Title |
Sleep: An Open-Source Python Software for Visualization, Analysis, and Staging of Sleep Data
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Published in |
Frontiers in Neuroinformatics, September 2017
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DOI | 10.3389/fninf.2017.00060 |
Pubmed ID | |
Authors |
Etienne Combrisson, Raphael Vallat, Jean-Baptiste Eichenlaub, Christian O'Reilly, Tarek Lajnef, Aymeric Guillot, Perrine M. Ruby, Karim Jerbi |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 7 | 16% |
Brazil | 3 | 7% |
Switzerland | 3 | 7% |
India | 3 | 7% |
Belgium | 2 | 5% |
United Kingdom | 2 | 5% |
Czechia | 1 | 2% |
Mexico | 1 | 2% |
China | 1 | 2% |
Other | 9 | 20% |
Unknown | 12 | 27% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 31 | 70% |
Scientists | 11 | 25% |
Practitioners (doctors, other healthcare professionals) | 2 | 5% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 178 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 36 | 20% |
Researcher | 25 | 14% |
Student > Master | 18 | 10% |
Student > Bachelor | 14 | 8% |
Student > Doctoral Student | 11 | 6% |
Other | 30 | 17% |
Unknown | 44 | 25% |
Readers by discipline | Count | As % |
---|---|---|
Neuroscience | 39 | 22% |
Engineering | 24 | 13% |
Computer Science | 18 | 10% |
Medicine and Dentistry | 10 | 6% |
Psychology | 7 | 4% |
Other | 24 | 13% |
Unknown | 56 | 31% |