Thordarson, BenediktIslind, Anna SigridurArnardottir, ErnaÓskarsdóttir, Maria2022-12-272022-12-272023-01-03978-0-9981331-6-456c89a4a-cda1-4f19-851b-074f1fbc199chttps://hdl.handle.net/10125/103011In 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.10engAttribution-NonCommercial-NoDerivatives 4.0 InternationalLeveraging IT, AI and Data Science for Healthcare Beyond the Hospital: Learning from Scientific, Operational, and Business Perspectivesmachine learningrespirationsleep researchunsupervised learningtext10.24251/HICSS.2023.380