A semi-supervised autoencoder framework for joint generation and classification of breathing.
Journal:
Computer methods and programs in biomedicine
Published Date:
Jul 31, 2021
Abstract
BACKGROUND AND OBJECTIVE: One of the main problems with biomedical signals is the limited amount of patient-specific data and the significant amount of time needed to record the sufficient number of samples needed for diagnostic and treatment purposes. In this study, we present a framework to simultaneously generate and classify biomedical time series based on a modified Adversarial Autoencoder (AAE) algorithm and one-dimensional convolutions. Our work is based on breathing time series, with specific motivation to capture breathing motion during radiotherapy lung cancer treatments.