TY - JOUR
T1 - Decreased sleep spindle density in patients with idiopathic REM sleep behavior disorder and patients with Parkinson’s disease
AU - Christensen, Julie Anja Engelhard
AU - Kempfner, Jacob
AU - Zoetmulder, Marielle
AU - Leonthin, Helle L.
AU - Arvastson, Lars Johan
AU - Christensen, Søren Ro
AU - Sørensen, Helge Bjarup Dissing
AU - Jennum, Poul
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Sleep spindles
KW - Parkinson’s disease
KW - REM sleep behavior disorder
KW - Automatic detection
KW - Matching Pursuit
KW - Support Vector Machine
U2 - 10.1016/j.clinph.2013.08.013
DO - 10.1016/j.clinph.2013.08.013
M3 - Journal article
C2 - 24125856
SN - 1388-2457
VL - 125
SP - 512
EP - 519
JO - Clinical Neurophysiology
JF - Clinical Neurophysiology
IS - 3
ER -