Code for detecting and classifying epileptiform activity (EA)

Code for detecting and classifying epileptiform activity (EA)
Written by Katharina Heining, last modified 2020/10/20  
Institution: University of Freiburg, Germany
Accompanying Paschen et al. (2020), eLife

The subdirectory core contains the main code:  

•  ed_detection.py:    wrapper for preprocessing, spike detection and spike sorting  
•  artisfaction.py:    semiautomatic identification of artifacts
•  blipS.py:            spike detection* blipsort.py:        spike sorting
•  ea_analysis.py:        wrapper for burst detection and classification
•  somify.py:            projecting data on a SOM and SOM plotting
•  helpers.py:            supportive functions for the other scripts
•  ea_management.py:    reading data, handling results, recording-class functions

configAnalysis.yml contains parameters used for analyses  
som.h5 holds the SOM obtained from reference dataset -- see Heining et al. (2019), referenced below.*  

The subdirectory code_for_figures contains the code used for illustration (Supplementary Figure 1).
 
The code contained in this folder is © K. Heining, 2020, developed at the University of Freiburg.  
This code is made available under the BSD license enclosed with the software (see licence.txt).
Over and above the legal restrictions imposed by this license, if you use this software for an academic publication then you are obliged to provide proper attribution.
For this, you need to cite the paper that describes the code:  
* Heining, K., Kilias, A., Janz, P., Häussler, U., Kumar, A., Haas, C. A., and Egert, U.
(2019). Bursts with high and low load of epileptiform spikes show context-dependent
correlations in epileptic mice. eNeuro, 6(5).