RESUMEN
This paper presents a new methodology of feature extraction of sleep and wake stages of a freely behaving rat based on Continuous Wavelet Transform (CWT). The automatic separation of those stages is very useful for experiments related to learning and memory consolidation since recent scientific evidence indicates that sleep is strongly involved with offline reprocessing of acquired information during waking. Our approach transforms hippocampal Local Field Potentials (LFP) in data vectors that describe the energy distribution pattern of the signal on scaled Morlet wavelets projections. Results indicate that the mathematical analysis used in this work can sensibly describe brain signal patterns that correlate to states of behaviour and that our method can be used for a wider range of applications in neuroscience research.
