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Frontiers in Neuroinformatics
2017 . Peer-reviewed
Data sources: Frontiers
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Sleep: An Open-Source Python Software for Visualization, Analysis, and Staging of Sleep Data

Authors: Combrisson, Etienne; Vallat, Raphael; Eichenlaub, Jean-Baptiste; O'Reilly, Christian; Lajnef, Tarek; Guillot, Aymeric; Ruby, Perrine M.; +1 Authors

Sleep: An Open-Source Python Software for Visualization, Analysis, and Staging of Sleep Data

Abstract

We introduce Sleep, a new Python open-source graphical user interface (GUI) dedicated to visualization, scoring and analyses of sleep data. Among its most prominent features are: (1) Dynamic display of polysomnographic data, spectrogram, hypnogram and topographic maps with several customizable parameters, (2) Implementation of several automatic detection of sleep features such as spindles, K-complexes, slow waves, and rapid eye movements (REM), (3) Implementation of practical signal processing tools such as re-referencing or filtering, and (4) Display of main descriptive statistics including publication-ready tables and figures. The software package supports loading and reading raw EEG data from standard file formats such as European Data Format, in addition to a range of commercial data formats. Most importantly, Sleep is built on top of the VisPy library, which provides GPU-based fast and high-level visualization. As a result, it is capable of efficiently handling and displaying large sleep datasets. Sleep is freely available (http://visbrain.org/sleep) and comes with sample datasets and an extensive documentation. Novel functionalities will continue to be added and open-science community efforts are expected to enhance the capacities of this module.

Keywords

polysomnography, opengl, hypnogram, graphoelements, scoring, graphical user interface, automatic detection, electroencephalography, Neuroscience

24 references, page 1 of 3

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Billinger, M., Brunner, C., and Müller-Putz, G. R. (2014). SCoT: a Python Toolbox for EEG Source Connectivity. Available online at: https://pdfs.semanticscholar. org/b196/7f587fbea9ecf4cb6be3f757a8136fc60ca8.pdf [OpenAIRE]

Campagnola, L., Klein, A., Larson, E., Rossant, C., and Rougier, N. P. (2015). “VisPy: harnessing the GPU for fast, high-level visualization,” in Proceedings of the 14th Python in Science Conference. Available online at: https://hal.inria.fr/ hal-01208191/ (Accessed May 23, 2017). [OpenAIRE]

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Combrisson, E., Perrone-Bertolotti, M., Soto, J. L., Alamian, G., Kahane, P., Lachaux, J.-P., et al. (2017). From intentions to actions: neural oscillations encode motor processes through phase, amplitude and phase-amplitude coupling. Neuroimage 147, 473-487. doi: 10.1016/j.neuroimage.2016. 11.042

Devuyst, S., Dutoit, T., Stenuit, P., and Kerkhofs, M. (2011). Automatic sleep spindles detection-overview and development of a standard proposal assessment method. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011, 1713-1716. doi: 10.1109/IEMBS.2011.6090491 [OpenAIRE]

Eichenlaub, J.-B., Bertrand, O., Morlet, D., and Ruby, P. (2014). Brain reactivity differentiates subjects with high and low dream recall frequencies during both sleep and wakefulness. Cereb. Cortex 24, 1206-1215. doi: 10.1093/cercor/bhs388

Eichenlaub, J.-B., Ruby, P., and Morlet, D. (2012). What is the specificity of the response to the own first-name when presented as a novel in a passive oddball paradigm? An ERP study. Brain Res. 1447, 65-78. doi: 10.1016/j.brainres.2012.01.072

Erdamar, A., Duman, F., and Yetkin, S. (2012). A wavelet and teager energy operator based method for automatic detection of K-complex in sleep EEG. Expert Syst. Appl. 39, 1284-1290. doi: 10.1016/j.eswa.2011.07.138

Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Brodbeck, C., et al. (2013). MEG and EEG data analysis with MNE-Python. Front. Neurosci. 7:267. doi: 10.3389/fnins.2013.00267

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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