2 research outputs found
A Performant Web-Based Visualization, Assessment, and Collaboration Tool for Multidimensional Biosignals
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
TSDF: A simple yet comprehensive, unified data storage and exchange format standard for digital biosensor data in health applications
Digital sensors are increasingly being used to monitor the change over time
of physiological processes in biological health and disease, often using
wearable devices. This generates very large amounts of digital sensor data, for
which, a consensus on a common storage, exchange and archival data format
standard, has yet to be reached. To address this gap, we propose Time Series
Data Format (TSDF): a unified, standardized format for storing all types of
physiological sensor data, across diverse disease areas. We pose a series of
format design criteria and review in detail current storage and exchange
formats. When judged against these criteria, we find these current formats
lacking, and propose a very simple, intuitive standard for both numerical
sensor data and metadata, based on raw binary data and JSON-format text files,
for sensor measurements/timestamps and metadata, respectively. By focusing on
the common characteristics of diverse biosensor data, we define a set of
necessary and sufficient metadata fields for storing, processing, exchanging,
archiving and reliably interpreting, multi-channel biological time series data.
Our aim is for this standardized format to increase the interpretability and
exchangeability of data, thereby contributing to scientific reproducibility in
studies where digital biosensor data forms a key evidence base