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Related Concept Videos

REM Sleep Behavior Disorder01:15

REM Sleep Behavior Disorder

590
REM Sleep Behavior Disorder (RBD) is a sleep disorder characterized by the absence of muscle paralysis that normally occurs during the REM phase of sleep. This absence allows individuals to physically act out their dreams, which are often vivid and disturbing. Common behaviors exhibited during episodes include kicking, punching, and yelling. These actions can be dangerous, potentially leading to injuries for the person with RBD or their bed partner.
RBD is significantly associated with...
590

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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
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Application of machine learning analysis based on diffusion tensor imaging to identify REM sleep behavior disorder.

Dong Ah Lee1, Ho-Joon Lee2, Hyung Chan Kim1

  • 1Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Korea.

Sleep & Breathing = Schlaf & Atmung
|July 8, 2021
PubMed
Summary

Machine learning analysis of diffusion tensor imaging (DTI) can identify patients with idiopathic rapid eye movement (REM) sleep behavior disorder (RBD). Conventional DTI parameters showed higher accuracy than connectomic profiles in identifying RBD patients.

Keywords:
Diffusion tensor imagingMachine learningREM sleep

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Area of Science:

  • Neuroimaging
  • Neurology
  • Machine Learning

Background:

  • Idiopathic rapid eye movement (REM) sleep behavior disorder (RBD) is a neurodegenerative disease.
  • Early identification of RBD is crucial for timely intervention and management.

Purpose of the Study:

  • To evaluate the feasibility of using machine learning analysis of diffusion tensor imaging (DTI) parameters for identifying patients with idiopathic RBD.
  • To compare the diagnostic performance of conventional DTI measures versus structural connectomic profiles in identifying idiopathic RBD.

Main Methods:

  • 20 patients with idiopathic RBD and 20 healthy controls underwent DTI scans.
  • Conventional DTI parameters and structural connectomic profiles were extracted.
  • Support vector machine (SVM) algorithms were employed for machine learning analysis.

Main Results:

  • Significant differences in DTI measures and connectomic profiles were observed between RBD patients and controls.
  • An SVM classifier using conventional DTI parameters achieved 87.5% accuracy and an AUC of 0.900.
  • An SVM classifier using structural connectomic profiles achieved 75.0% accuracy and an AUC of 0.833.

Conclusions:

  • Machine learning analysis of DTI is a feasible method for identifying patients with idiopathic RBD.
  • Conventional DTI parameters appear more effective than structural connectomic profiles for RBD identification.