Lempel-Ziv Complexity Analysis Research Articles

Sleep-Apnea Detection with the Lempel-Ziv Complexity Analysis of the Electrocardiogram and Respiratory Signals

Sleep-Apnea Detection with the Lempel-Ziv Complexity Analysis of the Electrocardiogram and Respiratory Signals

Permutation Lempel-Ziv 복잡성 척도를 적용한 근 피로도 추정

A new approach for quantifying muscle fatigue is presented by analyzing the deterministic complexity in the surface electromyogram(SEMG) using Permutation Lempel-Ziv(PLZ) complexity measure. The PLZ complexity measure provide a metric for the number of distinct deterministic patterns in signals by combining permutation patterns and Lempel-Ziv complexity analysis. For muscle fatigue experiment by using PLZ complexity measure, surface EMG signals from eleven subjects were recorded in biceps brachii muscle with constant isometric 20, 50, and 80%MVC contractions. The PLZ complexity measure is evaluated with the performance of muscle fatigue estimation for the SEMG signals compared to the performance of previous reported LZ complexity measure. This PLZ complexity measure has advantages that is computationally easy to compute and does not require any assumptions about the stationarity of the SEMG signal.

Leakage detection method of natural gas pipeline combining improved variational mode decomposition and Lempel–Ziv complexity analysis

With the continuous development of pipeline transportation industry, pipeline leakage often occurs, posing a great threat to people’s lives and property safety. In order to improve the detection accuracy of natural gas pipeline leakage, a pipeline leakage detection method based on improved variational mode decomposition algorithm and Lempel–Ziv complexity analysis is proposed. In this work, the normalized mutual information is used to determine the decomposition level K of variational mode decomposition, and the Lempel–Ziv complexity analysis algorithm is used to extract pipeline signal feature. The results show that the proposed leakage detection method has higher classification accuracy than other methods, which verifies the effectiveness of this method in the process of pipeline leakage detection.

COMPLEXITY AND MULTIFRACTAL OF VOLATILITY DURATION FOR AGENT-BASED FINANCIAL DYNAMICS AND REAL MARKETS

A random agent-based financial model is developed and investigated by the finite-range multitype contact dynamic system, in an attempt to reproduce and study the dynamics of financial markets. And an analysis method of detecting duration and intensity relationship in volatility series is introduced, called the volatility duration analysis. Then the auto-correlation analysis suggests that there exists evident volatility clustering feature in absolute volatility durations for the simulation data and the real data. Besides, the Lempel–Ziv complexity analysis is applied to study the complexity of the returns, the corresponding absolute returns and the volatility duration returns, which can reflect the fluctuation behaviors, the volatility behaviors and the volatility duration behaviors. At last, the multifractal phenomena of volatility durations of returns are comparatively studied for Shanghai Composite Index and the proposed model by multifractal detrended fluctuation analysis.

Multiple characteristics analysis of Alzheimer's electroencephalogram by power spectral density and Lempel-Ziv complexity.

To investigate the electroencephalograph (EEG) background activity in patients with Alzheimer's disease (AD), power spectrum density (PSD) and Lempel-Ziv (LZ) complexity analysis are proposed to extract multiple effective features of EEG signals from AD patients and further applied to distinguish AD patients from the normal controls. Spectral analysis based on autoregressive Burg method is first used to quantify the power distribution of EEG series in the frequency domain. Compared with the control group, the relative PSD of AD group is significantly higher in the theta frequency band while lower in the alpha frequency bands. In order to explore the nonlinear information, Lempel-Ziv complexity (LZC) and multi-scale LZC is further applied to all electrodes for the four frequency bands. Analysis results demonstrate that the group difference is significant in the alpha frequency band by LZC and multi-scale LZC analysis. However, the group difference of multi-scale LZC is much more remarkable, manifesting as more channels undergo notable changes, particularly in electrodes O1 and O2 in the occipital area. Moreover, the multi-scale LZC value provided a better classification between the two groups with an accuracy of 85.7%. In addition, we combine both features of the relative PSD and multi-scale LZC to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature, reaching 91.4%. The obtained results show that analysis of PSD and multi-scale LZC can be taken as a potential comprehensive measure to distinguish AD patients from the normal controls, which may benefit our understanding of the disease.

Lempel-Ziv complexity changes and physiological mental fatigue level during different mental fatigue state with spontaneous EEG

The objective was to study changes in EEG time-domain Kolmogorov complexity under different mental fatigue state and to evaluate mental fatigue using Lempel-Ziv complexity analysis of spontaneous EEG in healthy human subjects. EEG data for healthy subjects were acquired using a net of 2 electrodes (Fp1 and Fp2) at PM 4:00, AM 12:00 and AM 3:00 in the 24 hours sleep-deprived mental fatigue experiments. It was presented that initial results for eight subjects examined in three different mental fa-tigue state with 2-channel EEG time-domain Lempel-Ziv complexity computations. It was found that the value of mean Lempel-Ziv com-plexity corresponding to a special mental state fluctuates within the special range and the value of C(n) increases with mental fatigue increasing for the total frequency spectrum. The result in-dicates that the value of C(n) is strongly cor-relative with the mental fatigue state. These re-sults suggest that it may be possible to nonin-vasively differentiate different mental fatigue level according to the value of C(n) for particular mental state from scalp spontaneous EEG data. This method may be useful in further research and efforts to evaluate mental fatigue level ob-jectively. It may also provide a basis for the study of effects of mental fatigue on central neural system.

Abnormal EEG complexity in patients with schizophrenia and depression

Objective Schizophrenics are usually unable to perform well on cognitive tasks due to disturbances in cortical information processing that are observable as abnormalities in electroencephalogram (EEG) signals. However, whether such cortical disturbances can be assessed by quantitative EEG analysis remains unclear. The purpose of this study was to characterize EEG disturbances, using the Lempel–Ziv complexity (LZC), in the subjects with schizophrenia at rest or while performing mental arithmetic tasks. The results were compared to those from the subjects with depression and with healthy controls. Methods The subjects included 62 schizophrenia patients, 48 depression patients and 26 age-matched healthy controls. EEG was recorded under two conditions: (i) resting with eyes closed, and (ii) a mentally active condition wherein the subjects were asked to subtract 7 from 100 iteratively with their eyes closed. EEG signals were analyzed by LZC and conventional spectral methods. Results In all the groups, LZC of EEG decreased during the mental arithmetic compared with those under the resting conditions. Both the schizophrenia and the depression groups had a higher LZC ( p < 0.05) than the controls. Also, the schizophrenia group had a lower LZC ( p < 0.05) than the depression group during the mental arithmetic task as well as during the resting state. Significant differences in LZC, at some symmetrically located loci (FP1/FP2, F7/F8), between the two hemispheres were found in all the patient groups only during the arithmetic task. Conclusions Compared with conventional spectral analysis, LZC was more sensitive to both the power spectrum and the temporal amplitude distribution. LZC was associated with the ability to attend to the task and adapt the information processing system to the cognitive challenge. Thus, it would be useful in studying the disturbances in the cortical information processing patients with depression or schizophrenia. Significance LZC of EEG is associated with mental activity. Thus, LZC analysis can be an important tool in understanding the pathophysiology of schizophrenia and depression in future studies.

Arrhythmic Pulses Detection Using Lempel-Ziv Complexity Analysis

Computerized pulse analysis based on traditional Chinese medicine (TCM) is relatively new in the field of automatic physiological signal analysis and diagnosis. Considerable researches have been done on the automatic classification of pulse patterns according to their features of position and shape, but because arrhythmic pulses are difficult to identify, until now none has been done to automatically identify pulses by their rhythms. This paper proposes a novel approach to the detection of arrhythmic pulses using the Lempel-Ziv complexity analysis. Four parameters, one lemma, and two rules, which are the results of heuristic approach, are presented. This approach is applied on 140 clinic pulses for detecting seven pulse patterns, not only achieving a recognition accuracy of 97.1% as assessed by experts in TCM, but also correctly extracting the periodical unit of the intermittent pulse.

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.