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Prof. Dr. M. Jarke
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RWTH Aachen
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Semantic Anomaly Detection in Medical Time Series

Thesis type
  • Master
Status Running
Proposal on 30. Jan 2019 11:00
Proposal room Library i5
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Medical bedside monitoring of patients relies on anomaly detection in sequence data. Especially the increase of data collected in this safety critical area expedites the need

for fast, accurate and automatic anomaly detection. The main goal of this project is to de fine and evaluate a system that can serve as a basis for surveillance software integrated into monitors that are used in intensive care. Thus, the system has to detect anomalous patterns that are specifi c to certain physiologic signals like the electrical activity of the heart (ECG), blood pressure etc.

Autoencoders based on recurrent neural networks with long short-term memory cells or gated recurrent units will be evaluated for this task. As labelled medical training data is a sparse resource, a clustering approach will be combined with these autoencoders. This  enables unsupervised training of the anomaly detection mechanism.

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