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 timeseries 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.