We propose an integrated approach based on uniform quantization over a small number of levels for the evaluation and characterization of complexity of a process. This approach integrates information-domain analysis based on entropy rate, local nonlinear prediction, and pattern classification based on symbolic analysis. Normalized and non-normalized indexes quantifying complexity over short data sequences (300 samples) are derived. This approach provides a rule for deciding the optimal length of the patterns that may be worth considering and some suggestions about possible strategies to group patterns into a smaller number of families. The approach is applied to 24h Holter recordings of heart period variability derived from 12 normal (NO) subjects and 13 heart failure (HF) patients. We found that: (i) in NO subjects the normalized indexes suggest a larger complexity during the nighttime than during the daytime; (ii) this difference may be lost if non-normalized indexes are utilized; (iii) the circadian pattern in the normalized indexes is lost in HF patients; (iv) in HF patients the loss of the day-night variation in the normalized indexes is related to a tendency of complexity to increase during the daytime and to decrease during the nighttime; (v) the most likely length L of the most informative patterns ranges from 2 to 4; (vi) in NO subjects classification of patterns with L=3 indicates that stable patterns (i.e., those with no variations) are more present during the daytime, while highly variable patterns (i.e., those with two unlike variations) are more frequent during the nighttime; (vii) during the daytime in HF patients, the percentage of highly variable patterns increases with respect to NO subjects, while during the nighttime, the percentage of patterns with one or two like variations decreases.

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