Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review

dc.contributorhttps://orcid.org/0000-0002-9462-2294
dc.creatorDourado Junior, Mário Emílio Teixeira
dc.creatorFernandes, Felipe
dc.creatorBarbalho, Ingridy
dc.creatorBarros, Daniele
dc.creatorValentim, Ricardo
dc.creatorTeixeira, César
dc.creatorHenriques, Jorge
dc.creatorGil, Paulo
dc.date2023-07-26T19:35:21Z
dc.date2023-07-26T19:35:21Z
dc.date2021
dc.date.accessioned2023-09-04T14:10:06Z
dc.date.available2023-09-04T14:10:06Z
dc.identifierFERNANDES, Felipe; BARBALHO, Ingridy; BARROS, Daniele; VALENTIM, Ricardo; TEIXEIRA, César; HENRIQUES, Jorge; GIL, Paulo; DOURADO JÚNIOR, Mário. Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review. Biomedical Engineering Online, [S.L.], v. 20, n. 1, p. 1, 15 jun. 2021. Springer Science and Business Media LLC. http://dx.doi.org/10.1186/s12938-021-00896-2. Disponível em: https://biomedical-engineering-online.biomedcentral.com/articles/10.1186/s12938-021-00896-2. Acesso em: 17 jul. 2023.
dc.identifierhttps://repositorio.ufrn.br/handle/123456789/54210
dc.identifierhttp://dx.doi.org/10.1186/s12938-021-00896-2
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8604018
dc.descriptionIntroduction: The use of machine learning (ML) techniques in healthcare encompasses an emerging concept that envisages vast contributions to the tackling of rare diseases. In this scenario, amyotrophic lateral sclerosis (ALS) involves complexities that are yet not demystifed. In ALS, the biomedical signals present themselves as potential biomarkers that, when used in tandem with smart algorithms, can be useful to applications within the context of the disease. Methods: This Systematic Literature Review (SLR) consists of searching for and investigating primary studies that use ML techniques and biomedical signals related to ALS. Following the defnition and execution of the SLR protocol, 18 articles met the inclusion, exclusion, and quality assessment criteria, and answered the SLR research questions. Discussions: Based on the results, we identifed three classes of ML applications combined with biomedical signals in the context of ALS: diagnosis (72.22%), communication (22.22%), and survival prediction (5.56%). Conclusions: Distinct algorithmic models and biomedical signals have been reported and present promising approaches, regardless of their classes. In summary, this SLR provides an overview of the primary studies analyzed as well as directions for the construction and evolution of technology-based research within the scope of ALS.
dc.formatapplication/pdf
dc.languageen
dc.publisherBiomedical Engineering Online
dc.rightsAttribution 3.0 Brazil
dc.rightshttp://creativecommons.org/licenses/by/3.0/br/
dc.rightsLOCKSS system has permission to collect, preserve, and serve this Archival Unit
dc.subjectamyotrophic lateral sclerosis—als
dc.subjectartifcial intelligence
dc.subjectbiomedical signals
dc.subjectchronic neurological conditions
dc.subjectmachine learning
dc.subjectmotor neuron disease
dc.subjectsignal processing
dc.titleBiomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review
dc.typearticle


Este ítem pertenece a la siguiente institución