1 Department of Electrical Engineering, Technical University of Denmark2 Biomedical Engineering, Department of Electrical Engineering, Technical University of Denmark3 Copenhagen University Hospital4 H. Lundbeck A/S5 Copenhagen Center for Health Technology, Center, Technical University of Denmark
ObjectiveTo determine whether sleep spindles (SS) are potentially a biomarker for Parkinson’s disease (PD). MethodsFifteen PD patients with REM sleep behavior disorder (PD+RBD), 15 PD patients without RBD (PD−RBD), 15 idiopathic RBD (iRBD) patients and 15 age-matched controls underwent polysomnography (PSG). SS were scored in an extract of data from control subjects. An automatic SS detector using a Matching Pursuit (MP) algorithm and a Support Vector Machine (SVM) was developed and applied to the PSG recordings. The SS densities in N1, N2, N3, all NREM combined and REM sleep were obtained and evaluated across the groups. ResultsThe SS detector achieved a sensitivity of 84.7% and a specificity of 84.5%. At a significance level of α=1%, the iRBD and PD+RBD patients had a significantly lower SS density than the control group in N2, N3 and all NREM stages combined. At a significance level of α=5%, PD−RBD had a significantly lower SS density in N2 and all NREM stages combined. ConclusionsThe lower SS density suggests involvement in pre-thalamic fibers involved in SS generation. SS density is a potential early PD biomarker. SignificanceIt is likely that an automatic SS detector could be a supportive diagnostic tool in the evaluation of iRBD and PD patients.
Clinical Neurophysiology, 2014, Vol 125, Issue 3, p. 512-519
Journal Article; Research Support, Non-U.S. Gov't; Sleep spindles; Parkinson’s disease; REM sleep behavior disorder; Automatic detection; Matching Pursuit; Support Vector Machine