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Updated: May 10, 2026

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
Published on: June 15, 2018
Entropy and compression: two measures of complexity.
Teresa Henriques1, Hernâni Gonçalves, Luís Antunes
1Health Information and Decision Sciences Department, Faculty of Medicine, University of Porto, Porto, Portugal; Instituto de Telecomunicações, Porto, Portugal; Centre for Research in Health Technologies and Information Systems - CINTESIS, Porto, Portugal.
Shannon entropy and Kolmogorov complexity approximations effectively distinguished fetuses at risk of hypoxia from healthy ones using fetal heart rate monitoring. Lower umbilical artery blood pH correlated with reduced entropy and compression indices, suggesting combined measures improve fetal outcome prediction.
Area of Science:
- Perinatal medicine and biomedical signal processing.
- Application of information theory and algorithmic complexity in healthcare.
Background:
- Fetal heart rate (FHR) monitoring is crucial for predicting fetal outcomes.
- Traditional complexity measures for FHR are not yet standard clinical practice.
- Shannon entropy and Kolmogorov complexity offer distinct approaches to characterizing complexity.
Purpose of the Study:
- To compare the utility of Shannon entropy and Kolmogorov complexity approximations in analyzing FHR.
- To demonstrate that algorithmic complexity measures can be as effective as probabilistic entropy measures for FHR analysis.
- To assess the potential of these complexity measures in identifying fetuses at risk.
Main Methods:
- Applied approximate and sample entropy (probabilistic) and paq8l and bzip2 compressors (algorithmic) to FHR tracings.
- Utilized a dataset of delivered fetuses with varying umbilical artery blood (UAB) pH levels.
- Analyzed complexity indices on 5- and 10-minute segments from the last hour of labor.
Main Results:
- Both entropy and compression indices successfully differentiated fetuses at risk of hypoxia from healthy ones.
- Lower UAB pH values were associated with significantly lower entropy and compression indices.
- These differences were more pronounced in the final segments of the monitored period.
Conclusions:
- Conceptually different complexity measures (entropy and compression) capture distinct features of FHR.
- Combining these measures offers an improved approach to characterizing pathophysiological states in fetuses.
- The findings support the integration of both entropy and compression analysis for enhanced fetal monitoring.
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