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Unsupervised Representation Learning and Anomaly Detection in ECG Sequences
Unsupervised representation learning using deep generative models (e. g., variational autoencoders) has been used to learn expressive feature representations of sequences that can make downstream tasks, such as anomaly detection, easier to execute and more accurate.
Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection
Our results on the publicly available ECG5000 electrocardiogram dataset show the ability of the proposed approach to detect anomalous heartbeats in a fully unsupervised fashion, while providing structured and expressive data representations.
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Unsupervised Anomaly Detection
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Unsupervised Anomaly Detection in Energy Time Series Data Using Variational Recurrent Autoencoders with Attention
In this paper, we propose a generic, unsupervised and scalable framework for anomaly detection in time series data, based on a variational recurrent autoencoder.
