Jacob Kempfner

Decreased sleep spindle density in patients with idiopathic REM sleep behavior disorder and patients with Parkinson's disease.

OBJECTIVE To determine whether sleep spindles (SS) are potentially a biomarker for Parkinsons disease (PD). METHODS Fifteen 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. RESULTS The 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. CONCLUSIONS The lower SS density suggests involvement in pre-thalamic fibers involved in SS generation. SS density is a potential early PD biomarker. SIGNIFICANCE It is likely that an automatic SS detector could be a supportive diagnostic tool in the evaluation of iRBD and PD patients.

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.

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.

Validation of a novel automatic sleep spindle detector with high performance during sleep in middle aged subjects

Many of the automatic sleep spindle detectors currently used to analyze sleep EEG are either validated on young subjects or not validated thoroughly. The purpose of this study is to develop and validate a fast and reliable sleep spindle detector with high performance in middle aged subjects. An automatic sleep spindle detector using a bandpass filtering approach and a time varying threshold was developed. The validation was done on sleep epochs from EEG recordings with manually scored sleep spindles from 13 healthy subjects with a mean age of 57.9 ± 9.7 years. The sleep spindle detector reached a mean sensitivity of 84.6% and a mean specificity of 95.3%. The sleep spindle detector can be used to obtain measures of spindle count and density together with quantitative measures such as the mean spindle frequency, mean spindle amplitude, and mean spindle duration.

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.

Automatic SLEEP staging: From young aduslts to elderly patients using multi-class support vector machine

Aging is a process that is inevitable, and makes our body vulnerable to age-related diseases. Age is the most consistent factor affecting the sleep structure. Therefore, new automatic sleep staging methods, to be used in both of young and elderly patients, are needed. This study proposes an automatic sleep stage detector, which can separate wakefulness, rapid-eye-movement (REM) sleep and non-REM (NREM) sleep using only EEG and EOG. Most sleep events, which define the sleep stages, are reduced with age. This is addressed by focusing on the amplitude of the clinical EEG bands, and not the affected sleep events. The age-related influences are then reduced by robust subject-specific scaling. The classification of the three sleep stages are achieved by a multi-class support vector machine using the one-versus-rest scheme. It was possible to obtain a high classification accuracy of 0.91. Validation of the sleep stage detector in other sleep disorders, such as apnea and narcolepsy, should be considered in future work.

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.

Classification of iRBD and Parkinson's disease patients based on eye movements during sleep

Patients suffering from the sleep disorder idiopathic rapid-eye-movement sleep behavior disorder (iRBD) have been observed to be in high risk of developing Parkinsons disease (PD). This makes it essential to analyze them in the search for PD biomarkers. This study aims at classifying patients suffering from iRBD or PD based on features reflecting eye movements (EMs) during sleep. A Latent Dirichlet Allocation (LDA) topic model was developed based on features extracted from two electrooculographic (EOG) signals measured as parts in full night polysomnographic (PSG) recordings from ten control subjects. The trained model was tested on ten other control subjects, ten iRBD patients and ten PD patients, obtaining a EM topic mixture diagram for each subject in the test dataset. Three features were extracted from the topic mixture diagrams, reflecting “certainty”, “fragmentation” and “stability” in the timely distribution of the EM topics. Using a Naive Bayes (NB) classifier and the features “certainty” and “stability” yielded the best classification result and the subjects were classified with a sensitivity of 95 %, a specificity of 80% and an accuracy of 90 %. This study demonstrates in a data-driven approach, that iRBD and PD patients may exhibit abnorm form and/or timely distribution of EMs during sleep.