Year: 2023
Type: conference-paper
Abstract: Sleep patterns vary widely between individuals. We explore methods for identifying populations exhibiting similar sleep patterns in an automated fashion using polysomnography data. Our novel approach applies unsupervised machine learning algorithms to hypnodensities graphs generated by a pre-trained neural network. In a population of 100 subjects we identify two stable clusters whose characteristics we visualize graphically and through estimates of total sleep time. We also find that the hypnodensity representation of the sleep stages produces more robust clustering results than the same methods applied to traditional hypnograms.
Authors: Joshua R. Mirth, Christopher L. Felton, Clifton R. Haider, Stuart J. McCarter, Timothy I. Morgenthaler, Erik K. St. Louis, David R. Holmes
Institution: Mayo Clinic
Language: English
Topic: Sleep and related disorders
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: bronze
Funder: Office of Naval Research
Awards: G4516687957, G8876996369
OpenAlex ID: W4389543217