Semi-Supervised Recurrent Variational Autoencoder Approach for Visual Diagnosis of Atrial Fibrillation
In this work we propose a semi-supervised framework to visually assess the progression of time series. To this end, we present a recurrent version of the VAE to exploit the generative properties that lead it to learn in an unsupervised way a continuous compressed representation of the data. We introduce a classifier in the VAE training process to control the regulation of the latent space, allowing the network to learn latent variables that set the basis for creating an explainable evaluation of the data. We use the proposed framework to address the diagnosis of Atrial Fibrillation (AF) first validating it with simulated data with known properties and subsequently testing it with intracardiac data obtained from pacemakers and defibrillator systems.
Code (1)
Tasks
Time SeriesTime Series AnalysisSimilar Papers 제목 키워드 기반
Intentional Choreography with Semi-Supervised Recurrent VAEs
We summarize the model and results of PirouNet, a semi-supervised recurrent variational autoencoder. Given a small amount of dance sequences labeled with qualitative choreographic annotations, PirouNet conditionally gene…
Semi-Supervised Variational Inference over Nonlinear Channels
Deep learning methods for communications over unknown nonlinear channels have attracted considerable interest recently. In this paper, we consider semi-supervised learning methods, which are based on variational inferenc…
Meta-LearningVariational InferenceDeep Recurrent Semi-Supervised EEG Representation Learning for Emotion Recognition
EEG-based emotion recognition often requires sufficient labeled training samples to build an effective computational model. Labeling EEG data, on the other hand, is often expensive and time-consuming. To tackle this prob…
Deep AttentionEEGElectroencephalogram (EEG)Emotion Recognition+1Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder
Operational risk management is one of the biggest challenges nowadays faced by financial institutions. There are several major challenges of building a text classification system for automatic operational risk prediction…
ClassificationGeneral ClassificationManagementSemi-Supervised Text Classification+3Adversarial Autoencoders
In this paper, we propose the "adversarial autoencoder" (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the a…
ClusteringData VisualizationDecoderDimensionality Reduction+4