An efficient length- and rate-preserving concatenation of polar and repetition codes

We improve the method in \cite{Seidl:10} for increasing the finite-lengh performance of polar codes by protecting specific, less reliable symbols with simple outer repetition codes. Decoding of the scheme integrates easily in the known successive decoding algorithms for polar codes. Overall rate and block length remain unchanged, the decoding complexity is at most doubled. A comparison to related methods for performance improvement of polar codes is drawn.Comment: to be presented at International Zurich Seminar (IZS) 201

Local transmission of the eye worm Thelazia callipaeda in southern Germany

This report describes the first assumed locally transmitted case of the eye worm Thelazia callipaeda in a dog living in southern Germany. A 4-year-old male golden retriever from the town of Bühl in north eastern Baden-Württemberg, about 10km from the German-French border, showed one sided lacrimation for over 2weeks. Despite the application of antibiotics, there was no improvement, and the dog additionally showed blepharospasmus, epiphora and red conjunctivas. A deepened eye inspection revealed five whitish filiform parasites that were morphologically identified as T. callipaeda. The partial sequence of the mitochondrial cytochrome c oxidase subunit 1 gene (cox1, 605bp) from one specimen revealed a novel haplotype, which differs by 1.3% from the only one (haplotype 1) identified in Europe so far. Since the infected dog had never been abroad with the exception of two daytrips to the close Alsace region in France, the transmission of T. callipaeda most probably was domestic. With the presence of end hosts and Phortica flies nourishing on lachrymal secretions acting as intermediate hosts and an increasing number of dogs travelling to and coming from endemic regions in the South, the establishment of T. callipaeda in large parts of Europe cannot be exclude

Multiple Sleep Latency Test and Polysomnography in Patients with Central Disorders of Hypersomnolence

A multiple sleep latency test (MSLT) with occurrence of sleep onset REM periods (SOREMP) is considered one of the central diagnostic criteria for narcolepsy according to the International Classification of Sleep Disorders, but its sensitivity and specificity have been questioned. This study aims to describe MSLT and polysomnography (PSG) findings, including frequency and distribution of SOREMP during the day, in a large cohort of patients with central disorders of hypersomnolence (CDH). We retrospectively analyzed electrophysiological data from MSLT and PSG in 370 consecutive patients with narcolepsy type 1 (NT1, n = 97), type 2 (NT2, n = 31), idiopathic hypersomnia (IH, n = 48), nonorganic hypersomnia (NOH, n = 116) and insufficient sleep syndrome (ISS, n = 78). NT1 and NT2 patients had a significantly shorter mean Sleep Latency (mSL) and REM-Latency (REML) in MSLT and PSG. SOREMP occurred more frequently in narcoleptic vs. non-narcoleptic patients in MSLT and PSG. Occurrence of 3 or more SOREMP in MSLT and a SOREMP in PSG had a very high specificity and positive predictive value (98%/96% and 100% respectively), however relatively low sensitivity (65% and 45% respectively). NT1 more than NT2 patients have shorter mSL and more frequent SOREMP in MSLT and shorter SL as well as REML during nocturnal PSG. Increasing numbers of SOREMP in MSLT and especially SOREMP during PSG increase specificity on the expense of sensitivity in diagnosing narcolepsy. Therefore, frequency of SOREMP in MSLT naps and PSG can help to discriminate but not clearly separate narcoleptic from non-narcoleptic patients

Quality-dependent Deep Learning for Safe Autonomous Guidewire Navigation

Cardiovascular diseases are the main cause ofdeath worldwide. State-of-the-art treatment often includes theprocess of navigating endovascular instruments through thevasculature. Automation of the procedure receives much at-tention lately and may increase treatment quality and unburdenclinicians. However, in order to ensure the patient’s safety theendovascular device needs to be steered carefully through thebody. In this work, we present a collection of medical criteriathat are considered by physicians during an intervention andthat can be evaluated automatically enabling a highly objectiveassessment. Additionally, we trained an autonomous controllerwith deep reinforcement learning to gently navigate within a2D simulation of an aortic arch. Among others, the controllerreduced the maximum and mean contact force applied to thevessel walls by 43% and 29%, respectively

Assessing periodicity of periodic leg movements during sleep

Periodic leg movements (PLM) during sleep consist of involuntary periodic movements of the lower extremities. The debated functional relevance of PLM during sleep is based on correlation of clinical parameters with the PLM index (PLMI). However, periodicity in movements may not be reflected best by the PLMI. Here, an approach novel to the field of sleep research is used to reveal intrinsic periodicity in inter movement intervals (IMI) in patients with PLM

Automatic detection of microsleep episodes with deep learning

Brief fragments of sleep shorter than 15 s are defined as microsleep episodes (MSEs), often subjectively perceived as sleepiness. Their main characteristic is a slowing in frequency in the electroencephalogram (EEG), similar to stage N1 sleep according to standard criteria. The maintenance of wakefulness test (MWT) is often used in a clinical setting to assess vigilance. Scoring of the MWT in most sleep-wake centers is limited to classical definition of sleep (30-s epochs), and MSEs are mostly not considered in the absence of established scoring criteria defining MSEs but also because of the laborious work. We aimed for automatic detection of MSEs with machine learning, i.e. with deep learning based on raw EEG and EOG data as input. We analyzed MWT data of 76 patients. Experts visually scored wakefulness, and according to recently developed scoring criteria MSEs, microsleep episode candidates (MSEc), and episodes of drowsiness (ED). We implemented segmentation algorithms based on convolutional neural networks (CNNs) and a combination of a CNN with a long-short term memory (LSTM) network. A LSTM network is a type of a recurrent neural network which has a memory for past events and takes them into account. Data of 53 patients were used for training of the classifiers, 12 for validation and 11 for testing. Our algorithms showed a good performance close to human experts. The detection was very good for wakefulness and MSEs and poor for MSEc and ED, similar to the low inter-expert reliability for these borderline segments. We provide a proof of principle that it is feasible to reliably detect MSEs with deep neuronal networks based on raw EEG and EOG data with a performance close to that of human experts. Code of algorithms ( https://github.com/alexander-malafeev/microsleep-detection ) and data ( https://zenodo.org/record/3251716 ) are available

Learning-based autonomous vascular guidewire navigation without human demonstration in the venous system of a porcine liver

Purpose The navigation of endovascular guidewires is a dexterous task where physicians and patients can benefit from automation. Machine learning-based controllers are promising to help master this task. However, human-generated training data are scarce and resource-intensive to generate. We investigate if a neural network-based controller trained without human-generated data can learn human-like behaviors. Methods We trained and evaluated a neural network-based controller via deep reinforcement learning in a finite element simulation to navigate the venous system of a porcine liver without human-generated data. The behavior is compared to manual expert navigation, and real-world transferability is evaluated. Results The controller achieves a success rate of 100% in simulation. The controller applies a wiggling behavior, where the guidewire tip is continuously rotated alternately clockwise and counterclockwise like the human expert applies. In the ex vivo porcine liver, the success rate drops to 30%, because either the wrong branch is probed, or the guidewire becomes entangled. Conclusion In this work, we prove that a learning-based controller is capable of learning human-like guidewire navigation behavior without human-generated data, therefore, mitigating the requirement to produce resource-intensive human-generated training data. Limitations are the restriction to one vessel geometry, the neglected safeness of navigation, and the reduced transferability to the real world