Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
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.
Recommended Citation
Thordarson, Benedikt; Islind, Anna Sigridur; Arnardottir, Erna; and Óskarsdóttir, Maria, "Exploration of Sleep Events in the Latent Space of Variational Autoencoders on a Breath-by-Breath Basis" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 3.
https://aisel.aisnet.org/hicss-56/hc/beyond_hospital/3
Exploration of Sleep Events in the Latent Space of Variational Autoencoders on a Breath-by-Breath Basis
Online
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.
https://aisel.aisnet.org/hicss-56/hc/beyond_hospital/3