Identification of Sleep Patterns via Clustering of Hypnodensities

Joshua R. Mirth, Christopher L. Felton, Clifton R. Haider, Stuart J. McCarter, Timothy I. Morgenthaler, Erik K.St Louis, David R. Holmes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350324471
DOIs
StatePublished - 2023
Event45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Sydney, Australia
Duration: Jul 24 2023Jul 27 2023

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023
Country/TerritoryAustralia
CitySydney
Period7/24/237/27/23

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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