Alexander Neergaard Olesen1, Matteo Cesari2, Julie Anja Engelhard Christensen3, Helge Bjarup Dissing Sorensen2, Emmanuel Mignot4, Poul Jennum3. 1. Department of Electrical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark. Electronic address: aneol@elektro.dtu.dk. 2. Department of Electrical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark. 3. Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, Glostrup, Denmark. 4. Stanford Center for Sleep Sciences and Medicine, Stanford University, Palo Alto, CA, USA.
Abstract
OBJECTIVE: To evaluate rapid eye movement (REM) muscular activity in narcolepsy by applying five algorithms to electromyogram (EMG) recordings, and to investigate its value for narcolepsy diagnosis. PATIENTS/ METHODS: A modified version of phasic EMG metric (mPEM), muscle activity index (MAI), REM atonia index (RAI), supra-threshold REM EMG activity metric (STREAM), and Frandsen method (FR) were calculated from polysomnography recordings of 20 healthy controls, 18 clinic controls (subjects suspected with narcolepsy but finally diagnosed without any sleep abnormality), 16 narcolepsy type one without REM sleep behavior disorder (RBD), nine narcolepsy type one with RBD, and 18 narcolepsy type two. Diagnostic value of metrics in differentiating between groups was quantified by area under the receiver operating characteristic curve (AUC). Correlations among the metrics and cerebrospinal fluid hypocretin-1 (CSF-hcrt-1) values were calculated using linear models. RESULTS: All metrics excluding STREAM found significantly higher muscular activity in narcolepsy one cases versus controls (p < 0.05). Moreover, RAI showed high sensitivity in the detection of RBD. The mPEM achieved the highest AUC in differentiating healthy controls from narcoleptic subjects. The RAI best differentiated between narcolepsy 1 and 2. Lower CSF-hcrt-1 values correlated with high muscular activity quantified by mPEM, sMAI, lMAI, PEM and FR (p < 0.05). CONCLUSIONS: This automatic analysis showed higher number of muscle activations in narcolepsy 1 compared to controls. This finding might play a supportive role in diagnosing narcolepsy and in discriminating narcolepsy subtypes. Moreover, the negative correlation between CSF-hcrt-1 level and REM muscular activity supported a role for hypocretin in the control of motor tone during REM sleep.
OBJECTIVE: To evaluate rapid eye movement (REM) muscular activity in narcolepsy by applying five algorithms to electromyogram (EMG) recordings, and to investigate its value for narcolepsy diagnosis. PATIENTS/ METHODS: A modified version of phasic EMG metric (mPEM), muscle activity index (MAI), REM atonia index (RAI), supra-threshold REM EMG activity metric (STREAM), and Frandsen method (FR) were calculated from polysomnography recordings of 20 healthy controls, 18 clinic controls (subjects suspected with narcolepsy but finally diagnosed without any sleep abnormality), 16 narcolepsy type one without REM sleep behavior disorder (RBD), nine narcolepsy type one with RBD, and 18 narcolepsy type two. Diagnostic value of metrics in differentiating between groups was quantified by area under the receiver operating characteristic curve (AUC). Correlations among the metrics and cerebrospinal fluid hypocretin-1 (CSF-hcrt-1) values were calculated using linear models. RESULTS: All metrics excluding STREAM found significantly higher muscular activity in narcolepsy one cases versus controls (p < 0.05). Moreover, RAI showed high sensitivity in the detection of RBD. The mPEM achieved the highest AUC in differentiating healthy controls from narcoleptic subjects. The RAI best differentiated between narcolepsy 1 and 2. Lower CSF-hcrt-1 values correlated with high muscular activity quantified by mPEM, sMAI, lMAI, PEM and FR (p < 0.05). CONCLUSIONS: This automatic analysis showed higher number of muscle activations in narcolepsy 1 compared to controls. This finding might play a supportive role in diagnosing narcolepsy and in discriminating narcolepsy subtypes. Moreover, the negative correlation between CSF-hcrt-1 level and REM muscular activity supported a role for hypocretin in the control of motor tone during REM sleep.