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Predicting Axial Impairment in Parkinson’s Disease through a Single Inertial Sensor

Background: Current telemedicine approaches lack standardised procedures for the remote assessment of axial impairment in Parkinson’s disease (PD). Unobtrusive wearable sensors may be a feasible tool to provide clinicians with practical medical indices reflecting axial dysfunction in PD. This study...

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Autores principales: Borzì, Luigi, Mazzetta, Ivan, Zampogna, Alessandro, Suppa, Antonio, Irrera, Fernanda, Olmo, Gabriella
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8778464/
https://www.ncbi.nlm.nih.gov/pubmed/35062375
http://dx.doi.org/10.3390/s22020412
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author Borzì, Luigi
Mazzetta, Ivan
Zampogna, Alessandro
Suppa, Antonio
Irrera, Fernanda
Olmo, Gabriella
author_facet Borzì, Luigi
Mazzetta, Ivan
Zampogna, Alessandro
Suppa, Antonio
Irrera, Fernanda
Olmo, Gabriella
author_sort Borzì, Luigi
collection PubMed
description Background: Current telemedicine approaches lack standardised procedures for the remote assessment of axial impairment in Parkinson’s disease (PD). Unobtrusive wearable sensors may be a feasible tool to provide clinicians with practical medical indices reflecting axial dysfunction in PD. This study aims to predict the postural instability/gait difficulty (PIGD) score in PD patients by monitoring gait through a single inertial measurement unit (IMU) and machine-learning algorithms. Methods: Thirty-one PD patients underwent a 7-m timed-up-and-go test while monitored through an IMU placed on the thigh, both under (ON) and not under (OFF) dopaminergic therapy. After pre-processing procedures and feature selection, a support vector regression model was implemented to predict PIGD scores and to investigate the impact of L-Dopa and freezing of gait (FOG) on regression models. Results: Specific time- and frequency-domain features correlated with PIGD scores. After optimizing the dimensionality reduction methods and the model parameters, regression algorithms demonstrated different performance in the PIGD prediction in patients OFF and ON therapy (r = 0.79 and 0.75 and RMSE = 0.19 and 0.20, respectively). Similarly, regression models showed different performances in the PIGD prediction, in patients with FOG, ON and OFF therapy (r = 0.71 and RMSE = 0.27; r = 0.83 and RMSE = 0.22, respectively) and in those without FOG, ON and OFF therapy (r = 0.85 and RMSE = 0.19; r = 0.79 and RMSE = 0.21, respectively). Conclusions: Optimized support vector regression models have high feasibility in predicting PIGD scores in PD. L-Dopa and FOG affect regression model performances. Overall, a single inertial sensor may help to remotely assess axial motor impairment in PD patients.
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spelling pubmed-87784642022-01-22 Predicting Axial Impairment in Parkinson’s Disease through a Single Inertial Sensor Borzì, Luigi Mazzetta, Ivan Zampogna, Alessandro Suppa, Antonio Irrera, Fernanda Olmo, Gabriella Sensors (Basel) Article Background: Current telemedicine approaches lack standardised procedures for the remote assessment of axial impairment in Parkinson’s disease (PD). Unobtrusive wearable sensors may be a feasible tool to provide clinicians with practical medical indices reflecting axial dysfunction in PD. This study aims to predict the postural instability/gait difficulty (PIGD) score in PD patients by monitoring gait through a single inertial measurement unit (IMU) and machine-learning algorithms. Methods: Thirty-one PD patients underwent a 7-m timed-up-and-go test while monitored through an IMU placed on the thigh, both under (ON) and not under (OFF) dopaminergic therapy. After pre-processing procedures and feature selection, a support vector regression model was implemented to predict PIGD scores and to investigate the impact of L-Dopa and freezing of gait (FOG) on regression models. Results: Specific time- and frequency-domain features correlated with PIGD scores. After optimizing the dimensionality reduction methods and the model parameters, regression algorithms demonstrated different performance in the PIGD prediction in patients OFF and ON therapy (r = 0.79 and 0.75 and RMSE = 0.19 and 0.20, respectively). Similarly, regression models showed different performances in the PIGD prediction, in patients with FOG, ON and OFF therapy (r = 0.71 and RMSE = 0.27; r = 0.83 and RMSE = 0.22, respectively) and in those without FOG, ON and OFF therapy (r = 0.85 and RMSE = 0.19; r = 0.79 and RMSE = 0.21, respectively). Conclusions: Optimized support vector regression models have high feasibility in predicting PIGD scores in PD. L-Dopa and FOG affect regression model performances. Overall, a single inertial sensor may help to remotely assess axial motor impairment in PD patients. MDPI 2022-01-06 /pmc/articles/PMC8778464/ /pubmed/35062375 http://dx.doi.org/10.3390/s22020412 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Borzì, Luigi
Mazzetta, Ivan
Zampogna, Alessandro
Suppa, Antonio
Irrera, Fernanda
Olmo, Gabriella
Predicting Axial Impairment in Parkinson’s Disease through a Single Inertial Sensor
title Predicting Axial Impairment in Parkinson’s Disease through a Single Inertial Sensor
title_full Predicting Axial Impairment in Parkinson’s Disease through a Single Inertial Sensor
title_fullStr Predicting Axial Impairment in Parkinson’s Disease through a Single Inertial Sensor
title_full_unstemmed Predicting Axial Impairment in Parkinson’s Disease through a Single Inertial Sensor
title_short Predicting Axial Impairment in Parkinson’s Disease through a Single Inertial Sensor
title_sort predicting axial impairment in parkinson’s disease through a single inertial sensor
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8778464/
https://www.ncbi.nlm.nih.gov/pubmed/35062375
http://dx.doi.org/10.3390/s22020412
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