Normalized Cross-Match: Pattern Discovery Algorithm from Biofeedback Signals

  • Xueyuan GongEmail author
  • Simon Fong
  • Yain-Whar Si
  • Robert P. Biuk-Aghai
  • Raymond K. Wong
  • Athanasios V. Vasilakos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9794)


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.


Pattern discovery CrossMatch NCM Time-series streams 



The authors are thankful for the financial supports by the Macao Science and Technology Development Fund under the research project (No. 126/2014/A3), titled A Scalable Data Stream Mining Methodology: Stream-based Holistic Analytics and Reasoning in Parallel, by the University of Macau and the Macau SAR government.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Xueyuan Gong
    • 1
    Email author
  • Simon Fong
    • 1
  • Yain-Whar Si
    • 1
  • Robert P. Biuk-Aghai
    • 1
  • Raymond K. Wong
    • 2
  • Athanasios V. Vasilakos
    • 3
  1. 1.Department of Computer and Information ScienceUniversity of MacauMacauChina
  2. 2.School of Computer Science and EngineeringUniversity of New South WalesSydneyAustralia
  3. 3.Department of Computer Science, Electrical and Space EngineeringLulea University of TechnologyLuleaSweden

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