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DrCell – A Software Tool for the Analysis of Cell Signals Recorded with Extracellular Microelectrodes
Christoph Nick, Michael Goldhammer, Robert Bestel, Frederik Steger, Andreas Daus, Christiane Thielemann
Pages - 96 - 109 | Revised - 15-08-2013 | Published - 15-09-2013
Published in Signal Processing: An International Journal (SPIJ)
MORE INFORMATION
KEYWORDS
MATLAB® Toolbox, Bio Signal Processing, Spike Sorting, Network Analysis, Extracellular Recording.
ABSTRACT
Microelectrode arrays (MEAs) have been applied for in vivo and in vitro recording and stimulation of electrogenic cells, namely neurons and cardiac myocytes, for almost four decades. Extracellular recordings using the MEA technique inflict minimum adverse effects on cells and enable long term applications such as implants in brain or heart tissue.
Hence, MEAs pose a powerful tool for studying the processes of learning and memory, investigating the pharmacological impacts of drugs and the fundamentals of the basic electrical interface between novel electrode materials and biological tissue. Yet in order to study the areas mentioned above, powerful signal processing and data analysis tools are necessary.
In this paper a novel toolbox for the offline analysis of cell signals is presented that allows a variety of parameters to be detected and analyzed. We developed an intuitive graphical user interface (GUI) that enables users to perform high quality data analysis. The presented MATLAB® based toolbox gives the opportunity to examine a multitude of parameters, such as spike and neural burst timestamps, network bursts, as well as heart beat frequency and signal propagation for cardiomyocytes, signal-to-noise ratio and many more. Additionally a spike-sorting tool is included, offering a powerful tool for cases of multiple cell recordings on a single microelectrode.
For stimulation purposes, artifacts caused by the stimulation signal can be removed from the recording, allowing the detection of field potentials as early as 5 ms after the stimulation.
Hence, MEAs pose a powerful tool for studying the processes of learning and memory, investigating the pharmacological impacts of drugs and the fundamentals of the basic electrical interface between novel electrode materials and biological tissue. Yet in order to study the areas mentioned above, powerful signal processing and data analysis tools are necessary.
In this paper a novel toolbox for the offline analysis of cell signals is presented that allows a variety of parameters to be detected and analyzed. We developed an intuitive graphical user interface (GUI) that enables users to perform high quality data analysis. The presented MATLAB® based toolbox gives the opportunity to examine a multitude of parameters, such as spike and neural burst timestamps, network bursts, as well as heart beat frequency and signal propagation for cardiomyocytes, signal-to-noise ratio and many more. Additionally a spike-sorting tool is included, offering a powerful tool for cases of multiple cell recordings on a single microelectrode.
For stimulation purposes, artifacts caused by the stimulation signal can be removed from the recording, allowing the detection of field potentials as early as 5 ms after the stimulation.
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Mr. Christoph Nick
University of Applied Sciences Aschaffenburg - Germany
Christoph.nick@h-ab.de
Mr. Michael Goldhammer
University of Applied Sciences Aschaffenburg - Germany
Mr. Robert Bestel
University of Applied Sciences Aschaffenburg - Germany
Mr. Frederik Steger
University of Applied Sciences Aschaffenburg - Germany
Dr. Andreas Daus
University of Applied Sciences Aschaffenburg - Germany
Professor Christiane Thielemann
University of Applied Sciences Aschaffenburg - Germany
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