  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. First, we explore the potential in using
the Variational Autoencoder (VAE) and AAE algorithms to model breathing from
individual patients. We extend the AAE algorithm to allow joint semi-supervised
classification and generation of different types of signals. To simplify the
modeling task, we introduce a pre-processing and post-processing compressing
algorithm that transforms the multi-dimensional time series into vectors
containing time and position values, which are transformed back into time
series through an additional neural network. By incorporating few labeled
samples during training, our model outperforms other purely discriminative
networks in classifying breathing baseline shift irregularities from a dataset
completely different from the training set. To our knowledge, the presented
framework is the first approach that unifies generation and classification
within a single model for this type of biomedical data, enabling both computer
aided diagnosis and augmentation of labeled samples within a single framework.
