13 research outputs found
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 13Â years, respectively, who presented with SC having severe disabilities in their daily live. We treated both patients with IVIG 400Â mg/kg/day for 5Â 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 :Â 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
Plasma matrix metalloproteinase-9 and ACE-inhibitor-induced improvement of urinary albumin excretion in non-diabetic, microalbuminuric subjects
Introduction. Elevated plasma matrix metalloproteinase-9 (MMP-9) levels have been suggested to precede the development of microalbuminuria. As angiotensin-converting enzyme (ACE) inhibitors effectively reduce urinary albumin excretion (UAE), in the present study we have investigated the potential association of plasma MMP-9 levels with UAE and treatment effects of ACE-inhibition. Material and methods. In a placebo-controlled randomised trial we determined plasma MMP-9 levels at baseline and after three months of randomisation to either placebo (n=202) or fosinopril (20 mg/day, n=204) treatment. Results. Baseline plasma MMP-9 levels were not related to baseline UAE (r=-0.008, p=0.871). Three months of fosinopril treatment effectively reduced UAE compared to placebo treatment (-10.4 +/- 2.4 vs. 1.8 +/- 1.3 mg/24 hours, p Conclusion. The effective reduction of UAE with fosinopril was not related to plasma MMP-9 levels