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          Abstract

          In this Communication, we express our reservations on some aspects of the interpretation of the Lempel-Ziv Complexity measure (LZ) by Mateo et al. in "Interpretation of the Lempel-Ziv complexity measure in the context of biomedical signal analysis," IEEE Trans. Biomed. Eng., vol. 53, no. 11, pp. 2282-2288, Nov. 2006. In particular, we comment on the dependence of the LZ complexity measure on number of harmonics, frequency content and amplitude modulation. We disagree with the following statements made by Mateo et al. 1. "LZ is not sensitive to the number of harmonics in periodic signals." 2. "LZ increases as the frequency of a sinusoid increases." 3. "Amplitude modulation of a signal doesnot result in an increase in LZ." We show the dependence of LZ complexity measure on harmonics and amplitude modulation by using a modified version of the synthetic signal that has been used in the original paper. Also, the second statement is a generic statement which is not entirely true. This is true only in the low frequency regime and definitely not true in moderate and high frequency regimes.

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          EEG complexity as a measure of depth of anesthesia for patients.

          A new approach for quantifying the relationship between brain activity patterns and depth of anesthesia (DOA) is presented by analyzing the spatio-temporal patterns in the electroencephalogram (EEG) using Lempel-Ziv complexity analysis. Twenty-seven patients undergoing vascular surgery were studied under general anesthesia with sevoflurane, isoflurane, propofol, or desflurane. The EEG was recorded continuously during the procedure and patients' anesthesia states were assessed according to the responsiveness component of the observer's assessment of alertness/sedation (OAA/S) score. An OAA/S score of zero or one was considered asleep and two or greater was considered awake. Complexity of the EEG was quantitatively estimated by the measure C(n), whose performance in discriminating awake and asleep states was analyzed by statistics for different anesthetic techniques and different patient populations. Compared with other measures, such as approximate entropy, spectral entropy, and median frequency, C(n) not only demonstrates better performance (93% accuracy) across all of the patients, but also is an easier algorithm to implement for real-time use. The study shows that C(n) is a very useful and promising EEG-derived parameter for characterizing the (DOA) under clinical situations.
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            Interpretation of the Lempel-Ziv complexity measure in the context of biomedical signal analysis.

            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 signals.
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              Interpretation of approximate entropy: analysis of intracranial pressure approximate entropy during acute intracranial hypertension.

              We studied changes in intracranial pressure (ICP) complexity, estimated by the approximate entropy (ApEn) of the ICP signal, as subjects progressed from a state of normal ICP ( 25 mmHg for < or = 5 min). We hypothesized that the measures of intracranial pressure (ICP) complexity and irregularity would decrease during acute elevations in ICP. To test this hypothesis we studied ICP spikes in pediatric subjects with severe traumatic brain injury (TBI). We conclude that decreased complexity of ICP coincides with episodes of intracranial hypertension (ICH) in TBI. This suggests that the complex regulatory mechanisms that govern intracranial pressure are disrupted during acute rises in ICP. Furthermore, we carried out a series of experiments where ApEn was used to analyze synthetic signals of different characteristics with the objective of gaining a better understanding of ApEn itself, especially its interpretation in biomedical signal analysis.
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                Author and article information

                Journal
                01 August 2013
                2013-08-05
                Article
                1308.0130
                67f2825a-d05a-46ca-b1bc-fec2d0ba8b05

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                Submitted for review to IEEE Transactions on Biomedical Engineering
                nlin.CD physics.data-an

                Mathematical & Computational physics,Nonlinear & Complex systems
                Mathematical & Computational physics, Nonlinear & Complex systems

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