title: FTSPlot: fast time series visualization for large datasets.
abstract: The analysis of electrophysiological recordings often involves visual inspection of time series data to locate specific experiment epochs, mask artifacts, and verify the results of signal processing steps, such as filtering or spike detection. Long-term experiments with continuous data acquisition generate large amounts of data. Rapid browsing through these massive datasets poses a challenge to conventional data plotting software because the plotting time increases proportionately to the increase in the volume of data. This paper presents FTSPlot, which is a visualization concept for large-scale time series datasets using techniques from the field of high performance computer graphics, such as hierarchic level of detail and out-of-core data handling. In a preprocessing step, time series data, event, and interval annotations are converted into an optimized data format, which then permits fast, interactive visualization. The preprocessing step has a computational complexity of O(n x log(N)); the visualization itself can be done with a complexity of O(1) and is therefore independent of the amount of data. A demonstration prototype has been implemented and benchmarks show that the technology is capable of displaying large amounts of time series data, event, and interval annotations lag-free with < 20 ms ms. The current 64-bit implementation theoretically supports datasets with up to 2(64) bytes, on the x86_64 architecture currently up to 2(48) bytes are supported, and benchmarks have been conducted with 2(40) bytes/1 TiB or 1.3 x 10(11) double precision samples. The presented software is freely available and can be included as a Qt GUI component in future software projects, providing a standard visualization method for long-term electrophysiological experiments.
authors.affiliation: Department of ETSEIB, Technical University of Catalonia (UPC), Barcelona, Spain.
authors.forename: Michael
authors.initials: M
authors.lastname: Riss
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D016208:Databases, Factual; D004594:Electrophysiology; D006801:Humans; D021621:Imaging, Three-Dimensional; D009474:Neurons / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D012984:Software
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