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Learning Bayesian Network to Predict Group Emotion in Kindergarten by Evolutionary Computation Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Sparse Representation Based Anomalies Detection in Electrocardiography Signals

Author : Tomasz Andrysiak

Published in: International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding

Publisher: Springer International Publishing

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Abstract

In this article, we present the use of sparse representation of signal and dictionary learning method for solving the problem of anomaly detection. The analyzed signal was presented as a set of correct ECG structures and outliers (characterizing different types of disorders). In the course of learning we used the modified Method of Optimal Directions (MOD) to find a dictionary that would reflect correct structures of an ECG signal. The dictionary found this way became a basis for sparse representation of the analyzed ECG signal. In the process of anomaly detection based on decomposition of the analyzed signal onto correct values and outliers, there was used a modified Alternating Minimization Algorithm (AMA). Performance of the proposed method was tested using a widely available database of ECG signals - MIT–BIH Arrhythmia Database. The obtained experimental results confirmed the effectiveness of the method of anomaly detection in the analysed ECG signals.

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Metadata
Title
Sparse Representation Based Anomalies Detection in Electrocardiography Signals
Author
Tomasz Andrysiak
Copyright Year
2018
Book
International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding

Print ISBN: 978-3-319-67179-6

Electronic ISBN: 978-3-319-67180-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-67180-2_42

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