Application of machine learning analysis based on diffusion tensor imaging to identify REM sleep behavior disorder.
Sleep Breath
; 26(2): 633-640, 2022 06.
Article
em En
| MEDLINE
| ID: mdl-34236578
ABSTRACT
PURPOSE:
We evaluated the feasibility of machine learning analysis using diffusion tensor imaging (DTI) parameters to identify patients with idiopathic rapid eye movement (REM) sleep behavior disorder (RBD). We hypothesized that patients with idiopathic RBD could be identified via machine learning analysis based on DTI.METHODS:
We enrolled 20 patients with newly diagnosed idiopathic RBD at a tertiary hospital. We also included 20 healthy subjects as a control group. All of the subjects underwent DTI. We obtained the conventional DTI parameters and structural connectomic profiles from the DTI. We investigated the differences in conventional DTI measures and structural connectomic profiles between patients with idiopathic RBD and healthy controls. We then used machine learning analysis using a support vector machine (SVM) algorithm to identify patients with idiopathic RBD using conventional DTI and structural connectomic profiles.RESULTS:
Several regions showed significant differences in conventional DTI measures and structural connectomic profiles between patients with idiopathic RBD and healthy controls. The SVM classifier based on conventional DTI measures revealed an accuracy of 87.5% and an area under the curve of 0.900 to identify patients with idiopathic RBD. Another SVM classifier based on structural connectomic profiles yielded an accuracy of 75.0% and an area under the curve of 0.833.CONCLUSION:
Our findings demonstrate the feasibility of machine learning analysis based on DTI to identify patients with idiopathic RBD. The conventional DTI parameters might be more important than the structural connectomic profiles in identifying patients with idiopathic RBD.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Transtorno do Comportamento do Sono REM
/
Conectoma
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Sleep Breath
Assunto da revista:
NEUROLOGIA
/
OTORRINOLARINGOLOGIA
Ano de publicação:
2022
Tipo de documento:
Article