Key result
SignalPlant software renders images ~163 times faster than EEGLAB for large datasets.
Why the study?
Current 64-bit signal processing software suffers from long latency during visual inspection and labeling of large multichannel recordings such as EEG and ECG data.
Effect estimate: 163-times faster
SignalPlant provides a fast, free, and independent software platform for processing and visualizing large multichannel physiological signals like EEG and ECG.
May enable rapid review of large ECG datasets; leaves open prospective validation in clinical workflows.
The growing technical standard of acquisition systems allows the acquisition of large records, often reaching gigabytes or more in size as is the case with whole-day electroencephalograph (EEG) recordings, for example. Although current 64-bit software for signal processing is able to process (e.g. filter, analyze, etc) such data, visual inspection and labeling will probably suffer from rather long latency during the rendering of large portions of recorded signals. For this reason, we have developed SignalPlant-a stand-alone application for signal inspection, labeling and processing. The main motivation was to supply investigators with a tool allowing fast and interactive work with large multichannel records produced by EEG, electrocardiograph and similar devices. The rendering latency was compared with EEGLAB and proves significantly faster when displaying an image from a large number of samples (e.g. 163-times faster for 75 × 10(6) samples). The presented SignalPlant software is available free and does not depend on any other computation software. Furthermore, it can be extended with plugins by third parties ensuring its adaptability to future research tasks and new data formats.
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Plešinger et al. (2016) studied Biological signal processing (ECG, EEG). SignalPlant software vs. EEGLAB was evaluated on Rendering latency for large number of samples (75 x 10^6 samples) (163-times faster). SignalPlant software demonstrated significantly faster rendering latency compared to EEGLAB, being 163-times faster when displaying an image from 75 million samples.
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