Appl. Sci., Volume 14, Issue 13 (July-1 2024) – 605 articles
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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). The results using multiple performance metrics indicate that the usage of a basic MLP fed with highly descriptive features such as neural synchronization is enough to efficiently classify SWC stages. View this paper
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