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.
The original version of this chapter was revised: The spelling of the fourth author’s name was corrected. The erratum to this chapter is available at DOI: 10.1007/978-3-319-42996-0_24
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-42996-0_24
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Acknowledgments
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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Gong, X., Fong, S., Si, YW., Biuk-Aghai, R.P., Wong, R.K., Vasilakos, A.V. (2016). Normalized Cross-Match: Pattern Discovery Algorithm from Biofeedback Signals. In: Cao, H., Li, J., Wang, R. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9794. Springer, Cham. https://doi.org/10.1007/978-3-319-42996-0_14
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