PTB-XL+, a comprehensive electrocardiographic feature dataset

Machine learning (ML) methods for the analysis of electrocardiography (ECG) data are gaining importance, substantially supported by the release of large public datasets. However, these current datasets miss important derived descriptors such as ECG features that have been devised in the past hundred years and still form the basis of most automatic ECG analysis algorithms and are critical for cardiologists’ decision processes. ECG features are available from sophisticated commercial software but are not accessible to the general public. To alleviate this issue, we add ECG features from two leading commercial algorithms and an open-source implementation supplemented by a set of automatic diagnostic statements from a commercial ECG analysis software in preprocessed format. This allows the comparison of ML models trained on clinically versus automatically generated label sets. We provide an extensive technical validation of features and diagnostic statements for ML applications. We believe this release crucially enhances the usability of the PTB-XL dataset as a reference dataset for ML methods in the context of ECG data

Prenatal RhD Testing: A Review of Studies Published from 2006 to 2008*

The availability of noninvasive prenatal diagnosis for the fetal RhD status (NIPD RhD) is an obvious benefit for alloimmunized pregnant women. This review gives information about the performance characteristics of current diagnostic technologies and recent promising proof-of-principle studies. Notably, during the past 3 years almost twice as much samples have been investigated with NIPD RhD compared with the studies from 1998 to 2005. Thus we have now a lot more information compared with the knowledge before 2006. There is no doubt that funding of the SAFE Network of Excellence (2004–2009) from the European Commission within the framework 6 program has massively increased the worldwide experience in NIPD RhD. In 2009 European funding has been stopped. Because of this large investment from public funding sources, it is now the duty of policy makers (scientific boards, patient groups, physician organizations, and health assurances) to discuss if targeted antenatal Rh prophylaxis should be introduced in German-speaking countries or which additional data are required to make a decision and how these additional studies should be funded

PTB-XL+, a comprehensive electrocardiographic feature dataset

Abstract Machine learning (ML) methods for the analysis of electrocardiography (ECG) data are gaining importance, substantially supported by the release of large public datasets. However, these current datasets miss important derived descriptors such as ECG features that have been devised in the past hundred years and still form the basis of most automatic ECG analysis algorithms and are critical for cardiologists’ decision processes. ECG features are available from sophisticated commercial software but are not accessible to the general public. To alleviate this issue, we add ECG features from two leading commercial algorithms and an open-source implementation supplemented by a set of automatic diagnostic statements from a commercial ECG analysis software in preprocessed format. This allows the comparison of ML models trained on clinically versus automatically generated label sets. We provide an extensive technical validation of features and diagnostic statements for ML applications. We believe this release crucially enhances the usability of the PTB-XL dataset as a reference dataset for ML methods in the context of ECG data

Development and ramp-up of automated laser assembly: Final Report of the MANUNET Research Project DeLas

In order to respond to increasing competitive pressure from low-wage countries on manufacturing companies in Western Europe, competitive assembly systems need to be developed by combining flexibility, autonomy, and productivity. In high-wage countries, this goal can only be achieved by a high level of automation. Furthermore, automation allows for implementing highly reliable processes and has the potential to increase product quality and improve working conditions at the same time. High-performing modular machine architectures have found their way in every day industrial production. The remaining challenge lies in the efficient planning of complex assembly tasks. In this context, the objective of the funded research project DeLas was the significant reduction of process development and production ramp-up times for the automated assembly of laser systems and optical modules by bridging the gap between product development and automation control. The approach of the project was to develop and integrate methods and software tools for implementing complex sensor-guided handling and alignment processes into a software toolchain which covers the relevant steps of automated laser assembly. Based on virtual production environments, virtual and real commissioning of processes grew closer together and strengthened the role and impact of offline programming. The DeLas team focused on the missing links between the commercially available and well-established engineering tools. Therefore, interfaces between the different software systems have been realized. Parallel to the conceptual design and realization of the engineering toolchain, industrial use cases from the assembly of optical systems have been studied for a better understanding of industrial needs. The studies included the complete engineering chain between product specification and process commissioning on an industrial assembly platform. A demonstrator showing a stable assembly process for micro-optical systems has been presented during several events and trade fairs. The project has successfully shown the potential of better integration of engineering tools and engineering models in the industrial branches of lasers and optics. Main impact can be expected from shortening the time between product design and process commissioning potentially influencing the time-to-market (TTM) and machine utilization in an environment of versatile production requiring frequent commissioning

Femoral antetorsion: comparing asymptomatic volunteers and patients with femoroacetabular impingement

PURPOSE: To assess the range of femoral antetorsion with magnetic resonance (MR) imaging in asymptomatic volunteers and patients with different subtypes of femoroacetabular impingement (FAI) because abnormal femoral antetorsion might be a contributing factor in the development of FAI. MATERIALS AND METHODS: This study was institutional review board approved; all individuals provided signed informed consent. Sixty-three asymptomatic volunteers and 63 patients with symptomatic FAI between age 20 and 50 years were matched for age and sex. They underwent standard MR imaging with two additional rapid transverse sequences over the proximal and distal femur for antetorsion measurement. Twenty volunteers underwent a second MR imaging examination in the same leg. Two readers independently measured femoral antetorsion. The time for the additional sequences was tabulated. Interobserver agreement was calculated; differences in antetorsion were assessed by using analysis of variance and the unpaired t test. RESULTS: Femoral antetorsion can be assessed with MR imaging in about 80 seconds, with high interobserver agreement (intraclass correlation coefficient [ICC] = 0.967) and high agreement between different MR examinations (ICC = 0.966). Women had a significantly larger antetorsion than men (P < .001 for both readers), and antetorsion of the left femur was significantly larger than that of the right femur (P = .01 for reader 1, P = .02 for reader 2). Overall, antetorsion was similar in volunteers and in patients for reader 1 (12.7° ± 10.0 [standard deviation] vs 12.6° ± 9.8, respectively; P = .9) and reader 2 (12.8° ± 10.1 vs 13.5° ± 9.8, respectively; P = .7). Femoral antetorsion was significantly higher in patients with pincer-type FAI than in those with cam-type FAI for reader 1 (18.3° ± 9.8 vs 10.0° ± 9.1, P = .02) and reader 2 (18.7° ± 10.5 vs 11.6° ± 8.8, P = .04). CONCLUSION: Femoral antetorsion can be measured rapidly and with good reproducibility with MR imaging. Patients with pincer-type FAI had a significantly larger femoral antetorsion than patients with cam-type FAI

Detection of Patients with Congenital and Often Concealed Long-QT Syndrome by Novel Deep Learning Models

Introduction: The long-QT syndrome (LQTS) is the most common ion channelopathy, typically presenting with a prolonged QT interval and clinical symptoms such as syncope or sudden cardiac death. Patients may present with a concealed phenotype making the diagnosis challenging. Correctly diagnosing at-risk patients is pivotal to starting early preventive treatment. Objective: Identification of congenital and often concealed LQTS by utilizing novel deep learning network architectures, which are specifically designed for multichannel time series and therefore particularly suitable for ECG data. Design and Results: A retrospective artificial intelligence (AI)-based analysis was performed using a 12-lead ECG of genetically confirmed LQTS (n = 124), including 41 patients with a concealed LQTS (33%), and validated against a control cohort (n = 161 of patients) without known LQTS or without QT-prolonging drug treatment but any other cardiovascular disease. The performance of a fully convolutional network (FCN) used in prior studies was compared with a different, novel convolutional neural network model (XceptionTime). We found that the XceptionTime model was able to achieve a higher balanced accuracy score (91.8%) than the associated FCN metric (83.6%), indicating improved prediction possibilities of novel AI architectures. The predictive accuracy prevailed independently of age and QTc parameters. Conclusions: In this study, the XceptionTime model outperformed the FCN model for LQTS patients with even better results than in prior studies. Even when a patient cohort with cardiovascular comorbidities is used. AI-based ECG analysis is a promising step for correct LQTS patient identification, especially if common diagnostic measures might be misleading

Ambient Assisted Living - Ein Markt der Zukunft : Potenziale, Szenarien, Geschäftsmodelle

Der Begriff "Ambient Assisted Living" (AAL) steht für einen grundlegenden Paradigmenwechsel in der Interaktion zwischen Mensch und Lebensumgebung. AAL-Technologien bilden Assistenzsysteme, die in das direkte Lebensumfeld von Menschen integriert sind. Durch die Anpassung der Systeme an die speziellen Anforderungen ihrer Nutzer erhöhen sie im jeweiligen Nutzungskontext in jedem Lebensalter deren Lebensqualität. Die Industriezweige stehen vor der Herausforderung für AAL-Systeme Märkte zu entwickeln und zur Wirtschaftlichkeit zu führen. Diese Publikation greift diese Thematik auf. Sie behandelt unterschiedliche Geschäftsmodellansätze in sich entwickelnden AAL-Märkten. Im Vordergrund steht zunächst die modellhafte Beschreibung des Geschäftes als eine vereinfachte und 5 aggregierte Darstellung von betrieblichen Produktions- und Leistungssystemen einer Unternehmung. Verwendet wird ein Ansatz, der es erlaubt die besonderen Rahmenbedingungen zu berücksichtigen, die in bestimmten Industriezweigen die Schlüsselfunktionen unternehmerischen Handelns darstellen und den angestrebten wirtschaftlichen Erfolg einer Unternehmung bedingen. Das Modell macht die komplexen Zusammenhänge dem notwendigen Diskurs zugänglich