EMD-based data augmentation method applied to handwriting data for the diagnosis of Essential Tremor using LSTM networks
dc.contributor.author | Adrán Otero, José Fernando | |
dc.contributor.author | López de Ipiña Peña, Miren Karmele | |
dc.contributor.author | Solans Caballer, Óscar | |
dc.contributor.author | Martí Puig, Pere | |
dc.contributor.author | Sánchez Méndez, José Ignacio | |
dc.contributor.author | Iradi Arteaga, Jon | |
dc.contributor.author | Bergareche, Alberto | |
dc.contributor.author | Solé Casals, Jordi | |
dc.date.accessioned | 2022-10-20T13:24:14Z | |
dc.date.available | 2022-10-20T13:24:14Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Scientific Reports 12 : (2022) // Article ID 12819 | es_ES |
dc.identifier.issn | 2045-2322 | |
dc.identifier.uri | http://hdl.handle.net/10810/58136 | |
dc.description.abstract | The increasing capacity of today's technology represents great advances in diagnosing diseases using standard procedures supported by computer science. Deep learning techniques are able to extract the characteristics of temporal signals to study their patterns and diagnose diseases such as essential tremor. However, these techniques require a large amount of data to train the neural network and achieve good results, and the more data the network has, the more accurate the final model implemented. In this work we propose the use of a data augmentation technique to improve the accuracy of a Long short-term memory system in the diagnosis of essential tremor. For this purpose, the multivariate Empirical Mode Decomposition method will be used to decompose the original temporal signals collected from control subjects and patients with essential tremor. The time series obtained from the decomposition, covering different frequency ranges, will be randomly shuffled and combined to generate new artificial samples for each group. Then, both the generated artificial samples and part of the real samples will be used to train the LSTM network, and the remaining original samples will be used to test the model. The experimental results demonstrate the capability of the proposed method, which is compared to a set of 10 different data augmentation methods, and in all cases outperforms all other methods. In the best case, the proposed method increases the accuracy of the classifier from 83.20% to almost 93% when artificial samples are generated, which is a promising result when only small databases are available. | es_ES |
dc.description.sponsorship | This work is supported in part by the University of Vic-Central University of Catalonia (R0947), the Universidad del Pais Vasco/Euskal Herriko Unibertsitatea (PES22/30), the University of Cambridge, PPG 17/51 and GIU 092/19, the Basque Government (IT1489-22 and ELKARTEK21/109) and the Government of Gipuzkoa (2021-CIEN-000108-01). This work is also based upon work from COST Action CA18106 supported by COST (European Cooperation in Science and Technology). | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Nature | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.title | EMD-based data augmentation method applied to handwriting data for the diagnosis of Essential Tremor using LSTM networks | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © The Author(s) 2022. 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 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/. | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://www.nature.com/articles/s41598-022-16741-y | es_ES |
dc.identifier.doi | 10.1038/s41598-022-16741-y | |
dc.departamentoes | Ingeniería de sistemas y automática | es_ES |
dc.departamentoes | Organización de empresas | es_ES |
dc.departamentoeu | Enpresen antolakuntza | es_ES |
dc.departamentoeu | Sistemen ingeniaritza eta automatika | es_ES |
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Except where otherwise noted, this item's license is described as © The Author(s) 2022. 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 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/.