Leiden University Scholarly Publications

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Thill, M.; Däubener, S.; Konen, W.; Bäck, T.H.W. (2019)

Anomaly Detection in Electrocardiogram Readings with Stacked LSTM Networks

Article in monograph or in proceedings
Real-world anomaly detection for time series is still a challenging task. This is especially true for periodic or quasi-periodic time series since automated approaches have to learn long-term correlations before they are able to detect anomalies. Electrocardiography (ECG) time series, a prominent real-world example of quasi-periodic signals, are investigated in this work. Anomaly detection algorithms often have the additional goal to identify anomalies in an unsupervised manner. In this paper we present an unsupervised time series anomaly detection algorithm. It learns with recurrent Long Short-Term Memory (LSTM) networks to predict the normal time series behavior. The prediction error on several prediction horizons is used to build a statistical model of normal behavior. We propose new methods that are essential for a successful model-building process and for a high signal-to-noise-ratio. We apply our method to the well-known MIT-BIH ECG data set and present first results. We...Show moreReal-world anomaly detection for time series is still a challenging task. This is especially true for periodic or quasi-periodic time series since automated approaches have to learn long-term correlations before they are able to detect anomalies. Electrocardiography (ECG) time series, a prominent real-world example of quasi-periodic signals, are investigated in this work. Anomaly detection algorithms often have the additional goal to identify anomalies in an unsupervised manner. In this paper we present an unsupervised time series anomaly detection algorithm. It learns with recurrent Long Short-Term Memory (LSTM) networks to predict the normal time series behavior. The prediction error on several prediction horizons is used to build a statistical model of normal behavior. We propose new methods that are essential for a successful model-building process and for a high signal-to-noise-ratio. We apply our method to the well-known MIT-BIH ECG data set and present first results. We obtain a good recall of anomalies while having a very low false alarm rate (FPR) in a fully unsupervised procedure. We compare also with other anomaly detectors (NuPic, ADVec) from the state-of-the-art.Show less
All authors
Thill, M.; Däubener, S.; Konen, W.; Bäck, T.H.W.
Editor(s)
Barancikova, P.; Holena, M.; Horvat, T.; Pleva, M.; Rosa, R.
Date
2019
Title of host publication
Proceedings of the 19th Conference Information Technologies - Applications and Theory (ITAT 2019)
Pages
17 - 25
Link
http://ceur-ws.org/Vol-2473/

Publication Series

Name
2473

Conference

Conference
19th Conference Information Technologies - Applications and Theory (ITAT 2019)
Date
2019-09-20 - 2019-09-24
Location
Donovaly, Slovakia