Protocol of the SOMNIA project : an observational study to create a neurophysiological database for advanced clinical sleep monitoring

Introduction Polysomnography (PSG) is the primary tool for sleep monitoring and the diagnosis of sleep disorders. Recent advances in signal analysis make it possible to reveal more information from this rich data source. Furthermore, many innovative sleep monitoring techniques are being developed that are less obtrusive, easier to use over long time periods and in the home situation. Here, we describe the methods of the Sleep and Obstructive Sleep Apnoea Monitoring with Non-Invasive Applications (SOMNIA) project, yielding a database combining clinical PSG with advanced unobtrusive sleep monitoring modalities in a large cohort of patients with various sleep disorders. The SOMNIA database will facilitate the validation and assessment of the diagnostic value of the new techniques, as well as the development of additional indices and biomarkers derived from new and/or traditional sleep monitoring methods. Methods and analysis We aim to include at least 2100 subjects (both adults and children) with a variety of sleep disorders who undergo a PSG as part of standard clinical care in a dedicated sleep centre. Full-video PSG will be performed according to the standards of the American Academy of Sleep Medicine. Each recording will be supplemented with one or more new monitoring systems, including wrist-worn photoplethysmography and actigraphy, pressure sensing mattresses, multimicrophone recording of respiratory sounds including snoring, suprasternal pressure monitoring and multielectrode electromyography of the diaphragm

Beneficial use of immunoglobulins in the treatment of Sydenham chorea

This double case report indicates that treatment with intravenous immunoglobulins (IVIG) is effective in patients with Sydenham chorea (SC). SC is a rare but impressive clinical manifestation following streptococcal infection. This movement disorder characterised by chorea, emotional lability and muscle weakness, is one of the major criteria of acute rheumatic fever. Treatment of SC is typically limited to supportive care and palliative medications. Curative treatment is still in the experimental stage. Recent research on patients with SC proved that antibodies against the group A streptococcus cross-react with epitopes of neurons in the basal ganglia, namely, intracellular tubulin and extracellular lysoganglioside. Therefore, immune modulating therapy by means of prednisone, plasma exchange and IVIG are mentioned in the literature as possible effective treatment. Beneficial effect of IVIG has been shown in several diseases with molecular mimicry as the underlying pathophysiology. In this paper, we describe two girls aged 11 and 13 years, respectively, who presented with SC having severe disabilities in their daily live. We treated both patients with IVIG 400 mg/kg/day for 5 days. Treatment was tolerated well and had a pronounced positive effect. Shortly after the drug was administered, all signs and symptoms disappeared in both patients. Based upon these patients, we highlight IVIG as a serious treatment option for SC

Aleatoric Uncertainty Estimation of Overnight Sleep Statistics Through Posterior Sampling Using Conditional Normalizing Flows

In sleep staging, a polysomnography is visually scored by a human expert, who creates a hypnogram that classifies the measurement into a sequence of sleep stages, from which overnight sleep statistics, such as total sleep time, are derived. Because inter-scorer agreement between humans is limited, deep learning methods trained to automate sleep staging have aleatoric uncertainty about both hypnogram and overnight statistics. We would like to estimate this aleatoric uncertainty, which can be achieved by means of posterior sampling. Current approaches model the hypnogram through a time-based factorization of categorical distributions over sleep stages. This discards time-dependent information, invalidating posterior sampling of the overnight statistics. Instead of factorizing, we propose to jointly model the sequence of sleep stages, by introducing U-Flow, a conditional normalizing flow network. We compare U-Flow to factorized baselines, leveraging 921 recordings, and show that it achieves similar performance in terms of accuracy and Cohen’s kappa on the majority voted hypnograms, while outperforming in terms of uncertainty estimation of the overnight sleep statistics

Modeling the Impact of Inter-Rater Disagreement on Sleep Statistics using Deep Generative Learning

Sleep staging is the process by which an overnight polysomnographic measurement is segmented into epochs of 30 seconds, each of which is annotated as belonging to one of five discrete sleep stages. The resulting scoring is graphically depicted as a hypnogram, and several overnight sleep statistics are derived, such as total sleep time and sleep onset latency. Gold standard sleep staging as performed by human technicians is time-consuming, costly, and comes with imperfect inter-scorer agreement, which also results in inter-scorer disagreement about the overnight statistics. Deep learning algorithms have shown promise in automating sleep scoring, but struggle to model inter-scorer disagreement in sleep statistics. To that end, we introduce a novel technique using conditional generative models based on Normalizing Flows that permits the modeling of the inter-rater disagreement of overnight sleep statistics, termed U-Flow. We compare U-Flow to other automatic scoring methods on a hold-out test set of 70 subjects, each scored by six independent scorers. The proposed method achieves similar sleep staging performance in terms of accuracy and Cohen's kappa on the majority-voted hypnograms. At the same time, U-Flow outperforms the other methods in terms of modeling the inter-rater disagreement of overnight sleep statistics. The consequences of inter-rater disagreement about overnight sleep statistics may be great, and the disagreement potentially carries diagnostic and scientifically relevant information about sleep structure. U-Flow is able to model this disagreement efficiently and can support further investigations into the impact inter-rater disagreement has on sleep medicine and basic sleep research.</p

SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series

Continuous monitoring with an ever-increasing number of sensors has become ubiquitous across many application domains. Acquired data are typically high-dimensional and difficult to interpret, but they are also hypothesized to lie on a lower-dimensional manifold. Many deep learning (DL) models aim to identify this manifold, but do not promote structure nor interpretability. We propose the SOM-CPC model, which jointly optimizes Contrastive Predictive Coding (CPC), and a Self-Organizing Map (SOM) to find such an organized manifold. We address a largely unexplored and challenging set of scenarios comprising high-rate time series, and show on synthetic and real-life medical and audio data that SOM-CPC outperforms strong baseline models that combine DL with SOMs. SOM-CPC has great potential to expose latent patterns in high-rate data streams, and may therefore contribute to a better understanding of many different processes and systems

Certainty about Uncertainty in Sleep Staging: a Theoretical Framework

Sleep stage classification is an important tool for the diagnosis of sleep disorders. Because sleep staging has such a high impact on clinical outcome, it is important that it is done reliably. However, it is known that uncertainty exists in both expert scorers and automated models. On average, the agreement between human scorers is only 82.6%. In this study, we provide a theoretical framework to facilitate discussion and further analyses of uncertainty in sleep staging. To this end, we introduce two variants of uncertainty, known from statistics and the machine learning community: aleatoric and epistemic uncertainty. We discuss what these types of uncertainties are, why the distinction is useful, where they arise from in sleep staging, and provide recommendations on how this framework can improve sleep staging in the future

Interpretation and Further Development of the Hypnodensity Representation of Sleep Structure

Objective: The recently-introduced hypnodensity graph provides a probability distribution over sleep stages per data window (i.e. an epoch). This work explored whether this representation reveals continuities that can only be attributed to intraand inter-rater disagreement of expert scorings, or also to co-occurrence of sleep stagede-pendent features within one epoch. Approach: We proposed a simplified model for time series like the ones measured during sleep, and a second model to describe the annotation process by an expert. Generating data according to these models, enabled controlled experiments to investigate the interpretation of the hypnodensity graph. Moreover, the influence of both the supervised training strategy, and the used softmax non-linearity were investigated. Polysomnography recordings of 96 healthy sleepers (of which 11 were used as independent test set), were subsequently used to transfer conclusions to real data. Main results: A hypnodensity graph, predicted by a supervised neural classifier, represents the probability with which the sleep expert(s) assigned a label to an epoch. It thus reflects annotator behavior, and is thereby only indirectly linked to the ratio of sleep stage-dependent features in the epoch. Unsupervised training was shown to result in hypnodensity graph that were slightly less dependent on this annotation process, resulting in, on average, higher-entropy distributions over sleep stages (Hunsupervised = 0.41 vs Hsupervised = 0.29). Moreover, pre-softmax predictions were, for both training strategies, found to better reflect the ratio of sleep stage-dependent characteristics in an epoch, as compared to the post-softmax counterparts (i.e. the hypnodensity graph). In real data, this was observed from the linear relation between pre-softmax N3 predictions and the amount of delta power. Significance : This study provides insights in, and proposes new, representations of sleep that may enhance our comprehension about sleep and sleep disorders

Self-Organizing Maps for Contrastive Embeddings of Sleep Recordings

Nowadays, high amounts of data can be acquired in various applications, spurring the need for interpretable data representations that provide actionable insights. Algorithms that yield such representations ideally require as little a priori knowledge about the data or corresponding annotations as possible. To this end, we here investigate the use of Kohonen's Self-Organizing Map (SOM) in combination with data-driven low-dimensional embeddings obtained through self-supervised Contrastive Predictive Coding. We compare our approach to embeddings found with an auto-encoder and, moreover, investigate three ways to deal with node selection during SOM optimization. As a challenging experiment we analyze nocturnal sleep recordings of healthy subjects, and conclude that - for this noisy real-life data - contrastive learning yields a better low-dimensional embedding for the purpose of SOM training, compared to an auto-encoder. In addition, we show that a stochastic temperature-annealed SOM-training outperforms both a deterministic and a non-temperature-annealed stochastic approach. Clinical relevance - The hypnogram has for decades been the clinical standard in sleep medicine despite the fact that it is a highly simplified representation of a polysomnography recording. We propose a sensor-agnostic algorithm that is able to reveal more intricate patterns in sleep recordings which might teach us about sleep structure and sleep disorders

Protocol of the SOMNIA project: an observational study to create a neurophysiological database for advanced clinical sleep monitoring

INTRODUCTION: Polysomnography (PSG) is the primary tool for sleep monitoring and the diagnosis of sleep disorders. Recent advances in signal analysis make it possible to reveal more information from this rich data source. Furthermore, many innovative sleep monitoring techniques are being developed that are less obtrusive, easier to use over long time periods and in the home situation. Here, we describe the methods of the Sleep and Obstructive Sleep Apnoea Monitoring with Non-Invasive Applications (SOMNIA) project, yielding a database combining clinical PSG with advanced unobtrusive sleep monitoring modalities in a large cohort of patients with various sleep disorders. The SOMNIA database will facilitate the validation and assessment of the diagnostic value of the new techniques, as well as the development of additional indices and biomarkers derived from new and/or traditional sleep monitoring methods. METHODS AND ANALYSIS: We aim to include at least 2100 subjects (both adults and children) with a variety of sleep disorders who undergo a PSG as part of standard clinical care in a dedicated sleep centre. Full-video PSG will be performed according to the standards of the American Academy of Sleep Medicine. Each recording will be supplemented with one or more new monitoring systems, including wrist-worn photoplethysmography and actigraphy, pressure sensing mattresses, multimicrophone recording of respiratory sounds including snoring, suprasternal pressure monitoring and multielectrode electromyography of the diaphragm. ETHICS AND DISSEMINATION: The study was reviewed by the medical ethical committee of the Maxima Medical Center (Eindhoven, the Netherlands, File no: N16.074). All subjects provide informed consent before participation.The SOMNIA database is built to facilitate future research in sleep medicine. Data from the completed SOMNIA database will be made available for collaboration with researchers outside the institute

Genetic and pharmacological inhibition of galectin-3 prevents cardiac remodeling by interfering with myocardial fibrogenesis

BACKGROUND: Galectin-3 has been implicated in the development of organ fibrosis. It is unknown whether it is a relevant therapeutic target in cardiac remodeling and heart failure. METHODS AND RESULTS: Galectin-3 knock-out and wild-type mice were subjected to angiotensin II infusion (2.5 µg/kg for 14 days) or transverse aortic constriction for 28 days to provoke cardiac remodeling. The efficacy of the galectin-3 inhibitor N-acetyllactosamine was evaluated in TGR(mREN2)27 (REN2) rats and in wild-type mice with the aim of reversing established cardiac remodeling after transverse aortic constriction. In wild-type mice, angiotensin II and transverse aortic constriction perturbations caused left-ventricular (LV) hypertrophy, decreased fractional shortening, and increased LV end-diastolic pressure and fibrosis (P<0.05 versus control wild type). Galectin-3 knock-out mice also developed LV hypertrophy but without LV dysfunction and fibrosis (P=NS). In REN2 rats, pharmacological inhibition of galectin-3 attenuated LV dysfunction and fibrosis. To elucidate the beneficial effects of galectin-3 inhibition on myocardial fibrogenesis, cultured fibroblasts were treated with galectin-3 in the absence or presence of galectin-3 inhibitor. Inhibition of galectin-3 was associated with a downregulation in collagen production (collagen I and III), collagen processing, cleavage, cross-linking, and deposition. Similar results were observed in REN2 rats. Inhibition of galectin-3 also attenuated the progression of cardiac remodeling in a long-term transverse aortic constriction mouse model. CONCLUSIONS: Genetic disruption and pharmacological inhibition of galectin-3 attenuates cardiac fibrosis, LV dysfunction, and subsequent heart failure development. Drugs binding to galectin-3 may be potential therapeutic candidates for the prevention or reversal of heart failure with extensive fibrosis