Multicenter data sharing for collaboration in sleep medicine

Beier, Maximilian; Jansen, Christoph; Mayer, Geert; Penzel, Thomas; Rodenbeck, Andrea; Siewert, René; Witt, Michael; Wu, Jie; Krefting, Dagmar

doi:10.1016/j.future.2016.03.025

Cited by 18 publications

(11 citation statements)

References 26 publications

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“…However our approach should be further validated by different data sets derived from both healthy and pathological populations such as those found in the SIESTA Project (Klosh et al, 2001 ) and the Sleep-EDF database (Kemp et al, 2000 ). The interaction with the above and other databases could be achieved through online applications that allow data processing and sharing such as the platform presented in Frantzidis et al ( 2010 ) and Beier et al ( 2017 ). Another project our system could take part is the SmokeFreeBrain project which records PSG activity before and after varenicline-based (pharmacological) smoking cessation intervention ( http://smokefreebrain.eu/ ).…”

Section: Discussionmentioning

confidence: 99%

Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics

Chriskos

Frantzidιs

Gkivogkli

et al. 2018

Front. Hum. Neurosci.

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Sleep staging, the process of assigning labels to epochs of sleep, depending on the stage of sleep they belong, is an arduous, time consuming and error prone process as the initial recordings are quite often polluted by noise from different sources. To properly analyze such data and extract clinical knowledge, noise components must be removed or alleviated. In this paper a pre-processing and subsequent sleep staging pipeline for the sleep analysis of electroencephalographic signals is described. Two novel methods of functional connectivity estimation (Synchronization Likelihood/SL and Relative Wavelet Entropy/RWE) are comparatively investigated for automatic sleep staging through manually pre-processed electroencephalographic recordings. A multi-step process that renders signals suitable for further analysis is initially described. Then, two methods that rely on extracting synchronization features from electroencephalographic recordings to achieve computerized sleep staging are proposed, based on bivariate features which provide a functional overview of the brain network, contrary to most proposed methods that rely on extracting univariate time and frequency features. Annotation of sleep epochs is achieved through the presented feature extraction methods by training classifiers, which are in turn able to accurately classify new epochs. Analysis of data from sleep experiments on a randomized, controlled bed-rest study, which was organized by the European Space Agency and was conducted in the “ENVIHAB” facility of the Institute of Aerospace Medicine at the German Aerospace Center (DLR) in Cologne, Germany attains high accuracy rates, over 90% based on ground truth that resulted from manual sleep staging by two experienced sleep experts. Therefore, it can be concluded that the above feature extraction methods are suitable for semi-automatic sleep staging.

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Section: Discussionmentioning

confidence: 99%

Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics

Chriskos

Frantzidιs

Gkivogkli

et al. 2018

Front. Hum. Neurosci.

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“…Polysomnographic recordings are multidimensional longterm biosignal recordings used in sleep medicine and research to capture the physiological state during the night. It takes several input arguments and produces multiple output files . The most complex application of the set is provided as a compiled MATLAB application.…”

Section: Methodsmentioning

confidence: 99%

Sandboxing of biomedical applications in Linux containers based on system call evaluation

Witt

Jansen

Krefting

et al. 2018

Concurrency and Computation

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Summary Applications for biomedical data processing often integrate external libraries and frameworks for common algorithmic tasks. It typically reduces development time and increases overall code quality. With the introduction of lightweight container‐based virtualization, the bundling of applications and their required dependencies has become feasible, and containers can be transferred and executed in distributed environments. However, the incorporation of unreviewed code poses a security threat as it might contain malicious components. In this paper, measures to minimize risks of untrusted application execution are presented. Based on the system calls issued during sample execution of the application, both the container itself and the container runtime configuration are restricted to the set of actions the application requires. It is shown that the employed security measures are suited to counteract different attacks while application runtime is not affected.

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“…In 2015 our proof-of-concept version of a purely web-browser-based EDF parser and visualizer was published as part of a biosignal research infrastructure (Beier et al, 2017). It supports requirements (a) and (e)–(g), but is relatively slow and offers no further features.…”

Section: Related Workmentioning

confidence: 99%

A Performant Web-Based Visualization, Assessment, and Collaboration Tool for Multidimensional Biosignals

Beier

Penzel

Krefting

2019

Front. Neuroinform.

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Biosignal-based research is often multidisciplinary and benefits greatly from multi-site collaboration. This requires appropriate tooling that supports collaboration, is easy to use, and is accessible. However, current software tools do not provide the necessary functionality, usability, and ubiquitous availability. The latter is particularly crucial in environments, such as hospitals, which often restrict users' permissions to install software. This paper introduces a new web-based application for interactive biosignal visualization and assessment. A focus has been placed on performance to allow for handling files of any size. The proposed solution can load local and remote files. It parses data locally on the client, and harmonizes channel labels. The data can then be scored, annotated, pseudonymized and uploaded to a clinical data management system for further analysis. The data and all actions can be interactively shared with a second party. This lowers the barrier to quickly visually examine data, collaborate and make informed decisions.

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Multicenter data sharing for collaboration in sleep medicine

Cited by 18 publications

References 26 publications

Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics

Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics

Sandboxing of biomedical applications in Linux containers based on system call evaluation

A Performant Web-Based Visualization, Assessment, and Collaboration Tool for Multidimensional Biosignals

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