Health AI Central
Peer-Reviewed Publication
J Neurosci Methods2025;423110516.November 1, 2025Journal Article

Deep clustering of polysomnography data to characterize sleep structure in healthy sleep and non-rapid eye movement parasomnias.

I A M Huijben1, R J G van Sloun2, A Pijpers3, S Overeem4, M M van Gilst4
1Department of Electrical Engineering, Eindhoven University of Technology, The Netherlands; Onera Health, The Netherlands. Electronic address: i.a.m.huijben@tue.nl.
2Department of Electrical Engineering, Eindhoven University of Technology, The Netherlands.
3Sleep Medicine Center Kempenhaeghe, The Netherlands.
4Department of Electrical Engineering, Eindhoven University of Technology, The Netherlands; Sleep Medicine Center Kempenhaeghe, The Netherlands.
PubMed DOI

Abstract

BACKGROUND: The clinical standard to interpret polysomnography (PSG) data is to categorize sleep in five stages, which omits information. SOM-CPC is an unsupervised method that extracts features through contrastive predictive coding (CPC), and visualizes them in two dimensions using a self-organizing map (SOM). We propose various visualizations and analyses for pattern recognition in PSG data thro…

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