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Towards a New Evolutionary Subsampling Technique for Heuristic Optimisation of Load Disaggregators Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Normalized Cross-Match: Pattern Discovery Algorithm from Biofeedback Signals

Authors : Xueyuan Gong, Simon Fong, Yain-Whar Si, Robert P. Biuk-Aghai, Raymond K. Wong, Athanasios V. Vasilakos

Published in: Trends and Applications in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

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Abstract

Biofeedback signals are important elements in critical care applications, such as monitoring ECG data of a patient, discovering patterns from large amount of ECG data sets, detecting outliers from ECG data, etc. Because the signal data update continuously and the sampling rates may be different, time-series data stream is harder to be dealt with compared to traditional historical time-series data. For the pattern discovery problem on time-series streams, Toyoda proposed the CrossMatch (CM) approach to discover the patterns between two time-series data streams (sequences), which requires only O(n) time per data update, where n is the length of one sequence. CM, however, does not support normalization, which is required for some kinds of sequences (e.g. EEG data, ECG data). Therefore, we propose a normalized-CrossMatch approach (NCM) that extends CM to enforce normalization while maintaining the same performance capabilities.

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Metadata
Title
Normalized Cross-Match: Pattern Discovery Algorithm from Biofeedback Signals
Authors
Xueyuan Gong
Simon Fong
Yain-Whar Si
Robert P. Biuk-Aghai
Raymond K. Wong
Athanasios V. Vasilakos
Copyright Year
2016
Book
Trends and Applications in Knowledge Discovery and Data Mining

Print ISBN: 978-3-319-42995-3

Electronic ISBN: 978-3-319-42996-0

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-42996-0_14

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