Exploration of Sleep Events in the Latent Space of Variational Autoencoders on a Breath-by-Breath Basis

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dc.contributor.authorThordarson, Benedikt
dc.contributor.authorIslind, Anna Sigridur
dc.contributor.authorArnardottir, Erna
dc.contributor.authorÓskarsdóttir, Maria
dc.date.accessioned2022-12-27T19:06:54Z
dc.date.available2022-12-27T19:06:54Z
dc.date.issued2023-01-03
dc.description.abstractIn this exploratory paper, we attempt to address a growing demand for unsupervised machine learning techniques on sleep data by applying a variational autoencoder on respiratory sleep data on a breath-by-breath basis. We transform respiratory signals into a latent representation and cluster them together using KMeans clustering. We calculate the cluster preference of scored events and attempt to explain their position in the latent space. We show that a variational autoencoder can accurately reconstruct three respiratory signals from individual breaths despite being sampled through a latent dimension 384 times smaller than the input data. Our results also indicate that respiratory events in particular show a tendency to cluster together in the latent space despite a purely unsupervised learning approach. Finally, we lay the groundwork for future work made possible in this paper.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2023.380
dc.identifier.isbn978-0-9981331-6-4
dc.identifier.other56c89a4a-cda1-4f19-851b-074f1fbc199c
dc.identifier.urihttps://hdl.handle.net/10125/103011
dc.language.isoeng
dc.relation.ispartofProceedings of the 56th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectLeveraging IT, AI and Data Science for Healthcare Beyond the Hospital: Learning from Scientific, Operational, and Business Perspectives
dc.subjectmachine learning
dc.subjectrespiration
dc.subjectsleep research
dc.subjectunsupervised learning
dc.titleExploration of Sleep Events in the Latent Space of Variational Autoencoders on a Breath-by-Breath Basis
dc.type.dcmitext
prism.startingpage3091

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Leveraging IT, AI and Data Science for Healthcare Beyond the Hospital: Learning from Scientific, Operational, and Business Perspectives