A versatile computational algorithm for time-series data analysis and machine-learning models

Here we introduce Local Topological Recurrence Analysis (LoTRA), a simple computational approach for analyzing time-series data. Its versatility is elucidated using simulated data, Parkinsonian gait, and in vivo brain dynamics. We also show that this algorithm can be used to build a remarkably simpl...

Descripción completa

Detalles Bibliográficos
Autores principales: Chomiak, Taylor, Rasiah, Neilen P., Molina, Leonardo A., Hu, Bin, Bains, Jaideep S., Füzesi, Tamás
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Brief Communication
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8578326/
https://www.ncbi.nlm.nih.gov/pubmed/34753948
http://dx.doi.org/10.1038/s41531-021-00240-4
_version_ 1784596233529065472
author Chomiak, Taylor
Rasiah, Neilen P.
Molina, Leonardo A.
Hu, Bin
Bains, Jaideep S.
Füzesi, Tamás
author_facet Chomiak, Taylor
Rasiah, Neilen P.
Molina, Leonardo A.
Hu, Bin
Bains, Jaideep S.
Füzesi, Tamás
author_sort Chomiak, Taylor
collection PubMed
description Here we introduce Local Topological Recurrence Analysis (LoTRA), a simple computational approach for analyzing time-series data. Its versatility is elucidated using simulated data, Parkinsonian gait, and in vivo brain dynamics. We also show that this algorithm can be used to build a remarkably simple machine-learning model capable of outperforming deep-learning models in detecting Parkinson’s disease from a single digital handwriting test.
format Online
Article
Text
id pubmed-8578326
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-85783262021-11-15 A versatile computational algorithm for time-series data analysis and machine-learning models Chomiak, Taylor Rasiah, Neilen P. Molina, Leonardo A. Hu, Bin Bains, Jaideep S. Füzesi, Tamás NPJ Parkinsons Dis Brief Communication Here we introduce Local Topological Recurrence Analysis (LoTRA), a simple computational approach for analyzing time-series data. Its versatility is elucidated using simulated data, Parkinsonian gait, and in vivo brain dynamics. We also show that this algorithm can be used to build a remarkably simple machine-learning model capable of outperforming deep-learning models in detecting Parkinson’s disease from a single digital handwriting test. Nature Publishing Group UK 2021-11-09 /pmc/articles/PMC8578326/ /pubmed/34753948 http://dx.doi.org/10.1038/s41531-021-00240-4 Text en © The Author(s) 2021 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 Brief Communication
Chomiak, Taylor
Rasiah, Neilen P.
Molina, Leonardo A.
Hu, Bin
Bains, Jaideep S.
Füzesi, Tamás
A versatile computational algorithm for time-series data analysis and machine-learning models
title A versatile computational algorithm for time-series data analysis and machine-learning models
title_full A versatile computational algorithm for time-series data analysis and machine-learning models
title_fullStr A versatile computational algorithm for time-series data analysis and machine-learning models
title_full_unstemmed A versatile computational algorithm for time-series data analysis and machine-learning models
title_short A versatile computational algorithm for time-series data analysis and machine-learning models
title_sort versatile computational algorithm for time-series data analysis and machine-learning models
topic Brief Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8578326/
https://www.ncbi.nlm.nih.gov/pubmed/34753948
http://dx.doi.org/10.1038/s41531-021-00240-4
work_keys_str_mv AT chomiaktaylor aversatilecomputationalalgorithmfortimeseriesdataanalysisandmachinelearningmodels
AT rasiahneilenp aversatilecomputationalalgorithmfortimeseriesdataanalysisandmachinelearningmodels
AT molinaleonardoa aversatilecomputationalalgorithmfortimeseriesdataanalysisandmachinelearningmodels
AT hubin aversatilecomputationalalgorithmfortimeseriesdataanalysisandmachinelearningmodels
AT bainsjaideeps aversatilecomputationalalgorithmfortimeseriesdataanalysisandmachinelearningmodels
AT fuzesitamas aversatilecomputationalalgorithmfortimeseriesdataanalysisandmachinelearningmodels
AT chomiaktaylor versatilecomputationalalgorithmfortimeseriesdataanalysisandmachinelearningmodels
AT rasiahneilenp versatilecomputationalalgorithmfortimeseriesdataanalysisandmachinelearningmodels
AT molinaleonardoa versatilecomputationalalgorithmfortimeseriesdataanalysisandmachinelearningmodels
AT hubin versatilecomputationalalgorithmfortimeseriesdataanalysisandmachinelearningmodels
AT bainsjaideeps versatilecomputationalalgorithmfortimeseriesdataanalysisandmachinelearningmodels
AT fuzesitamas versatilecomputationalalgorithmfortimeseriesdataanalysisandmachinelearningmodels

Ejemplares similares