Active Learning for LSTM-autoencoder-based Anomaly Detection in Electrocardiogram Readings

0536614 - ÚI 2021 RIV DE eng C - Conference Paper (international conference)
Šabata, T. - Holeňa, Martin
Active Learning for LSTM-autoencoder-based Anomaly Detection in Electrocardiogram Readings.
Proceedings of the Workshop on Interactive Adaptive Learning. Aachen: Technical University & CreateSpace Independent Publishing, 2020 - (Kottke, D.; Krempl, G.; Lemaire, V.; Holzinger, A.; Calma, A.), s. 72-77. CEUR Workshop Proceedings, 2660. ISSN 1613-0073.
[IAL 2020: International Workshop on Interactive Adaptive Learning /4./. Virtual Ghent (BE), 14.09.2020-14.09.2020]
R&D Projects: GA ČR(CZ) GA18-18080S
Institutional support: RVO:67985807
Keywords : Active Learning * Anomaly detection * LSTM-Autoencoder * Time series
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://ceur-ws.org/Vol-2660/ialatecml_shortpaper1.pdf

Recently, the amount of generated time series data has been increasing rapidly in many areas such as healthcare, security, meteorology and others. However, it is very rare that those time series are annotated. For this reason, unsupervised machine learning techniques such as anomaly detection are often used with such data. There exist many unsupervised algorithms for anomaly detection ranging from simple statistical techniques such as moving average or ARIMA till complex deep learning algorithms such as LSTM-autoencoder. For a nice overview of the recent algorithms we refer to read. Difficulties with the unsupervised approach are: defining an anomaly score to correctly represent how anomalous is the time series, and setting a threshold for that score to distinguish between normal and anomaly data. Supervised anomaly detection, on the other hand, needs an expensive involvement of a human expert. An additional problem with supervised anomaly detection is usually the occurrence of very low ratio of anomalies, yielding highly imbalanced data. In this extended abstract, we propose an active learning extension for an anomaly detector based on a LSTM-autoencoder. It performs active learning using various classification algorithms and addresses data imbalance with oversampling and under-sampling techniques. We are currently testing it on the ECG5000 dataset from the UCR time series classification archive.

Permanent Link: http://hdl.handle.net/11104/0314366