A semi-supervised autoencoder framework for joint generation and classification of breathing

TL;DR

This paper introduces a semi-supervised autoencoder framework that jointly generates and classifies breathing time series, improving diagnosis and data augmentation for lung cancer radiotherapy using a modified Adversarial Autoencoder and neural network-based preprocessing.

Contribution

It presents the first unified model for simultaneous generation and classification of biomedical time series, specifically breathing signals, using a modified AAE with semi-supervised learning.

Findings

Outperforms purely discriminative models in classifying breathing irregularities.

Effectively models patient-specific breathing patterns with limited labeled data.

Enables data augmentation and diagnosis within a single neural network framework.

Abstract

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.