Interpretation of the Lempel-Ziv Complexity Measure in the Context of Biomedical Signal Analysis
Top Cited Papers
- 16 October 2006
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Biomedical Engineering
- Vol. 53 (11) , 2282-2288
- https://doi.org/10.1109/tbme.2006.883696
Abstract
Lempel-Ziv complexity (LZ) and derived LZ algorithms have been extensively used to solve information theoretic problems such as coding and lossless data compression. In recent years, LZ has been widely used in biomedical applications to estimate the complexity of discrete-time signals. Despite its popularity as a complexity measure for biosignal analysis, the question of LZ interpretability and its relationship to other signal parameters and to other metrics has not been previously addressed. We have carried out an investigation aimed at gaining a better understanding of the LZ complexity itself, especially regarding its interpretability as a biomedical signal analysis technique. Our results indicate that LZ is particularly useful as a scalar metric to estimate the bandwidth of random processes and the harmonic variability in quasi-periodic signalsKeywords
This publication has 41 references indexed in Scilit:
- Analysis of EEG background activity in Alzheimer's disease patients with Lempel–Ziv complexity and central tendency measureMedical Engineering & Physics, 2006
- Quantifying physiological data with Lempel-Ziv complexity-certain issuesIEEE Transactions on Biomedical Engineering, 2002
- Derived fuzzy knowledge model for estimating the depth of anesthesiaIEEE Transactions on Biomedical Engineering, 2001
- EEG complexity as a measure of depth of anesthesia for patientsIEEE Transactions on Biomedical Engineering, 2001
- Complexity measure and complexity rate information based detection of ventricular tachycardia and fibrillationMedical & Biological Engineering & Computing, 2000
- Detecting ventricular tachycardia and fibrillation by complexity measureIEEE Transactions on Biomedical Engineering, 1999
- Predicting movement during anaesthesia by complexity analysis of electroencephalograms.Medical & Biological Engineering & Computing, 1999
- Estimating regularity in epileptic seizure time-series dataIEEE Engineering in Medicine and Biology Magazine, 1998
- New approach to studies on ECG dynamics: extraction and analyses of QRS complex irregularity time series.Medical & Biological Engineering & Computing, 1997
- Information transmission in human cerebral cortexPhysica D: Nonlinear Phenomena, 1997