Helge Bjarup Dissing Sørensen

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

STUDY OBJECTIVEnSeveral 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.nnnDESIGNnWe 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.nnnSETTINGnSleep laboratory studies conducted from 2001-2011.nnnPARTICIPANTSnIn 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.nnnINTERVENTIONSnNone.nnnMEASUREMENTS AND RESULTSnAll 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.nnnCONCLUSIONSnOur 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.

Automatic sleep classification using a data-driven topic model reveals latent sleep states

BACKGROUNDnThe golden standard for sleep classification uses manual scoring of polysomnography despite points of criticism such as oversimplification, low inter-rater reliability and the standard being designed on young and healthy subjects.nnnNEW METHODnTo meet the criticism and reveal the latent sleep states, this study developed a general and automatic sleep classifier using a data-driven approach. Spectral EEG and EOG measures and eye correlation in 1s windows were calculated and each sleep epoch was expressed as a mixture of probabilities of latent sleep states by using the topic model Latent Dirichlet Allocation. Model application was tested on control subjects and patients with periodic leg movements (PLM) representing a non-neurodegenerative group, and patients with idiopathic REM sleep behavior disorder (iRBD) and Parkinsons Disease (PD) representing a neurodegenerative group. The model was optimized using 50 subjects and validated on 76 subjects.nnnRESULTSnThe optimized sleep model used six topics, and the topic probabilities changed smoothly during transitions. According to the manual scorings, the model scored an overall subject-specific accuracy of 68.3 ± 7.44 (% μ ± σ) and group specific accuracies of 69.0 ± 4.62 (control), 70.1 ± 5.10 (PLM), 67.2 ± 8.30 (iRBD) and 67.7 ± 9.07 (PD).nnnCOMPARISON WITH EXISTING METHODnStatistics of the latent sleep state content showed accordances to the sleep stages defined in the golden standard. However, this study indicates that sleep contains six diverse latent sleep states and that state transitions are continuous processes.nnnCONCLUSIONSnThe model is generally applicable and may contribute to the research in neurodegenerative diseases and sleep disorders.

Automatic sleep scoring in normals and in individuals with neurodegenerative disorders according to new international sleep scoring criteria.

The aim of this study was to develop a fully automatic sleep scoring algorithm on the basis of a reproduction of new international sleep scoring criteria from the American Academy of Sleep Medicine. A biomedical signal processing algorithm was developed, allowing for automatic sleep depth quantification of routine polysomnographic recordings through feature extraction, supervised probabilistic Bayesian classification, and heuristic rule-based smoothing. The performance of the algorithm was tested using 28 manually classified day-night polysomnograms from 18 normal subjects and 10 patients with Parkinson disease or multiple system atrophy. This led to quantification of automatic versus manual epoch-by-epoch agreement rates for both normals and abnormals. Resulting average agreement rates were 87.7% (Cohen’s Kappa: 0.79) and 68.2% (Cohen’s Kappa: 0.26) in the normal and abnormal group, respectively. Based on an observed reliability of the manual scorer of 92.5% (Cohen’s Kappa: 0.87) in the normal group and 85.3% (Cohen’s Kappa: 0.73) in the abnormal group, this study concluded that although the developed algorithm was capable of scoring normal sleep with an accuracy around the manual interscorer reliability, it failed in accurately scoring abnormal sleep as encountered for the Parkinson disease/multiple system atrophy patients.

Data-driven modeling of sleep EEG and EOG reveals characteristics indicative of pre-Parkinson's and Parkinson's disease.

BACKGROUNDnManual scoring of sleep relies on identifying certain characteristics in polysomnograph (PSG) signals. However, these characteristics are disrupted in patients with neurodegenerative diseases.nnnNEW METHODnThis study evaluates sleep using a topic modeling and unsupervised learning approach to identify sleep topics directly from electroencephalography (EEG) and electrooculography (EOG). PSG data from control subjects were used to develop an EOG and an EEG topic model. The models were applied to PSG data from 23 control subjects, 25 patients with periodic leg movements (PLMs), 31 patients with idiopathic REM sleep behavior disorder (iRBD) and 36 patients with Parkinsons disease (PD). The data were divided into training and validation datasets and features reflecting EEG and EOG characteristics based on topics were computed. The most discriminative feature subset for separating iRBD/PD and PLM/controls was estimated using a Lasso-regularized regression model.nnnRESULTSnThe features with highest discriminability were the number and stability of EEG topics linked to REM and N3, respectively. Validation of the model indicated a sensitivity of 91.4% and a specificity of 68.8% when classifying iRBD/PD patients.nnnCOMPARISON WITH EXISTING METHODnThe topics showed visual accordance with the manually scored sleep stages, and the features revealed sleep characteristics containing information indicative of neurodegeneration.nnnCONCLUSIONSnThis study suggests that the amount of N3 and the ability to maintain NREM and REM sleep have potential as early PD biomarkers. Data-driven analysis of sleep may contribute to the evaluation of neurodegenerative patients.

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.

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.

Detection of arousals in Parkinson's disease patients

Arousal from sleep are short awakenings, which can be identified in the EEG as an abrupt change in frequency. Arousals can occur in all sleep stages and the number and frequency increase with age. Frequent arousals during sleep results in sleep fragmentation and is associated with daytime sleepiness. Manual scoring of arousals is time-consuming and the inter-score agreement is highly varying especially for patients with sleep related disorders. The aim of this study was to design an arousal detection algorithm capable of detecting arousals from sleep, in both non-REM and REM sleep in patients suffering from Parkinsons disease (PD). 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 8 patients diagnosed with PD. The performance of the algorithm was validated using the leave-one-out method resulting in a sensitivity of 89.8 % and a positive predictive value (PPV) of 88.8 %. This result is high compared to previous presented arousal detection algorithms.

Technological aided assessment of the acutely ill patient – The case of postoperative complications

Surgical interventions come with complications and highly reported mortality after major surgery. The mortality may be a result of delayed detection of severe complications due to lower monitoring frequency in the general wards. Several studies have shown that continuous monitoring is superior to the manually intermittent recorded monitoring in terms of detecting abnormal physiological signs. Hopefully improved observations may result in earlier detection and clinical intervention. This narrative review will describe current monitoring possibilities for postoperative patients and how it may prevent complications. Several wireless systems are being developed for monitoring vital parameters, but many of these are not yet validated for critically ill patients. The ultimate goal with patient monitoring and detect of events is to prevent postoperative complications, death and costs in the health care system. A few studies indicate that monitoring systems detect deteriorating patients earlier than the nurses, and this was associated with less clinical instability. An important caveat of future devices is to assess their effect in relevant patient populations and not only in healthy test-subjects. Implementation of novel technologies is expensive although expected to be cost-effective if just few adverse events can be prevented. The future is here with promising devices and the possibility to give an unprecedented precise risk estimation of adverse post-surgical events. Next step is to integrate existing evidence based treatment algorithms to demonstrate the clinical efficacy of implementing the new technology.