Efficient Sleep–Wake Cycle Staging via Phase–Amplitude Coupling Pattern Classification
Vinícius Rosa Cota,
Simone Del Corso,
Gianluca Federici
et al.
Abstract:The objective and automatic detection of the sleep–wake cycle (SWC) stages is essential for the investigation of its physiology and dysfunction. Here, we propose a machine learning model for the classification of SWC stages based on the measurement of synchronization between neural oscillations of different frequencies. Publicly available electrophysiological recordings of mice were analyzed for the computation of phase–amplitude couplings, which were then supplied to a multilayer perceptron (MLP). Firstly, we… Show more
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