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Automatic electrocardiogram (ECG) analysis has been one of the very early use cases for computer assisted diagnosis (CAD). Most ECG devices provide some level of automatic ECG analysis. In the recent years, Deep Learning (DL) is increasingly used for this task, with the first models that claim to perform better than human physicians. In this manuscript, a pilot study is conducted to evaluate the added value of such a DL model to existing built-in analysis with respect to clinical relevance. 29 12-lead ECGs have been analyzed with a published DL model and results are compared to build-in analysis and clinical diagnosis. We could not reproduce the results of the test data exactly, presumably due to a different runtime environment. However, the errors were in the order of rounding errors and did not affect the final classification. The excellent performance in detection of left bundle branch block and atrial fibrillation that was reported in the publication could be reproduced. The DL method and the built-in method performed similarly good for the chosen cases regarding clinical relevance. While benefit of the DL method for research can be attested and usage in training can be envisioned, evaluation of added value in clinical practice would require a more comprehensive study with further and more complex cases.

Automatic electrocardiogram (ECG) analysis has been one of the very early use cases for computer assisted diagnosis (CAD). Most ECG devices provide some level of automatic ECG analysis. In the recent years, Deep Learning (DL) is increasingly used for this task, with the first models that claim to perform better than human physicians. In this manuscript, a pilot study is conducted to evaluate the added value of such a DL model to existing built-in analysis with respect to clinical relevance. 29 12-lead ECGs have been analyzed with a published DL model and results are compared to build-in analysis and clinical diagnosis. We could not reproduce the results of the test data exactly, presumably due to a different runtime environment. However, the errors were in the order of rounding errors and did not affect the final classification. The excellent performance in detection of left bundle branch block and atrial fibrillation that was reported in the publication could be reproduced. The DL method and the built-in method performed similarly good for the chosen cases regarding clinical relevance. While benefit of the DL method for research can be attested and usage in training can be envisioned, evaluation of added value in clinical practice would require a more comprehensive study with further and more complex cases.

The global COVID-19 pandemic revealed the necessity for mobile and web-based solutions for a variety of medical processes, e.g., individual risk calculation, communication of health information and contact tracing. Many such solutions are provided in form of open source software. However, there are major obstacles to the sustainable long-term continuation of such projects. As the topic of sustainability strategies is complex, a classification would be useful to help new projects to identify relevant sustainability factors. Based on a literature review a classification for long-term success of open source software was created. This paper presents a classification focusing on five unique categories: (1) structural decision, (2) revenue generation, (3) user focus, (4) openness and (5) community building. It was developed within the NUM-COMPASS project, focusing content-wise on pandemic apps and structure-wise on open-source provision. We provide some insights into the community building dimension by discussing factors that go into building sustainable communities.

The COVID-19 pandemic has brought along a massive increase in app development. However, most of these apps are not using interoperable data. The COMPASS project of the German COVID-19 Research Network of University Medicine ("Netzwerk Universitätsmedizin (NUM)") tackles this issue, by offering open-source technology, best practice catalogues, and suggestions for designing interoperable pandemic health applications (https://www.netzwerk-universitaetsmedizin.de/projekte/compass). Therefore, COMPASS conceived a framework that includes automated conformity checks as well as reference implementations for more efficient and pandemic-tailored app developments. It further aims to motivate and support developers to use interoperable standards.

The COVID-19 pandemic has brought along a massive increase in app development. However, most of these apps are not using interoperable data. The COMPASS project of the German COVID-19 Research Network of University Medicine (“Netzwerk Universitätsmedizin (NUM)”) tackles this issue, by offering open-source technology, best practice catalogues, and suggestions for designing interoperable pandemic health applications (https://www.netzwerk-universitaetsmedizin.de/projekte/compass). Therefore, COMPASS conceived a framework that includes automated conformity checks as well as reference implementations for more efficient and pandemic-tailored app developments. It further aims to motivate and support developers to use interoperable standards.

Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July 2020) in eight languages and attracted 7290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures., publishedVersion

For cross-sectoral sharing of health data, cloud services provide benefits regarding the availability of relevant information in critical situations. Nevertheless, storing electronic health records in the cloud may impact patient's privacy, since the cloud service provider might lack trustworthiness. Client-side cryptography mitigates the privacy risk by encrypting data in the user's computational environment before transmitting them to the cloud. However, client-side cryptography raises performance concerns. This paper investigates Web Assembly and JavaScript solutions that enable client-side cryptography in web applications and compares their performance against server-side cryptography. We contextualize the study within two healthcare web applications: a prototype for patient record sharing during acute stroke care and an application for sharing data in sleep medicine treatment. Our results show that JavaScript and Web Assembly libraries add more time to encryption and decryption than server-side cryptography. However, due to the concurrency introduced by simultaneous users, the performance of server-side cryptography is worse than client-side, taking longer times to attend to the demand and requiring a larger infrastructure to be effective. Moreover, the JavaScript library asmCrypto and Web Assembly approaches perform better than other libraries and are feasible candidates for application development.

The Multiple Sclerosis Data Alliance (MSDA), a global multi-stakeholder collaboration, is working to accelerate research insights for innovative care and treatment for people with multiple sclerosis (MS) through better use of real-world data (RWD). Despite the increasing reliance on RWD, challenges and limitations complicate the generation, collection, and use of these data. MSDA aims to tackle sociological and technical challenges arising with scaling up RWD, specifically focused on MS data. MSDA envisions a patient-centred data ecosystem in which all stakeholders contribute and use big data to co-create the innovations needed to advance timely treatment and care of people with MS. We would like to thank the sponsors of the MS Data Alliance. The MSDA receives income from a range of corporate sponsors, recently including: Biogen, BristolMyersSquibb (formerly Celgene), Canopy Growth Corporation, Genzyme, Icometrix, Janssen, Merck, Mylan, Novartis, QMENTA, Quanterix, Roche. We would like to thank the European MS Platform and the University MS Center of Hasselt University for founding the initiative. Next to this, we would like to thank the other core partners for their continued support: the European Charcot Foundation, The European Institute for Innovation through Health Data, the University of Gottingen and KULeuven. A special thanks to our pioneer registries: the UK MS registry and the German MS registry. Peeters, LM (corresponding author), Agoralaan C Bis, B-3590 Diepenbeek, Belgium. Liesbet.peeters@uhasselt.be

The increasingly digitized healthcare system requires new skills from all those involved. In order to impart these competencies, appropriate courses must be developed at educational institutions. In view of the rapid development of new aspects of digitization, this presents a challenge; suitable teaching formats must be developed successively. The establishment of cross-location teaching networks is one way to better meet training needs and to make the necessary spectrum of educational content available. As part of the Medical Informatics Initiative, the HiGHmed consortium is establishing such a teaching network, in the field of medical informatics, which covers many topics related to the digitization of the health care system. Various problem areas in the German education system were identified that hinder the development of the teaching network. These problem areas were prioritized firstly according to the urgency of the solution from the point of view of the HiGHmed consortium and secondly according to existing competencies in the participating societies. A workshop on the four most relevant topics was organized with experts from the German Society for Medical Informatics, Biometry and Epidemiology (GMDS), the Society for Medical Education (GMA) and the HiGHmed consortium. These are: recognition of exam results from teaching modules that are offered digitally and across locations, and their integration into existing curricula; recognition of digital, cross-location teaching in the teachers' teaching load; nationwide uniform competencies for teachers, in order to be able to conduct digital teaching effectively and with comparable quality; technical infrastructure to efficiently and securely communicate and manage the recognition of exam results between educational institutions. For all subject areas, existing preliminary work was identified on the basis of working questions, and short- and long-term needs for action were formulated. Finally, a need for the redesign of a technologically supported syntactic and semantic interoperability of learning performance recording was identified., Das zunehmend digitalisierte Gesundheitswesen erfordert neue Kompetenzen von allen Beteiligten. Um diese Kompetenzen vermitteln zu können, müssen an den Bildungsinstitutionen entsprechende Lehrveranstaltungen entwickelt werden. Dies ist angesichts der rasanten Entwicklung neuer Digitalisierungsaspekte eine Herausforderung; passende Lehrformate müssen sukzessive aufgebaut werden. Die Etablierung standortübergreifender Lehrverbünde ist eine Möglichkeit, den Ausbildungsbedarf besser zu decken und die notwendige Bandbreite an Bildungsinhalten verfügbar zu machen. Im Rahmen der Medizininformatik-Initiative wird im HiGHmed-Konsortium ein solcher Lehrverbund im Fachgebiet der Medizinischen Informatik aufgebaut, in welches viele Themen der Digitalisierung des Gesundheitswesens fallen. Dabei wurden verschiedene Problemfelder im deutschen Bildungswesen identifiziert, welche den Aufbau des Lehrverbundes hemmen. Diese Problemfelder wurden unter Beachtung der Dringlichkeit der Lösung aus Sicht des HiGHmeducation Konsortiums und andererseits nach vorhandenen Kompetenzen in den teilnehmenden Fachgesellschaften priorisiert. Zu den vier relevantesten Themen wurde ein Workshop mit Fachleuten der deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (GMDS), der Gesellschaft für Medizinische Ausbildung (GMA) und des HiGHmed Konsortiums ausgerichtet. Diese sind: Anerkennung von Prüfungsleistungen aus Lehrmodulen, welche digital und standortübergreifend angeboten werden, und Integration in bestehende Curricula; Anerkennung digitaler, standortübergreifender Lehre im Deputat der Lehrenden; bundesweit einheitliche Kompetenzen für Lehrende, um digitale Lehre effektiv und mit vergleichbarer Qualität betreiben zu können; technische Infrastrukturen, um die Anerkennung zwischen Bildungseinrichtungen effizient und sicher kommunizieren und verwalten zu können. Für alle Themengebiete wurden anhand von Arbeitsfragen existierende Vorarbeiten identifiziert sowie kurz- und langfristige Handlungsbedarfe formuliert. Abschließend wurde themenübergreifend ein Bedarf für die Neugestaltung einer technologisch unterstützten syntaktischen und semantischen Interoperabilität der Erfassung von Lernleistungen festgestellt., GMS Journal for Medical Education; 37(6):Doc56

A standard format for neurophysiology data is urgently needed to improve clinical care and promote research data exchange. Previous neurophysiology format standardization projects have provided valuable insights into how to accomplish the project. In medical imaging, the Digital Imaging and Communication in Medicine (DICOM) standard is widely adopted. DICOM offers a unique environment to accomplish neurophysiology format standardization because neurophysiology data can be easily integrated with existing DICOM-supported elements such as video, ECG, and images and also because it provides easy integration into hospital Picture Archiving and Communication Systems (PACS) long-term storage systems. Through the support of the International Federation of Clinical Neurophysiology (IFCN) and partners in industry, DICOM Working Group 32 (WG-32) has created an initial set of standards for routine electroencephalography (EEG), polysomnography (PSG), electromyography (EMG), and electrooculography (EOG). Longer and more complex neurophysiology data types such as high-definition EEG, long-term monitoring EEG, intracranial EEG, magnetoencephalography, advanced EMG, and evoked potentials will be added later. In order to provide for efficient data compression, a DICOM neurophysiology codec design competition will be held by the IFCN and this is currently being planned. We look forward to a future when a common DICOM neurophysiology data format makes data sharing and storage much simpler and more efficient.

Die Digitalisierung durchzieht inzwischen weite Teile der Gesellschaft. Auch der Gesundheitssektor befindet sich zurzeit im digitalen Wandel. Dabei steht die Gesundheitsbranche, die in Deutschland zum grosen Teil aus kleinen und mittelstandischen Unternehmen besteht, vor vollig neuen Herausforderungen. War die Entwicklung bisher aufgrund der aufwendigen und kostenintensiven Zertifizierungen eher durch inkrementelle Innovation gepragt, sind heute sowohl technisch als auch politisch Losungen moglich, die noch vor wenigen Jahren kaum Marktchancen gehabt hatten. Auch die digitale Start-up-Szene ist im Gesundheitsbereich sehr aktiv. Die notwendigen Expertisen, um sich diesen Herausforderungen zu stellen und mit innovativen Losungen wettbewerbsfahig zu bleiben, sind dabei so vielfaltig, dass sie in kleineren Unternehmen oft nur unvollstandig zur Verfugung stehen. Hier setzt der Innovation Hub Digital Health (IHDH) an, der Unternehmen der Gesundheitsbranche einen einfachen Zugang der an der Fachhochschule existierenden Expertisen und Ressourcen bieten soll. In diesem Betrag werden das Konzept sowie die praktischen Erfahrungen nach zweieinhalb Jahren Laufzeit vorgestellt und kritisch diskutiert.

Die automatische Analyse von Polysomnographien (PSG) ist eine wichtige Methode in der schlafmedizinischen Forschung. Aufgrund der Datenvolumina, der unterschiedlichen Datenformate und Signalqualitat stellt jedoch insbesondere die Analyse von Daten von unterschiedlichen Gerateherstellern und verschiedenen Schlaflaboren eine besondere Herausforderung dar. Die grose Menge an heterogenen Daten muss datenschutzkonform in angemessener Zeit analysiert und Ergebnisse mussen beteiligten Forschern wieder verfugbar gemacht werden. Da insbesondere unbemerkte Qualitatsmangel der Signale und Artefakte das Analyseergebnis verfalschen konnen, ist die Uberprufung der Eignung einer PSG fur das jeweilige Analyseverfahren von besonderer Wichtigkeit. Dieser Artikel untersucht neun Verfahren zur automatischen Detektierung von Aufnahmeartefakten in Polysomnographien, welche in eine cloud-basierte Analyseplattform integriert sind. Anhand von 269 annotierten PSG-Datensatzen werden die Detektionsraten und Ergebnisse evaluiert. Die entstandene Analyseplattform erreicht mittels einer automatischen Formatkonvertierung von PSG-Aufnahmen und anschliesender Artefakterkennung eine Detektionsrate von 80 % der zuvor manuell markierten Artefakte. Die zu verarbeitenden Daten werden in einem Cloud-System vollautomatisch verteilt und Ergebnisse in einem Datenmanagementsystem (DMS) gesichert. Die umgesetzte Analyseplattform dient der Unterstutzung kollaborativer schlafmedizinischer Forschung durch die Integration eines projektbasierten DMS, mittels dessen Datensatze erfasst und ausgetauscht werden konnen. Die Integration von Algorithmen zur Vereinheitlichung des Eingabedatenformats und der automatischen Artefakterkennung kann Ergebnisse nachgelagerter Analysen verbessern.

Sleep is a fundamental biological process crucial for survival and health of (not only) humans. But many circumstances like physiological and mental disorders, environment and lifestyle may affect healthy sleep. To date, 88 different sleep disorders are internationally recognized. They cover a broad field of medical areas. Analysis of human sleep is typically based on multidimensional biosignal recordings, so called polysomnographies (PSG). Therefore research often includes digital signal processing. Clinical sleep research is an inherent multidisciplinary field. Inter-institutional and interdisciplinary collaborations are required to address the complexity of sleep regulation and disturbance. But to date, collaborative sleep research is poorly supported by IT systems. In particular, the management and processing of PSGs is challenging. A large variety of PSG devices, data formats, measurement procedures and quality variations impedes consistent biosignal data processing. In this manuscript we introduce a virtual research platform supporting inter-institutional data sharing and processing. The infrastructure is based on XNAT—a free and open source neuroimaging research platform, a loosely coupled service oriented architecture and scalable virtualization in the back end. The system is capable of local pseudonymization of biosignal data, mapping to a standardized set of parameters and automatic quality assessment. Terms and quality measures are derived from the “Manual for the Scoring of Sleep and Associated Events” of the American Academy of Sleep Medicine (AASM), the de facto standard for diagnostic biosignal analysis in sleep medicine.

The use of modern medical equipment in crisis and war zones for emergency medical teams (EMT) of the World Health Organization is an important factor for fast and efficient humanitarian aid. A reliable vital parameter monitoring is fundamental in mobile hospitals. Currently, the maintenance of medical devices in structurally weak areas is difficult due to the company’s proprietary standards. Rough environmental influences such as dust, moisture, heat or shocks can lead to dysfunktion and long-lasting failure of instrumentation. Pulse oximetry and blood pressure measurements are particularly susceptible. We developed an open source vital parameter monitoring system for use under adverse conditions and structurally weak areas. Blood oxygen levels, heart rate, blood pressure and electrocardiograms are recorded and transferred to decentralized displays. The main focus is on reliability and robustness of various optical sensors for pulse oximetry, the repair capability of the system also for non-technical personnel and the availability of individual standard components. Therefore we implemented a monitoring system basing on individual microcontrollers for each vital parameter. Different optical sensors for measurement in transmission and reflection were tested at suitable body sites with near-surface arteries. In combination with the electrocardiogram, evaluation of the pulse transit time enables continuous blood pressure measurements. A specially developed optical reflective sensor allows reliable measurement of blood oxygen level. For extended blood pressure measurements, the pulsetransit-time method (PTT) was implemented and enables a continuous monitoring. Even in emergencies, the trend in blood pressure can be monitored with PTT without prior calibration. The reliability was investigated.

This paper describes the teaching and learning experiences made at the women's Bachelor degree programme ‘Computer science and business administration’. It focuses on the consistent orientation towards practical experiences. Different courses are described showing the strong emphasis on practical training. One special course is described in more detail: ‘Industrial project’, which is held in both semesters, third and fifth, and where teams of students perform real projects for private clients from industry. It is shown that the entire programme is geared to offer practical experiences as much as possible to the students. After two semesters of teaching foundations of computer science, the students carry out the first industrial project supported jointly with advanced students from the fifth semester. At the fourth semester the students complete their 4-5 months internship, the fifth semester students attend the industrial project again and in the sixth semester the students write their Bachelor thesis, usually at an industrial partner's site. The women-only Bachelor degree programme has started in 2009 and has been developed to a success story since then. Almost all graduates have found a job immediately after the studies, or have continued a subsequent Master programme.

Network physiology describes the human body as a complex network of interacting organ systems. It has been applied successfully to determine topological changes in different sleep stages. However, the number of network links can quickly grow above the number of parameters that are typically analyzed with standard statistical methods. Artificial Neural Networks (ANNs) are a promising approach as they are successful in large parameter spaces, such as in digital imaging. On the other hand, ANN models do not provide an intrinsic approach to interpret their predictions, and they typically require large training data sets. Both aspects are critical in biomedical research. Medical decisions need to be explainable, and large data sets of quality assured patient and control data are rare. In this paper, different models for the classification of insomnia-a common sleep disorder-have been trained with 59 patients and age and gender matched controls, based on their physiological networks. Feature relevance evaluation is employed for all methods. For ANNs, the extrinsic interpretation method DeepLift is applied. The results are not identical across methods, but certain network links have been rated as relevant by all or most of the models. While ANNs show less classification accuracy (0.89) than advanced tree-based models (0.92 and 0.93), DeepLift provides an in-depth ANN interpretation with feature relevance scores for individual data samples. The analysis revealed modifications in the pulmonar, ocular, and cerebral subnetworks that have not been described before but are consistent with known findings on the physiological impact of insomnia.

Replication of computational experiments is essential for verifiable research. However, it requires a comprehensive and unambiguous description of all employed digital artifacts, in particular data, code and the computational environment. Recently, the FAIR Guiding Principles have been published to support reproducible research. In this paper, a cloud-based biomedical collaboration platform has been evaluated regarding FAIR principles and has been extended to support reproducibility. The FAICE suite is presented, encompassing tools to thoroughly describe and reproduce a computational experiment within the original execution environment as well as within a dynamically configured VM.

Applications for data analysis of biomedical data are complex programs and often consist of multiple components. Re-usage of existing solutions from external code repositories or program libraries is common in algorithm development. To ease reproducibility as well as transfer of algorithms and required components into distributed infrastructures Linux containers are increasingly used in those environments, that are at least partly connected to the internet. However concerns about the untrusted application remain and are of high interest when medical data is processed. Additionally, the portability of the containers needs to be ensured by using only security technologies, that do not require additional kernel modules. In this paper we describe measures and a solution to secure the execution of an example biomedical application for normalization of multidimensional biosignal recordings. This application, the required runtime environment and the security mechanisms are installed in a Docker-based container. A fine-grained restricted environment (sandbox) for the execution of the application and the prevention of unwanted behaviour is created inside the container. The sandbox is based on the filtering of system calls, as they are required to interact with the operating system to access potentially restricted resources e.g. the filesystem or network. Due to the low-level character of system calls, the creation of an adequate rule set for the sandbox is challenging. Therefore the presented solution includes a monitoring component to collect required data for defining the rules for the application sandbox. Performance evaluation of the application execution shows no significant impact of the resulting sandbox, while detailed monitoring may increase runtime up to over 420%.

Recently, time delay stability analysis of biosignals has been successfully applied as a multivariate time series analysis method to assess the human physiological network in young adults. The degree of connectivity between different network nodes is described by the so-called link strength. Based on polysomnographic recordings (PSGs), it could be shown that the network changes with the sleep stage. Here, we apply the method to a large set of healthy controls spanning six decades of age. As it is well known, that the overall sleep architecture is dependent both on age and on gender, we particularly address the question, if these changes are also found in the network dynamics. We find moderate dependencies of the network on gender. Significantly higher link strengths up to 13% are found in women for some links in different frequency bands of central and occipital regions in REM and light sleep (N2). Higher link strengths are found in men consistently in cardio-cerebral links in N2, but not significant. Age dependency is more pronounced. In particular a significant overall weakening of the network with age is found for wakefulness and non-REM sleep stages. The largest overall decrease is observed in N2 with 0.017 per decade. For individual links decrease rates up to 0.08 per decade are found, in particular for intra-brain links in non-REM sleep. Many of them show a significant decrease with age. Non-linear regression employing an artificial neural network can predict the age with a mean absolute error (MAE) of about five years, suggesting that an age-resolution of about a decade would be appropriate in normative data for physiological networks.

Introduction We investigate how characteristics of sleep-wake dynamics in humans are modified by narcolepsy, a clinical condition that is supposed to destabilize sleep-wake regulation. Subjects with and without cataplexy are considered separately. Differences in sleep scoring habits as a possible confounder have been examined. Aims and methods Four groups of subjects are considered: narcolepsy patients from China with (n = 88) and without (n = 15) cataplexy, healthy controls from China (n = 110) and from Europe (n = 187, 2 nights each). After sleep-stage scoring and calculation of sleep characteristic parameters, the distributions of wake-episode durations and sleep-episode durations are determined for each group and fitted by power laws (exponent α) and by exponentials (decay time τ). Results We find that wake duration distributions are consistent with power laws for healthy subjects (China: α = 0.88, Europe: α = 1.02). Wake durations in all groups of narcolepsy patients, however, follow the exponential law (τ = 6.2-8.1 min). All sleep duration distributions are best fitted by exponentials on long time scales (τ = 34-82 min). Conclusions We conclude that narcolepsy mainly alters the control of wake-episode durations but not sleep-episode durations, irrespective of cataplexy. Observed distributions of shortest wake and sleep durations suggest that differences in scoring habits regarding the scoring of short-term sleep stages may notably influence the fitting parameters but do not affect the main conclusion.

Objective Physiological networks (PN) model couplings between organs in a high-dimensional parameter space. Machine learning methods, in particular artifical neural networks (ANNs), are powerful on high-dimensional classification tasks. However, lack of interpretability of the resulting models has been a drawback in research. We assess relevant PN topology changes in obstructive sleep apnea (OSA) by novel ANN interpretation techniques. Approach ANNs are trained to classify OSA based on the PNs of 48 patients and 48 age and gender matched healthy controls. The PNs consisting of 2812 links are derived from overnight biosignal recordings. The interpretation technique DeepLift is applied to the resulting ANN models, enabling the determination of the relevant features for classification decisions on individual subjects. The mean relevance scores of the features are compared to other machine learning methods (decision tree and random forests) and statistical tests on group differences. Main results The ANN interpretation results show good agreement with the compared methods and 87% of the samples could be correctly classified. OSA patients show a significantly higher coupling (p [Formula: see text] 0.001) in light sleep (N2) between breathing rate and EEG [Formula: see text] power in all electrode locations and to chin and leg muscular tone. In deep sleep (N3), OSA leads to significantly lower coupling (p [Formula: see text] 0.01) in lateral connections of EEG [Formula: see text] and [Formula: see text] power in central and frontal positions. Misclassified OSA patients had all mild/moderate AHIs and did not show PN topology changes. Both nights of these patients have been consistently misclassified as healthy. This may indicate, that the impact of respiratory events differs in subjects, thus forming different phenotypes. Significance The proposed PN analysis provides a powerful and robust method to quantify a broad range of physiological interactions. Interpretability of the ANN make them a promising tool to identify new diagnostic markers in data-driven approaches.

Human sample data is stored in biobanks with software managing digital derived sample data. When these stand-alone components are connected and a search infrastructure is employed users become able to collect required research data from different data sources. Data protection, patient rights, data heterogeneity and access control are major challenges for such an infrastructure. This dissertation will investigate concepts for a multi-level security architecture to comply with these requirements.

Biomedical analysis, in particular image and biosignal analysis, often requires several methods applied to the same data. The data is typically of large volume, so data transfer can become a bottleneck in remote analysis. Furthermore, biomedical data may contain patient data, raising data protection issues. We propose a highly virtualized infrastructure, employing Docker Swarm technology as the computing infrastructure. An underlying Openstack based IaaS cloud provides additional security features for a flexible and efficient multi-tenant analysis platform. We introduce the prototype infrastructure along a sample use-case of multiple versions of a machine-learning method applied to feature sets extracted from multidimensional biosignal recordings from Sleep Apnea patients and healthy controls.

Vibration synchronized magnetic resonance imaging of harmonically oscillating tissue interfaces is proposed for cardiac magnetic resonance elastography. The new approach exploits cardiac triggered cine imaging synchronized with extrinsic harmonic stimulation (f = 22.83 Hz) to display oscillatory tissue deformations in magnitude images. Oscillations are analyzed by intensity threshold-based image processing to track wave amplitude variations over the cardiac cycle. In agreement to literature data, results in 10 volunteers showed that endocardial wave amplitudes during systole (0.13 ± 0.07 mm) were significantly lower than during diastole (0.34 ± 0.14 mm, P < 0.001). Wave amplitudes were found to decrease 117 ± 40 ms before myocardial contraction and to increase 75 ± 31 ms before myocardial relaxation. Vibration synchronized magnetic resonance imaging improves the temporal resolution of magnetic resonance elastography as it overcomes the use of extra motion encoding gradients, is less sensitive to susceptibility artifacts, and does not suffer from dynamic range constraints frequently encountered in phase-based magnetic resonance elastography.

In this paper, we describe the grid integration of medical image processing applications as grid workflows. The workflow management system is able to execute all tasks related to grid communication, such as authorization, scheduling and monitoring. It remains to the developer to make the code accessible for the workflow manager, and to define, what to do with it. Coarse-grained parallelization of processing steps for runtime reduction can easily be realized. We describe the procedure how to port the code to the grid and show exemplarily the integration of segmentation and registration algorithms for transrectal ultrasound guided prostate biopsies.

Grid computing, the collaboration of distributed resources across institutional borders, is an emerging technology to meet the rising demand on computing power and storage capacity in fields such as high-energy physics, climate modeling, or more recently, life sciences. A secure, reliable, and highly efficient data transport plays an integral role in such grid environments and even more so in medical grids. Unfortunately, many grid middleware distributions, such as the well-known Globus Toolkit, lack the integration of the world-wide medical image communication standard Digital Imaging and Communication in Medicine (DICOM). Currently, the DICOM protocol first needs to be converted to the file transfer protocol (FTP) that is offered by the grid middleware. This effectively reduces most of the advantages and security an integrated network of DICOM devices offers. In this paper, a solution is proposed that adapts the DICOM protocol to the Globus grid security infrastructure and utilizes routers to transparently route traffic to and from DICOM systems. Thus, all legacy DICOM devices can be seamlessly integrated into the grid without modifications. A prototype of the grid routers with the most important DICOM functionality has been developed and successfully tested in the MediGRID test bed, the German grid project for life sciences.

Clinical Sleep Research is an inherent multidisciplinary field, as many health issues may affect a person's sleep conditions and sleep disorders may cause several health problems. Many patients with chronic sleep disorders suffer from different further medical conditions -- called multimorbidity. Due to the high variety of the reasons and the courses of sleep disorders, individual cases are difficult to compare. Therefore there is a high demand for sleep researchers to collaborate with each other to reach necessary participant numbers and multidisciplinary expertise. To date, inter-institutional sleep research is poorly supported by IT systems. In particular the heterogeneity and the quality variations within the acquired bio signal data -- caused by different bio signal recorders or different measurement procedures -- are impeding common bio signal data processing. In this manuscript we introduce a virtual research platform supporting inter-institutional data sharing and processing. The infrastructure is based on XNAT -- a free and open-source neuroimaging research platform -- a loosely coupled service oriented architecture and scalable virtualization in the backend. The system is capable of local pseudonymization of bio signal data, mapping to a standardized set of parameters and automatic quality assessment. Terms and quality measures are derived from the "Manual for the Scoring of Sleep and Associated Events" of the American Academy of Sleep Medicine, the de-facto standard for diagnostic bio signal analysis in sleep medicine.

A microbubble in a sound wave oscillates in volume and translates unsteadily. The two motions are coupled. In large-scale simulations of the structure of bubble clouds driven by acoustic fields, it has been of significant convenience to decouple volume oscillations and translation, as an approximation. The errors of this decoupling approximation were considered in an earlier presentation [A. J. Reddy and A. J. Szeri, J. Acoust. Soc. Am. 112, 1346–1352 (2002)], in the parameter range of interest in medical ultrasound. In this work, the approximation is reexamined for a much broader range of driving frequencies and bubble sizes. Solving the equation of motion for linearly oscillating bubbles, it is found that even for weak acoustic forcing, the translation speed obtained with the decoupling approximation can be in error as much as 30% relative to the translation speed in the full equations. The error depends on the bubble size, the driving frequency, and the liquid properties. The results are presented in a...

Filamentary formations of acoustic cavitation bubbles in an ultrasonic resonator are recorded by high-speed stereoscopic means. The bubble locations and motions are reconstructed in three dimensions, and a velocity distribution of bubbles is obtained. Experimental bubble trajectories are compared to a one-to-one simulation by a particle modeling approach which shows reasonable agreement. Such investigations are important for a better understanding of the mechanisms taking place in applications of intense ultrasound in liquids, and for verification and improvement of particle modeling of cavitation bubbles.

Sonoluminescing single bubbles driven simultaneously by two harmonic frequencies were recently reported to increase the maximum light output up to a factor of 3 with respect to single mode excitation. In this paper, experimental and numerical results on single-bubble sonoluminescence (SBSL) in an air/water system using the fundamental mode of 25 kHz and the second harmonic at 50 kHz are presented. The region of light emission is mapped in the three-dimensional parameter space spanned by the two driving pressure amplitudes and their relative phase. Good agreement was seen between measured light output, maximum bubble radius, and stability boundaries and the numerical model which is based on spherical bubble oscillations regarding diffusive and shape stability. The maximum brightness was enhanced by a factor up to 2.5 with respect to single mode SBSL. However, long-term measurements reveal great variation of the emission at fundamental mode driven SBSL and of the boost factor reached with two frequencies. The overall brightness maxima of both excitation methods within a period of several hours turn out to show little difference.

Neurosciencific research is increasingly based on image analysis methods. Large sets of imaging data are processed using complex image analysis tools. While today magnetic resonance imaging (MRI) is widely used for both functional and anatomical analysis of the human brain, new imaging modalities are beginning to prove their capabilities for neurological research. Among them, optical coherence tomography (OCT) allows for noninvasive visualization of anatomical structures on a micrometer scale. Becoming a standard diagnostic tool in ophthalmology, it is of rising interest for neurological research. Crucial to all data analysis methods is the quality of the input data. The platform presented in this paper is designed for automatic quality assessment of retinal OCTs. It extends the image management platform XNAT by services to calculate and store quality measures. It is also extensible regarding new quality measure algorithms, allowing the developer to upload Matlab code, compile it for the infrastructure's hardware architecture and test it in the system. The image processing tools to calculate the quality measures are provided as a cloud-based service employing OpenStack as underlying IT infrastructure. The prototype implementation encompassing security and performance aspects are presented.

Im Studiengang “Informatik und Wirtschaft” an der HTW Berlin wurden in den letzten drei Jahren Erfahrungen mit verschiedenen Lehrformen gesammelt. Der Beitrag greift exemplarisch drei Formate heraus und stellt diese vor. Diskutiert werden die im Curriculum verankerte Veranstaltung “Projekte in der Wirtschaft”, ein Blockseminar in Form eines Hackathon und der Einsatz des Lernteamcoaching als Ersatz fur eine normale Vorlesung.

Sleep medicine and sleep research often include comprehensive analysis of multidimensional biosignal recordings during the whole bedtime phase, so called polysomnographies. In order to build an environment for support on the development of new analysis tools and clinical research in the field of sleep research, a production grid infrastructure is employed. Clinical data and polysomnographies from over 300 persons have been integrated as a reference database, and different algorithms for automated analysis of electrocardiograms (ECGs) are implemented. The database can be queried and matching data can be analyzed individually and as a collection. Workflow integration of the analysis and portlet based user interfaces provide a high level of abstraction, hiding the complexity of the underlying grid system and employing basic fault tolerance. The system is laid out for future extension to allow medical decision support based on the developed methods.

Interactive visualization and correction of intermediate results are required in many medical image analysis pipelines. To allow certain interaction in the remote execution of compute- and data-intensive applications, new features of HTML5 are used. They allow for transparent integration of user interaction into Grid- or Cloud-enabled scientific workflows. Both 2D and 3D visualization and data manipulation can be performed through a scientific gateway without the need to install specific software or web browser plugins. The possibilities of web-based visualization are presented along the FreeSurfer-pipeline, a popular compute- and data-intensive software tool for quantitative neuroimaging.

Personal Health Systems are developed for individualized diagnosis, prevention and treatment depending on the actual health-state of the regarding person with a minimum of interference of his or her daily life. They imply the continuous or at least frequent monitoring of the person's health status, subsequent evaluation and possible action initialization, may it be feedback to the patient, a message to the attending physician or the readjustment of a therapeutical device. In this paper, we propose personal health systems that combine new sensor and information technologies. They allow for extensive undisturbing biosignal acquisition at the person's home and comprehensive data processing and analysis using a grid infrastructure. The grid can be considered as a virtual space where people can share their personal health data with attending medical professionals and where an individual and adaptive set of processing and analysis tools is available in a uniform, easy-to-access and cost-efficient way. The current state of public IT networks and grid infrastructures may not allow real-time applications, while complex near-term postprocessing and decision support can be accomplished. The presented systems are dedicated to sleep related breathing disorders, but the underlying concept may also be suitable for other use-cases.

This chapter describes security and privacy issues within the scope of biomedical Grid Computing. Grid Computing is of rising interest for life sciences (Konagaya, 2006) and has been used since many years in sciences like high energy physics. Anyhow, medical applications on the grid require a special focus on data security and data protection issues. Based on general security and privacy rules, the authors describe the current state of the art of grid security. Then they describe which additional security measures have to be established in different biomedical grid scenarios. Legal aspects have to be taken into account as well as the current possibilities and flaws of grid security technology. Describing the enhanced security concept in MediGRID (MediGRID, 2005) they outline how medical Grid Computing could fulfill privacy regulations used in more demanding environments.

Direct cardiac magnetic resonance elastography (MRE) based on harmonically oscillating tissue interfaces is introduced. This method exploits cardiac triggered cine imaging synchronized to extrinsic harmonic motion of 22.83Hz frequency for displaying oscillatory tissue deformations in the magnitude image. Oscillations are analyzed by intensity-threshold based image processing for delineating wave amplitude variations over the cardiac cycle. Initial results showed that endocardial wave amplitudes during systole (0.13±0.07)mm were significantly lower than during diastole (0.34±0.14)mm. Wave amplitudes were found to decrease (117 ± 40)ms before myocardial contraction and to increase (75±31)ms prior to myocardial relaxation. Not requiring extra motionencoding gradients the introduced method enables short-TR vibration imaging and therewith improves the time resolution in cardiac MRE.