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

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.