Unsupervised representation learning and anomaly detection in ECG sequences

João Pereira, Margarida Silveira

Research output: Contribution to journalArticleAcademicpeer-review

12 Citations (Scopus)


While the big data revolution takes place, large amounts of electronic health records, such as electrocardiograms (ECGs) and vital signs data, have become available. These signals are often recorded as time series of observations and are now easier to obtain. In particular, with the arise of smart devices that can perform ECG, there is the quest for developing novel approaches that allow to monitor these signals efficiently, and quickly detect anomalies. However, since most data generated remains unlabelled, the task of anomaly detection is still very challenging. 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. We propose an approach for unsupervised representation learning of ECG sequences using a variational autoencoder parameterised by recurrent neural networks, and use the learned representations for anomaly detection using multiple detection strategies. We tested our approach on the ECG5000 electrocardiogram dataset of the UCR time series classification archive. Our results show that the proposed approach is able to learn expressive representations of ECG sequences, and to detect anomalies with scores that outperform other both supervised and unsupervised methods.

Original languageEnglish
Pages (from-to)389-407
Number of pages19
JournalInternational Journal of Data Mining and Bioinformatics
Issue number4
Publication statusPublished - 2019
Externally publishedYes


  • Anomaly detection
  • Bioinformatics
  • Clustering
  • Data mining
  • Deep learning
  • Electrocardiogram
  • Healthcare
  • Recurrent neural networks
  • Representation learning
  • Time series
  • Unsupervised learning
  • Variational autoencoders


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