Identification of Sleep Patterns via Clustering of Hypnodensities

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...

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Bibliographic Details
Published in2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Vol. 2023; pp. 1 - 4
Main Authors Mirth, Joshua R., Felton, Christopher L., Haider, Clifton R., McCarter, Stuart J., Morgenthaler, Timothy I., Louis, Erik K. St, Holmes, David R.
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.01.2023
Subjects
Algorithms
Biology
Cluster Analysis
Data visualization
Humans
Machine learning algorithms
Neural networks
Neural Networks, Computer
Polysomnography - methods
Sleep Stages
Sociology
Statistics
Wearable computers
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ISSN2694-0604
DOI10.1109/EMBC40787.2023.10340905

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Summary: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.
ISSN:2694-0604
DOI:10.1109/EMBC40787.2023.10340905