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

Figures

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  Figure 1. EDF in the context from measurement to visualization. Every few seconds the recorded the signal data of each channel is appended. Later, this structure gets transformed into a channel-based representation.

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  Figure 2. Overlay with the EDF file header information, showing the static and the dynamic header data.

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  Figure 3. Runtime data for different SVG and Canvas implementations drawing different amounts of signals (rows) using single values and min-max pairs as data. CNV_1, Canvas with min-max-pairs; CNV_2, Canvas with single values; SVG_1, SVG using multiple path elements and min-max pairs; SVG_2, SVG using one path element for all min-max pairs; SVG_3, SVG using multiple path elements and single values; SVG_4, SVG using a polyline element and single values.

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  Figure 4. A PSG session in Google Chrome on MacOS (left, host) and an emulated tablet (right, peer). Both parties see the same data range and the marked events. The mouse position of the other party is shown as a red dot.

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  Figure 5. Message and data flow within the applications and between the two instances in the collaboration setting. Data sources are always handled through the parser. To enable visualization of data provided by a peer, the cached data is sent over the WebRTC data channel and is presented to the parser of the receiving instance as a WebRTC data source.

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  Figure 6. The user interface when a user is logged in to XNAT and had uploaded a file, which was automatically analyzed. Detected events like artifacts (blue areas) were played back into the visualization.

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  Figure 7. All distinct steps involved from raw data to final picture, and different optimization approaches investigated.