P04-Spatial geometric analysis in sleep polysomnographic data

The study is devoted to data processing methods in automatic sleep polysomnography (PSG) analysis. The idea is in using covariance matrices a carrier of a discriminative information. In the study, we are challenging with a problem of sleep stage classification. We are trying to solve that problem using spatial geometric analysis. For experiments, we took data from seven patients; data were recorded in National Institute of Mental Health. Artifact-free segments were extracted from the data. The covariance matrix was obtained for each segment.

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