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

BioMedical Engineering OnLine

Paulo Gil¹  Jorge Henriques¹  César Teixeira¹  Felipe Fernandes²  Ricardo Valentim²  Daniele Barros²  Ingridy Barbalho²  Mário Dourado Júnior² 

[1] Department of Informatics Engineering, Univ Coimbra, CISUC-Center for Informatics and Systems of the University of Coimbra, Coimbra, Portugal;Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, RN, Brazil;

关键词: Amyotrophic lateral sclerosis—ALS;    Artificial intelligence;    Biomedical signals;    Chronic neurological conditions;    Machine learning;    Motor neuron disease;    Signal processing;   

DOI  :  10.1186/s12938-021-00896-2

来源: Springer

【 摘 要 】

IntroductionThe 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 demystified. 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.MethodsThis Systematic Literature Review (SLR) consists of searching for and investigating primary studies that use ML techniques and biomedical signals related to ALS. Following the definition and execution of the SLR protocol, 18 articles met the inclusion, exclusion, and quality assessment criteria, and answered the SLR research questions.DiscussionsBased on the results, we identified 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%).ConclusionsDistinct 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.

【 授权许可】

CC BY