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Normalized Cross-Match: Pattern Discovery Algorithm from Biofeedback Signals
Department of Computer and Information Science, University of Macau.
Department of Computer and Information Science, University of Macau.
Department of Computer and Information Science, University of Macau.
School of Computer Science and Engineering, University of New South Wales, Sydney.
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Number of Authors: 62016 (English)In: Trends and Applications in Knowledge Discovery and Data Mining: PAKDD 2016 Workshops, BDM, MLSDA, PACC, WDMBF, Auckland, New Zealand, April 19, 2016, Revised Selected Papers, Encyclopedia of Global Archaeology/Springer Verlag, 2016, p. 169-180Conference paper, Published paper (Refereed)
Abstract [en]

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

Place, publisher, year, edition, pages
Encyclopedia of Global Archaeology/Springer Verlag, 2016. p. 169-180
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9794
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing; Computer Aided Design
Identifiers
URN: urn:nbn:se:ltu:diva-32780DOI: 10.1007/978-3-319-42996-0_14ISI: 000386511900014Scopus ID: 84978818240Local ID: 760e2096-0641-45fd-a1f3-393a32dc5d6dISBN: 978-3-319-42995-3 (print)ISBN: 978-3-319-42996-0 (electronic)OAI: oai:DiVA.org:ltu-32780DiVA, id: diva2:1006014
Conference
Pacific-Asia Conference on Knowledge Discovery and Data Mining : 19/04/2016
Note

Validerad; 2016; Nivå 1; 2016-11-25 (andbra)

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-07-10Bibliographically approved

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Vasilakos, Athanasios

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Citation style
  • apa
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Output format
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