Supervised semi-automatic detection of slow waves in non-anaesthetized mice with the use of neural network approach

Authors: Bukhtiyarova, OlgaSoltani, SaraChauvette, SylvainTimofeev, Igor
Abstract: Slow waves (SWs) are EEG or local field potential (LFP) events that are present preferentially during slow-wave sleep and reflect periods of synchronized hyperpolarization followed by depolarization of many cortical neurons. We developed a new algorithm of supervised semi-automatic SW detection based on pattern recognition of the original signal with artificial neural network. The method enabled fast analysis of long-lasting recordings in non-anaesthetized freely behaving mice. It allowed finding tens of thousands of SW in 24-hour period of recording with their density in the order of 1.3 SW per second during slow-wave sleep and 0.03 SW per second during waking state. Occasional SWs were also found in REM sleep. The proposed algorithm can be used for off-line and on-line detection of SW
Document Type: Article de recherche
Issue Date: 20 April 2016
Open Access Date: 10 May 2017
Document version: VoR
Permalink: http://hdl.handle.net/20.500.11794/14018
This document was published in: Translational Brain Rhythmicity, Vol. 1 (1), 14-18 (2016)
https://doi.org/10.15761/TBR.1000104
Open Access Text
Alternative version: 10.15761/TBR.1000104
Collection:Articles publiés dans des revues avec comité de lecture

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