Jin
Chen, Yimeng
Du, Juanjuan
Cui, Shishuang
Zhou, Haiyan
Chen, Shengdi
author_facet Gao, Chao
Smith, Stephen
Lones, Michael
Jamieson, Stuart
Alty, Jane
Cosgrove, Jeremy
Zhang, Pingchen
Liu, Jin
Chen, Yimeng
Du, Juanjuan
Cui, Shishuang
Zhou, Haiyan
Chen, Shengdi
author_sort Gao, Chao
collection PubMed
description BACKGROUND: There is an urgent need for developing objective, effective and convenient measurements to help clinicians accurately identify bradykinesia. The purpose of this study is to evaluate the accuracy of an objective approach assessing bradykinesia in finger tapping (FT) that uses evolutionary algorithms (EAs) and explore whether it can be used to identify early stage Parkinson’s disease (PD). METHODS: One hundred and seven PD, 41 essential tremor (ET) patients and 49 normal controls (NC) were recruited. Participants performed a standard FT task with two electromagnetic tracking sensors attached to the thumb and index finger. Readings from the sensors were transmitted to a tablet computer and subsequently analyzed by using EAs. The output from the device (referred to as "PD-Monitor") scaled from − 1 to + 1 (where higher scores indicate greater severity of bradykinesia). Meanwhile, the bradykinesia was rated clinically using the Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) FT item. RESULTS: With an increasing MDS-UPDRS FT score, the PD-Monitor score from the same hand side increased correspondingly. PD-Monitor score correlated well with MDS-UPDRS FT score (right side: r = 0.819, P = 0.000; left side: r = 0.783, P = 0.000). Moreover, PD-Monitor scores in 97 PD patients with MDS-UPDRS FT bradykinesia and each PD subgroup (FT bradykinesia scored from 1 to 3) were all higher than that in NC. Receiver operating characteristic (ROC) curves revealed that PD-Monitor FT scores could detect different severity of bradykinesia with high accuracy (≥89.7%) in the right dominant hand. Furthermore, PD-Monitor scores could discriminate early stage PD from NC, with area under the ROC curve greater than or equal to 0.899. Additionally, ET without bradykinesia could be differentiated from PD by PD-Monitor scores. A positive correlation of PD-Monitor scores with modified Hoehn and Yahr stage was found in the left hand sides. CONCLUSIONS: Our study demonstrated that a simple to use device employing classifiers derived from EAs could not only be used to accurately measure different severity of bradykinesia in PD, but also had the potential to differentiate early stage PD from normality.
format Online
Article
Text
id pubmed-6094893
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-60948932018-08-24 Objective assessment of bradykinesia in Parkinson’s disease using evolutionary algorithms: clinical validation Gao, Chao Smith, Stephen Lones, Michael Jamieson, Stuart Alty, Jane Cosgrove, Jeremy Zhang, Pingchen Liu, Jin Chen, Yimeng Du, Juanjuan Cui, Shishuang Zhou, Haiyan Chen, Shengdi Transl Neurodegener Research BACKGROUND: There is an urgent need for developing objective, effective and convenient measurements to help clinicians accurately identify bradykinesia. The purpose of this study is to evaluate the accuracy of an objective approach assessing bradykinesia in finger tapping (FT) that uses evolutionary algorithms (EAs) and explore whether it can be used to identify early stage Parkinson’s disease (PD). METHODS: One hundred and seven PD, 41 essential tremor (ET) patients and 49 normal controls (NC) were recruited. Participants performed a standard FT task with two electromagnetic tracking sensors attached to the thumb and index finger. Readings from the sensors were transmitted to a tablet computer and subsequently analyzed by using EAs. The output from the device (referred to as "PD-Monitor") scaled from − 1 to + 1 (where higher scores indicate greater severity of bradykinesia). Meanwhile, the bradykinesia was rated clinically using the Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) FT item. RESULTS: With an increasing MDS-UPDRS FT score, the PD-Monitor score from the same hand side increased correspondingly. PD-Monitor score correlated well with MDS-UPDRS FT score (right side: r = 0.819, P = 0.000; left side: r = 0.783, P = 0.000). Moreover, PD-Monitor scores in 97 PD patients with MDS-UPDRS FT bradykinesia and each PD subgroup (FT bradykinesia scored from 1 to 3) were all higher than that in NC. Receiver operating characteristic (ROC) curves revealed that PD-Monitor FT scores could detect different severity of bradykinesia with high accuracy (≥89.7%) in the right dominant hand. Furthermore, PD-Monitor scores could discriminate early stage PD from NC, with area under the ROC curve greater than or equal to 0.899. Additionally, ET without bradykinesia could be differentiated from PD by PD-Monitor scores. A positive correlation of PD-Monitor scores with modified Hoehn and Yahr stage was found in the left hand sides. CONCLUSIONS: Our study demonstrated that a simple to use device employing classifiers derived from EAs could not only be used to accurately measure different severity of bradykinesia in PD, but also had the potential to differentiate early stage PD from normality. BioMed Central 2018-08-16 /pmc/articles/PMC6094893/ /pubmed/30147869 http://dx.doi.org/10.1186/s40035-018-0124-x Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.
spellingShingle Research
Gao, Chao
Smith, Stephen
Lones, Michael
Jamieson, Stuart
Alty, Jane
Cosgrove, Jeremy
Zhang, Pingchen
Liu, Jin
Chen, Yimeng
Du, Juanjuan
Cui, Shishuang
Zhou, Haiyan
Chen, Shengdi
Objective assessment of bradykinesia in Parkinson’s disease using evolutionary algorithms: clinical validation
title Objective assessment of bradykinesia in Parkinson’s disease using evolutionary algorithms: clinical validation
title_full Objective assessment of bradykinesia in Parkinson’s disease using evolutionary algorithms: clinical validation
title_fullStr Objective assessment of bradykinesia in Parkinson’s disease using evolutionary algorithms: clinical validation
title_full_unstemmed Objective assessment of bradykinesia in Parkinson’s disease using evolutionary algorithms: clinical validation
title_short Objective assessment of bradykinesia in Parkinson’s disease using evolutionary algorithms: clinical validation
title_sort objective assessment of bradykinesia in parkinson’s disease using evolutionary algorithms: clinical validation
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6094893/
https://www.ncbi.nlm.nih.gov/pubmed/30147869
http://dx.doi.org/10.1186/s40035-018-0124-x
work_keys_str_mv AT gaochao objectiveassessmentofbradykinesiainparkinsonsdiseaseusingevolutionaryalgorithmsclinicalvalidation
AT smithstephen objectiveassessmentofbradykinesiainparkinsonsdiseaseusingevolutionaryalgorithmsclinicalvalidation
AT lonesmichael objectiveassessmentofbradykinesiainparkinsonsdiseaseusingevolutionaryalgorithmsclinicalvalidation
AT jamiesonstuart objectiveassessmentofbradykinesiainparkinsonsdiseaseusingevolutionaryalgorithmsclinicalvalidation
AT altyjane objectiveassessmentofbradykinesiainparkinsonsdiseaseusingevolutionaryalgorithmsclinicalvalidation
AT cosgrovejeremy objectiveassessmentofbradykinesiainparkinsonsdiseaseusingevolutionaryalgorithmsclinicalvalidation
AT zhangpingchen objectiveassessmentofbradykinesiainparkinsonsdiseaseusingevolutionaryalgorithmsclinicalvalidation
AT liujin objectiveassessmentofbradykinesiainparkinsonsdiseaseusingevolutionaryalgorithmsclinicalvalidation
AT chenyimeng objectiveassessmentofbradykinesiainparkinsonsdiseaseusingevolutionaryalgorithmsclinicalvalidation
AT dujuanjuan objectiveassessmentofbradykinesiainparkinsonsdiseaseusingevolutionaryalgorithmsclinicalvalidation
AT cuishishuang objectiveassessmentofbradykinesiainparkinsonsdiseaseusingevolutionaryalgorithmsclinicalvalidation
AT zhouhaiyan objectiveassessmentofbradykinesiainparkinsonsdiseaseusingevolutionaryalgorithmsclinicalvalidation
AT chenshengdi objectiveassessmentofbradykinesiainparkinsonsdiseaseusingevolutionaryalgorithmsclinicalvalidation