Papers › Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection

Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection

4 Apr 20192019 IEEE International Conference on Big Data and Smart Computing (BigComp) 2019 4archive 2025-07-28

João Pereira, Margarida Silveira

The amount of time series data generated in Healthcare is growing very fast and so is the need for methods that can analyse these data, detect anomalies and provide meaningful insights. However, most of the data available is unlabelled and, therefore, anomaly detection in this scenario has been a great challenge for researchers and practitioners. Recently, unsupervised representation learning with deep generative models has been applied to find representations of data, without the need for big labelled datasets. Motivated by their success, we propose an unsupervised framework for anomaly detection in time series data. In our method, both representation learning and anomaly detection are fully unsupervised. In addition, the training data may contain anomalous data. We first learn representations of time series using a Variational Recurrent Autoencoder. Afterwards, based on those representations, we detect anomalous time series using Clustering and the Wasserstein distance. 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. Furthermore, our approach outperforms previous supervised and unsupervised methods on this dataset.

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Tasks

Anomaly Detection Clustering Outlier Detection Representation Learning Time Series Time Series Analysis Unsupervised Anomaly Detection

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Outlier Detection	ECG5000	VRAE+SVM	Accuracy	0.9843	#1 of 3	Archive leaderboard	report
Unsupervised Anomaly Detection	ECG5000	VRAE+SVM	AUC	0.9836	#1 of 1	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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