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1.	Gong, Xueyuan, et al. (author) Normalized Cross-Match : Pattern Discovery Algorithm from Biofeedback Signals 2016 In: Trends and Applications in Knowledge Discovery and Data Mining. - Cham : Encyclopedia of Global Archaeology/Springer Verlag. - 9783319429953 - 9783319429960 ; , s. 169-180 Conference paper (peer-reviewed)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

Gong, Xueyuan, et al. (author)
Normalized Cross-Match : Pattern Discovery Algorithm from Biofeedback Signals
2016
In: Trends and Applications in Knowledge Discovery and Data Mining. - Cham : Encyclopedia of Global Archaeology/Springer Verlag. - 9783319429953 - 9783319429960 ; , s. 169-180
Conference paper (peer-reviewed)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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