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

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2023-01-03

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3091

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In 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.

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Leveraging IT, AI and Data Science for Healthcare Beyond the Hospital: Learning from Scientific, Operational, and Business Perspectives, machine learning, respiration, sleep research, unsupervised learning

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10

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Proceedings of the 56th Hawaii International Conference on System Sciences

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Attribution-NonCommercial-NoDerivatives 4.0 International

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