Gertrud Laura Sørensen

Sleep transitions in hypocretin-deficient narcolepsy.

STUDY OBJECTIVES Narcolepsy is characterized by instability of sleep-wake, tonus, and rapid eye movement (REM) sleep regulation. It is associated with severe hypothalamic hypocretin deficiency, especially in patients with cataplexy (loss of tonus). As the hypocretin neurons coordinate and stabilize the brains sleep-wake pattern, tonus, and REM flip-flop neuronal centers in animal models, we set out to determine whether hypocretin deficiency and/or cataplexy predicts the unstable sleep-wake and REM sleep pattern of the human phenotype. DESIGN We measured the frequency of transitions in patients with narcolepsy between sleep-wake states and to/from REM and NREM sleep stages. Patients were subdivided by the presence of +/- cataplexy and +/- hypocretin-1 deficiency. SETTING Sleep laboratory studies conducted from 2001-2011. PATIENTS In total 63 narcolepsy patients were included in the study. Cataplexy was present in 43 of 63 patients and hypocretin-1 deficiency was present in 37 of 57 patients. MEASUREMENTS AND RESULTS Hypocretin-deficient patients with narcolepsy had a significantly higher frequency of sleep-wake transitions (P = 0.014) and of transitions to/from REM sleep (P = 0.044) than patients with normal levels of hypocretin-1. Patients with cataplexy had a significantly higher frequency of sleep-wake transitions (P = 0.002) than those without cataplexy. A multivariate analysis showed that transitions to/from REM sleep were predicted mainly by hypocretin-1 deficiency (P = 0.011), whereas sleep-wake transitions were predicted mainly by cataplexy (P = 0.001). CONCLUSIONS In human narcolepsy, hypocretin deficiency and cataplexy are both associated with signs of destabilized sleep-wake and REM sleep control, indicating that the disorder may serve as a human model for the sleep-wake and REM sleep flip-flop switches.

Attenuated Heart Rate Response is Associated with Hypocretin Deficiency in Patients with Narcolepsy

STUDY OBJECTIVE Several studies have suggested that hypocretin-1 may influence the cerebral control of the cardiovascular system. We analyzed whether hypocretin-1 deficiency in narcolepsy patients may result in a reduced heart rate response. DESIGN We analyzed the heart rate response during various sleep stages from a 1-night polysomnography in patients with narcolepsy and healthy controls. The narcolepsy group was subdivided by the presence of +/- cataplexy and +/- hypocretin-1 deficiency. SETTING Sleep laboratory studies conducted from 2001-2011. PARTICIPANTS In total 67 narcolepsy patients and 22 control subjects were included in the study. Cataplexy was present in 46 patients and hypocretin-1 deficiency in 38 patients. INTERVENTIONS None. MEASUREMENTS AND RESULTS All patients with narcolepsy had a significantly reduced heart rate response associated with arousals and leg movements (P < 0.05). Heart rate response associated with arousals was significantly lower in the hypocretin-1 deficiency and cataplexy groups compared with patients with normal hypocretin-1 levels (P < 0.04) and patients without cataplexy (P < 0.04). Only hypocretin-1 deficiency significantly predicted the heart rate response associated with arousals in both REM and non-REM in a multivariate linear regression. CONCLUSIONS Our results show that autonomic dysfunction is part of the narcoleptic phenotype, and that hypocretin-1 deficiency is the primary predictor of this dysfunction. This finding suggests that the hypocretin system participates in the modulation of cardiovascular function at rest.

Pulse wave velocity in the carotid artery

The pulse wave velocity (PWV) in the carotid artery (CA) has been estimated based on ultrasound data collected by the experimental scanner RASMUS at DTU. Data is collected from one test subject using a frame rate (FR) of 4000 Hz. The influence of FRs is also investigated. The PWV is calculated from distension wave forms (DWF) estimated using cross-correlation. The obtained velocities give results in the area between 3-4 m/s, and the deviations between estimated PWV from two beats of a pulse are around 10%. The results indicate that the method presented is applicable for detecting the local PWV. Additional studies with data collections from several test subjects are required to determine the accuracy of the approach. Based on a spectrum analysis it appears that there is no gain from using FRs above 1000 Hz, but it is shown that FRs below 1000 Hz do not give accurate PWVs.

Attenuated heart rate response in REM sleep behavior disorder and Parkinson's disease†‡§

The objective of this study was to determine whether patients with Parkinsons disease with and without rapid‐eye‐movement sleep behavior disorder and patients with idiopathic rapid‐eye‐movement sleep behavior disorder have an attenuated heart rate response to arousals or to leg movements during sleep compared with healthy controls. Fourteen and 16 Parkinsons patients with and without rapid‐eye‐movement sleep behavior disorder, respectively, 11 idiopathic rapid‐eye‐movement sleep behavior disorder patients, and 17 control subjects underwent 1 night of polysomnography. The heart rate response associated with arousal or leg movement from all sleep stages was analyzed from 10 heartbeats before the onset of the sleep event to 15 heartbeats following onset of the sleep event. The heart rate reponse to arousals was significantly lower in both parkinsonian groups compared with the control group and the idiopathic rapid‐eye‐movement sleep behavior disorder group. The heart rate response to leg movement was significantly lower in both Parkinsons groups and in the idiopathic rapid‐eye‐movement sleep behavior disorder group compared with the control group. The heart rate response for the idiopathic rapid‐eye‐movement sleep behavior disorder group was intermediate with respect to the control and the parkinsonian groups. The attenuated heart rate response may be a manifestation of the autonomic deficits experienced in Parkinsons disease. The idiopathic rapid‐eye‐movement sleep behavior disorder patients not only exhibited impaired motor symptoms but also incipient autonomic dysfunction, as revealed by the attenuated heart rate response.

REM Behaviour Disorder detection associated with neurodegenerative diseases

Abnormal skeleton muscle activity during REM sleep is characterized as REM Behaviour Disorder (RBD), and may be an early marker for different neurodegenerative diseases. Early detection of RBD is therefore highly important, and in this ongoing study a semi-automatic method for RBD detection is proposed by analyzing the motor activity during sleep. Method: A total number of twelve patients have been involved in this study, six normal controls and six patients diagnosed with Parkinsons Disease (PD) with RBD. All subjects underwent at least one ambulant polysomnographic (PSG) recording. The sleep recordings were scored, according to the new sleep-scoring standard from the American Academy of Sleep Medicine, by two independent sleep specialists. A follow-up analysis of the scoring consensus between the two specialists has been conducted. Based on the agreement of the two manual scorings, a computerized algorithm has been attempted implemented. By analysing the REM and non-REM EMG activity, using advanced signal processing tools combined with a statistical classifier, it is possible to discriminate normal and abnormal EMG activity. Due to the small number of patients, the overall performance of the algorithm was calculated using the leave-one-out approach and benchmarked against a previously published computerized/visual method. Results: Based on the available data and using optimal settings, it was possible to correctly classify PD subjects with RBD with 100% sensitivity, 100% specificity, which is an improvement compared to previous published studies. Conclusion: The overall result indicates the usefulness of a computerized scoring algorithm and may be a feasible way of reducing scoring time. Further enhancement on additional data, i.e. subjects with idiopathic RBD (iRBD) and PD without RBD, is needed to validate its robustness and the overall result.

Reduced sympathetic activity in idiopathic rapid-eye-movement sleep behavior disorder and Parkinson's disease

BACKGROUND More than 50% of patients with idiopathic REM sleep behavior disorder (iRBD) will develop Parkinsons disease or Lewy body dementia. In a previous study, we found attenuated heart rate responses in iRBD and Parkinsons disease patients during sleep. The current study aimed to evaluate heart rate variability further in order to identify possible changes in these components during wakefulness and sleep in patients with iRBD and Parkinsons disease. METHODS We evaluated heart rate variability in 5-minute electrocardiography segments from wakefulness, and non-REM and REM sleep in 11 iRBD patients and 23 Parkinsons disease patients, and compared these with 10 control subjects. RESULTS AND CONCLUSIONS Patients with iRBD had attenuated sympathetic nervous system activity compared with controls and this was more pronounced in patients with Parkinsons disease. The cardiac parasympathetic nervous system seems to be relatively well preserved in patients with iRBD and Parkinsons disease. The progressive reduction of sympathetic nervous activity is in line with the postganglionic sympathetic nervous dysfunction seen in early Parkinsons disease.

Automatic REM sleep detection associated with idiopathic rem sleep Behavior Disorder

Rapid eye movement sleep Behavior Disorder (RBD) is a strong early marker of later development of Parkinsonism. Currently there are no objective methods to identify and discriminate abnormal from normal motor activity during REM sleep. Therefore, a REM sleep detection without the use of chin electromyography (EMG) is useful. This is addressed by analyzing the classification performance when implementing two automatic REM sleep detectors. The first detector uses the electroencephalography (EEG), electrooculography (EOG) and EMG to detect REM sleep, while the second detector only uses the EEG and EOG. Method: Ten normal controls and ten age matched patients diagnosed with RBD were enrolled. All subjects underwent one polysomnographic (PSG) recording, which was manual scored according to the new sleep-scoring standard from the American Academy of Sleep Medicine. Based on the manual scoring, an automatic computerized REM detection algorithm has been implemented, using wavelet packet combined with artificial neural network. Results: When using the EEG, EOG and EMG modalities, it was possible to correctly classify REM sleep with an average Area Under Curve (AUC) equal to 0.90±0.03 for normal subjects and AUC = 0.81±0.05 for RBD subjects. The performance difference between the two groups was significant (p < 0.01). No significant drop (p > 0.05) in performance was observed when only using the EEG and EOG in neither of the groups. Conclusion: The overall result indicates that the EMG does not play an important role when classifying REM sleep.

Rapid eye movement sleep behavior disorder as an outlier detection problem.

Objective: Idiopathic rapid eye movement (REM) sleep behavior disorder is a strong early marker of Parkinson’s disease and is characterized by REM sleep without atonia and/or dream enactment. Because these measures are subject to individual interpretation, there is consequently need for quantitative methods to establish objective criteria. This study proposes a semiautomatic algorithm for the early detection of Parkinson’s disease. This is achieved by distinguishing between normal REM sleep and REM sleep without atonia by considering muscle activity as an outlier detection problem. Methods: Sixteen healthy control subjects, 16 subjects with idiopathic REM sleep behavior disorder, and 16 subjects with periodic limb movement disorder were enrolled. Different combinations of five surface electromyographic channels, including the EOG, were tested. A muscle activity score was automatically computed from manual scored REM sleep. This was accomplished by the use of subject-specific features combined with an outlier detector (one-class support vector machine classifier). Results: It was possible to correctly separate idiopathic REM sleep behavior disorder subjects from healthy control subjects and periodic limb movement subjects with an average validation area under the receiver operating characteristic curve of 0.993 when combining the anterior tibialis with submentalis. Additionally, it was possible to separate all subjects correctly when the final algorithm was tested on 12 unseen subjects. Conclusions: Detection of idiopathic REM sleep behavior disorder can be regarded as an outlier problem. Additionally, the EOG channels can be used to detect REM sleep without atonia and is discriminative better than the traditional submentalis. Furthermore, based on data and methodology, arousals and periodic limb movements did only have a minor influence on the quantification of the muscle activity. Analysis of muscle activity during nonrapid eye movement sleep may improve the separation even further.

Sleep-wake transition in narcolepsy and healthy controls using a support vector machine.

Abstract Narcolepsy is characterized by abnormal sleep–wake regulation, causing sleep episodes during the day and nocturnal sleep disruptions. The transitions between sleep and wakefulness can be identified by manual scorings of a polysomnographic recording. The aim of this study was to develop an automatic classifier capable of separating sleep epochs from epochs of wakefulness by using EEG measurements from one channel. Features from frequency bands &agr; (0–4 Hz), &bgr; (4–8 Hz), &dgr; (8–12 Hz), &thgr; (12–16 Hz), 16 to 24 Hz, 24 to 32 Hz, 32 to 40 Hz, and 40 to 48 Hz were extracted from data by use of a wavelet packet transformation and were given as input to a support vector machine classifier. The classification algorithm was assessed by hold-out validation and 10-fold cross-validation. The data used to validate the classifier were derived from polysomnographic recordings of 47 narcoleptic patients (33 with cataplexy and 14 without cataplexy) and 15 healthy controls. Compared with manual scorings, an accuracy of 90% was achieved in the hold-out validation, and the area under the receiver operating characteristic curve was 95%. Sensitivity and specificity were 90% and 88%, respectively. The 10-fold cross-validation procedure yielded an accuracy of 88%, an area under the receiver operating characteristic curve of 92%, a sensitivity of 87%, and a specificity of 87%. Narcolepsy with cataplexy patients experienced significantly more sleep–wake transitions during night than did narcolepsy without cataplexy patients (P = 0.0199) and healthy subjects (P = 0.0265). In addition, the sleep–wake transitions were elevated in hypocretin-deficient patients. It is concluded that the classifier shows high validity for identifying the sleep–wake transition. Narcolepsy with cataplexy patients have more sleep–wake transitions during night, suggesting instability in the sleep–wake regulatory system.

A computerized algorithm for arousal detection in healthy adults and patients with Parkinson disease.

Summary Arousals occur from all sleep stages and can be identified as abrupt electroencephalogram (EEG) and electromyogram (EMG) changes. Manual scoring of arousals is time consuming with low interscore agreement. The aim of this study was to design an arousal detection algorithm capable of detecting arousals from non–rapid eye movement (REM) and REM sleep, independent of the subjects age and disease. The proposed algorithm uses features from EEG, EMG, and the manual sleep stage scoring as input to a feed-forward artificial neural network (ANN). The performance of the algorithm has been assessed using polysomnographic (PSG) recordings from a total of 24 subjects. Eight of the subjects were diagnosed with Parkinson disease (PD) and the rest (16) were healthy adults in various ages. The performance of the algorithm was validated in 3 settings: testing on the 8 patients with PD using the leave-one-out method, testing on the 16 healthy adults using the leave-one-out method, and finally testing on all 24 subjects using a 4-fold crossvalidation. For these 3 validations, the sensitivities were 89.8%, 90.3%, and 89.4%, and the positive predictive values (PPVs) were 88.8%, 89.4%, and 86.1%. These results are high compared with those of previously presented arousal detection algorithms and especially compared with the high interscore variability of manual scorings.