Evaluating common lossless compression algorithms for electrophysiological data

Abstract

Event Abstract Back to Event Evaluating common lossless compression algorithms for electrophysiological data Friedrich Kretschmer1* and Jutta Kretzberg1 1 Carl-von-Ossietzky-University of Oldenburg, AG Computational Neuroscience, Germany Digitally recorded electrophysiological data is typically large due to the required high sampling rates, long recording times and/or amount of channels (e.g. multielectrode recordings). Therefore, data compression is a helpful method to record and archive experimental data. In this study we evaluate the compression ratio and encoding speed of different common lossless compression algorithms applied to electrophysiological recorded data. Most of the presented algorithms are generally used in the field of audio-compression and are therefore not specifically optimized for electrophysiological data. Despite the fact that the achieved compression rates are below the ones of purpose-built algorithms, several advantages in their usage can be noted.All of the tested algorithms are available as standalone programs for common PC-platforms and can freely be downloaded, installed and applied in a form that requires minimal effort from the user. Beside the very established general compression algorithms like gzip, bzip2, LZMA, or RAR several new lossless waveform-compression algorithms are widely used in the audio-domain. In this study we compare the compression results achieved by: “flac”, “WavPack”, “TTA”, “Shorten”, “OptimFrog”, “La” and “Monkey’s Audio” on electrophysiological data and show that a compression ratio (size of the compressed data / size of original data * 100%) of down to ~61% can be achieved at very fast encoding speeds allowing direct recording of the data in compressed form (“flac” and “WavPack”). Lower ratios of ~58% can be achieved at slower compression speed (“OptimFrog”) and even real-time encoding capabilities are supported by “TTA” yielding to a compression ratio of still ~62%. We also show that the compression ratio highly depends on the data being compressed. A higher signal to noise ratio will increase the quality of compression significantly. Multichannel encoding is supported by “flac” (8 channels), “tta” (48 channels) and “WavPack” (14 channels) making them suitable for multielectrode data. Some of the formats can be streamed (“flac” and “WavPack”) and for “flac” even several out-of-the-box hardware implementations exist . The tested algorithms perform very well on electrophysiological data and could be established as a useful tool to significantly reduce the amount of required storage space and/or bandwidth without discarding information in a manner that is easily accessible to everybody. Conference: Neuroinformatics 2009, Pilsen, Czechia, 6 Sep - 8 Sep, 2009. Presentation Type: Poster Presentation Topic: Electrophysiology Citation: Kretschmer F and Kretzberg J (2019). Evaluating common lossless compression algorithms for electrophysiological data. Front. Neuroinform. Conference Abstract: Neuroinformatics 2009. doi: 10.3389/conf.neuro.11.2009.08.068 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 22 May 2009; Published Online: 09 May 2019. * Correspondence: Friedrich Kretschmer, Carl-von-Ossietzky-University of Oldenburg, AG Computational Neuroscience, Oldenburg, Germany, friedrich.kretschmer@informatik.uni-oldenburg.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Friedrich Kretschmer Jutta Kretzberg Google Friedrich Kretschmer Jutta Kretzberg Google Scholar Friedrich Kretschmer Jutta Kretzberg PubMed Friedrich Kretschmer Jutta Kretzberg Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.