A Fast Algorithm for Computing the Fourier Spectrum of a Fractional Period

Application of machine learning analysis based on diffusion tensor imaging to identify REM sleep behavior disorder

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.