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Multimodal Data for the Detection of Freezing of Gait in Parkinson’s Disease
Freezing of gaits (FOG) is a very disabling symptom of Parkinson’s Disease (PD), affecting about 50% of PD patients and 80% of advanced PD patients. Studies have shown that FOG is related to a complex interplay between motor, cognitive and affective factors. A full characterization of FOG is crucial...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9546845/ https://www.ncbi.nlm.nih.gov/pubmed/36207427 http://dx.doi.org/10.1038/s41597-022-01713-8 |
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author | Zhang, Wei Yang, Zhuokun Li, Hantao Huang, Debin Wang, Lipeng Wei, Yanzhao Zhang, Lei Ma, Lin Feng, Huanhuan Pan, Jing Guo, Yuzhu Chan, Piu |
author_facet | Zhang, Wei Yang, Zhuokun Li, Hantao Huang, Debin Wang, Lipeng Wei, Yanzhao Zhang, Lei Ma, Lin Feng, Huanhuan Pan, Jing Guo, Yuzhu Chan, Piu |
author_sort | Zhang, Wei |
collection | PubMed |
description | Freezing of gaits (FOG) is a very disabling symptom of Parkinson’s Disease (PD), affecting about 50% of PD patients and 80% of advanced PD patients. Studies have shown that FOG is related to a complex interplay between motor, cognitive and affective factors. A full characterization of FOG is crucial for FOG detection/prediction and prompt intervention. A protocol has been designed to acquire multimodal physical and physiological information during FOG, including gait acceleration (ACC), electroencephalogram (EEG), electromyogram (EMG), and skin conductance (SC). Two tasks were designed to trigger FOG, including gait initiation failure and FOG during walking. A total number of 12 PD patients completed the experiments and produced a length of 3 hours and 42 minutes of valid data including 2 hours and 14 minutes of normal gait and 1 hour and 28 minutes of freezing of gait. The FOG episodes were labeled by two qualified physicians. The multimodal data have been validated by a FOG detection task. |
format | Online Article Text |
id | pubmed-9546845 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-95468452022-10-09 Multimodal Data for the Detection of Freezing of Gait in Parkinson’s Disease Zhang, Wei Yang, Zhuokun Li, Hantao Huang, Debin Wang, Lipeng Wei, Yanzhao Zhang, Lei Ma, Lin Feng, Huanhuan Pan, Jing Guo, Yuzhu Chan, Piu Sci Data Data Descriptor Freezing of gaits (FOG) is a very disabling symptom of Parkinson’s Disease (PD), affecting about 50% of PD patients and 80% of advanced PD patients. Studies have shown that FOG is related to a complex interplay between motor, cognitive and affective factors. A full characterization of FOG is crucial for FOG detection/prediction and prompt intervention. A protocol has been designed to acquire multimodal physical and physiological information during FOG, including gait acceleration (ACC), electroencephalogram (EEG), electromyogram (EMG), and skin conductance (SC). Two tasks were designed to trigger FOG, including gait initiation failure and FOG during walking. A total number of 12 PD patients completed the experiments and produced a length of 3 hours and 42 minutes of valid data including 2 hours and 14 minutes of normal gait and 1 hour and 28 minutes of freezing of gait. The FOG episodes were labeled by two qualified physicians. The multimodal data have been validated by a FOG detection task. Nature Publishing Group UK 2022-10-07 /pmc/articles/PMC9546845/ /pubmed/36207427 http://dx.doi.org/10.1038/s41597-022-01713-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Zhang, Wei Yang, Zhuokun Li, Hantao Huang, Debin Wang, Lipeng Wei, Yanzhao Zhang, Lei Ma, Lin Feng, Huanhuan Pan, Jing Guo, Yuzhu Chan, Piu Multimodal Data for the Detection of Freezing of Gait in Parkinson’s Disease |
title | Multimodal Data for the Detection of Freezing of Gait in Parkinson’s Disease |
title_full | Multimodal Data for the Detection of Freezing of Gait in Parkinson’s Disease |
title_fullStr | Multimodal Data for the Detection of Freezing of Gait in Parkinson’s Disease |
title_full_unstemmed | Multimodal Data for the Detection of Freezing of Gait in Parkinson’s Disease |
title_short | Multimodal Data for the Detection of Freezing of Gait in Parkinson’s Disease |
title_sort | multimodal data for the detection of freezing of gait in parkinson’s disease |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9546845/ https://www.ncbi.nlm.nih.gov/pubmed/36207427 http://dx.doi.org/10.1038/s41597-022-01713-8 |
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