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P#72

Slow Wave detection algorithm in non-REM sleep

Giulia Carbonari

  • Tandil,
  • Argentina
  • Giulia Carbonari ¹
  • , Eugenia Moris ¹
  • , Cecilia Forcato ²
  • , Rodrigo Ramele ³
  • , Ignacio Larrabide ¹
  • 1 PLADEMA, UNICEN-CONICET
  • 2 Laboratorio de Sueño y Memoria, Depto. de Ciencias de la Vida, Instituto Tecnológico de Buenos Aires (ITBA)
  • 3 Centro de Inteligencia Computacional, Depto. de Ingeniería Informática, Instituto Tecnológico de Buenos Aires (ITBA

Sleep is a natural, reversible resting-state and in mammals it is formed by the cyclical occurrence of Rapid Eye Movement (REM) and non-REM sleep. The later one includes slow wave sleep (SWS) and light sleep. SWS is characterized by a generalized bi-stability of
cortical membrane potentials, alternating between “up” and “down” states with a periodicity of approximately one second.

Due to the essential roles of SWS in cognition, sleep restoration and memory consolidation, multiple methods have been used to improve slow-wave sleep, one of the least invasive being auditory closed-loop stimulation.

In this work, we developed a labeling tool to perform manual annotations on EEG signals based on python and the popular MNE package. This tool performs a semi-automatic labeling to assist a visual expert in the identification of the signal components.

As we are particularly interested in Slow Oscillations (SO) and its role in memory function, this program was used to tag five naps and extract the sections where SO were identified. Based on this analysis, parameters that characterize each signal component were extracted: slope, frequency of waves with multiple peaks, and amplitudes. Thus, this tool is the initial step to perform an automatic SO detection to develop the auditory closed-loop stimulation mentioned above.

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