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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00536614" target="_blank" >RIV/67985807:_____/20:00536614 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21240/20:00343031

Result on the web

<a href="http://ceur-ws.org/Vol-2660/ialatecml_shortpaper1.pdf" target="_blank" >http://ceur-ws.org/Vol-2660/ialatecml_shortpaper1.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

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

Original language description

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.n

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA18-18080S" target="_blank" >GA18-18080S: Fusion-Based Knowledge Discovery in Human Activity Data</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the Workshop on Interactive Adaptive Learning

ISBN

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ISSN

1613-0073

e-ISSN

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Number of pages

6

Pages from-to

72-77

Publisher name

Technical University & CreateSpace Independent Publishing

Place of publication

Aachen

Event location

Virtual Ghent

Event date

Sep 14, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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