Multiscale Lempel-Ziv Complexity Research Articles

Laser Doppler Fluximetry in Cutaneous Vasculature: Methods for Data Analyses

Introduction: Acquisition of a deeper understanding of microvascular function across physiological and pathological conditions can be complicated by poor accessibility of the vascular networks and the necessary sophistication or intrusiveness of the equipment needed to acquire meaningful data. Laser Doppler fluximetry (LDF) provides a mechanism wherein investigators can readily acquire large amounts of data with minor inconvenience for the subject. However, beyond fairly basic analyses of erythrocyte perfusion (fluximetry) data within the cutaneous microcirculation (i.e., perfusion at rest and following imposed challenges), a deeper understanding of microvascular perfusion requires a more sophisticated approach that can be challenging for many investigators. Methods: This manuscript provides investigators with clear guidance for data acquisition from human subjects for full analysis of fluximetry data, including levels of perfusion, single- and multiscale Lempel-Ziv complexity (LZC) and sample entropy (SampEn), and wavelet-based analyses for the major physiological components of the signal. Representative data and responses are presented from a recruited cohort of healthy volunteers, and computer codes for full data analysis (MATLAB) are provided to facilitate efforts by interested investigators. Conclusion: It is anticipated that these materials can reduce the challenge to investigators integrating these approaches into their research programs and facilitate translational research in cardiovascular science.

Refined composite hierarchical multiscale Lempel-Ziv complexity: A quantitative diagnostic method of multi-feature fusion for rotating energy devices

Refined composite hierarchical multiscale Lempel-Ziv complexity: A quantitative diagnostic method of multi-feature fusion for rotating energy devices

Compound faults identification for rolling bearing based on variable step-size multiscale weighted Lempel-Ziv complexity

Due to the mutual coupling between sub-faults in the case of compound faults in rolling bearings, coupled with external noise, energy attenuation during information transmission, etc., the acquired fault information is usually very weak and complex. This greatly increases the difficulty of extracting features from bearing compound faults. To further enhance the accuracy of fault feature extraction and achieve precise identification of rolling bearing compound faults, this paper presents an innovative compound fault diagnosis method that integrates variable step-size multiscale weighted Lempel-Ziv complexity (VMW-LZC) and intrinsic time-scale decomposition (ITD). First, considering that the traditional Lempel-Ziv complexity (LZC) method can only extract single-dimensional fault information without thoroughly exploiting fault characteristics, we optimize the coarse-graining process of proper rotation components (PRCs) after ITD using a variable step-size multiscale strategy. Additionally, LZCs obtained from variable step-sizes in each scale are fused and reconstructed using a weighted method. Meanwhile, multiple variable step-size multiscale LZCs are combined into a new signal evaluation index, VMW-LZC. In addition, with the new signal evaluation index VMW-LZC, the optimal PRC which is best representative of fault characteristic information is chosen. Furthermore, frequency spectrum of autocorrelation function of optimal PRC is used to identify multiple faults of bearings. To exemplify the efficiency of presented method, a comparison has been made among the optimal PRC chosen by the methods with VMW-LZC, traditional LZC (T-LZC), multiscale LZC(M-LZC), and classical kurtosis as signal evaluation indexes. The result has indicated that fused signal evaluation index VMW-LZC can actualize more precise option of sensitive fault component signals and the proposed ITD-VMW-LZC method can do a more precise job in identifying a compound fault of bearings.

A novel method to identify the multiple faults of intermediate bearing based on multiscale Lempel–Ziv complexity enhancement

In order to solve the difficulty to correctly identify a compound failure in intermediate bearing of aircraft engine, the paper has brought forward the method of strengthening fault feature information of intermediate bearing with weighted multiscale Lempel–Ziv complexity parameter. The method works by combining multiscale Lempel–Ziv complexity parameter (M-LZC) with wavelet transform (WT) and singular value decomposition (SVD) and implements the feature extraction and identification of compound failure in intermediate bearing. Firstly, failure information from different frequency bands of casing vibration signals is separated and signals are denoised based on WT and SVD difference spectrum. Secondly, concerning that the larger Lempel–Ziv complexity (LZC) of signal is, the more abundant failure information in signal will be; meanwhile, so as to avoid “excessive coarse graining” caused by traditional binarization method, M-LZC values of component signals after denoising are used to describe the failure feature information included in component signals. Thirdly, to further strengthen the failure feature information of intermediate bearing, larger weight value will be endowed to the component signals containing more abundant failure feature information. Failure information is boosted by M-LZC weight coefficient of component signal. Finally, component signals of an enhanced feature are reconstructed and the spectrum of reconstructed signal is used to make precise identification of compound failure type of intermediate bearing in aircraft engine.

Multi-scale permutation Lempel-Ziv complexity and its application in feature extraction for Ship-radiated noise

Permutation Lempel-Ziv complexity (PLZC) is a recently proposed method for analyzing signal complexity. However, PLZC only characterizes the signal complexity from single scale and has certain limitations. In order to overcome these shortcomings and improve the performance of feature extraction for underwater acoustic signal, this paper introduced coarse graining operation, proposed the multi-scale permutation Lempel-Ziv complexity (MPLZC), and proposed an automatic hybrid multi-feature extraction method for ship-radiated noise signal (S-S) based on multi-scale Lempel-Ziv complexity (MLZC), multi-scale permutation entropy (MPE) and MPLZC. The results of simulation and realistic experiments show that MPLZC can better reflect the change of signal complexity in detecting the dynamic change of signals, and more effectively distinguish white noise, pink noise and blue noise than MPE and MLZC; compared with the three feature extraction methods based on MLZC, MPE and MPLZC respectively, the proposed method has the highest recognition rates of six S-Ss under the same number of features, and the recognition rate reaches 100% when the number of features is 5; the introduction of MPLZC significantly improves the performance for ship-radiated noise signal of the automatic hybrid multi-feature extraction method. It is indicated that the proposed method, as a new underwater acoustic technology, is valid in other underwater acoustic signals.

Feature extraction methods of ship-radiated noise: From single feature of multi-scale dispersion Lempel-Ziv complexity to mixed double features

Dispersion Lempel-Ziv complexity (DLZC) has been introduced into the field of underwater acoustic with great performance, but it only reflects complexity information from single-scale. To address this problem, we proposed multi-scale dispersion Lempel-Ziv complexity (MDLZC), which reflects the complexity of ship-radiated noise signals (SNSs) more comprehensively. Based on the proposed MDLZC, we put forward a single feature extraction method for SNSs, which denoted as 1-MDLZC; to enhance the recognition validity of 1-MDLZC, we came up with a double features extraction method based on MDLZC under two scale factors (SFs), which denoted as 2-MDLZC; to associate the complementarity of MDLZC and multi-scale dispersion entropy (MDE), we advanced mixed double features extraction method based on MDLZC and MDE under two SFs, which denoted as 1-MDLZC&1-MDE. The experimental results demonstrated that, 1-MDLZC has the highest recognition rate compared to single feature extraction method based on MDE and multi-scale Lempel-Ziv complexity (MLZC), which up to 97%; 2-MDLZC improves the recognition rate of 1-MDLZC by 2.75%; 1-MDLZC&1-MDE makes the recognition rate achieve 100% in many cases under double features.

Identification of rotor–stator rub-impact faults based on dual complexity parameters

For effective identification of rub-impact faults between rotor and stator of equipment, the paper has contributed the method integrating dual complexity parameters and variational mode decomposition (VMD). Firstly, to effectively separate the fault characteristics involved in signals, VMD algorithm was applied to decompose vibration signals and the component signals were obtained; secondly, taking account of the large difference of fault feature information involved in different component signals and in order to make options of sensitive fault component signals and reduce the loss of fault features, multi-scale Lempel–Ziv complexity and complexity parameter in Hjorth parameters were brought. Starting from 2 different perspectives of complexity evaluation, choose from the sensitive component signals containing more fault characteristics with these 2 complexity parameters; thirdly, signals were reconstructed based on selected sensitive component signals, and meanwhile, singular value difference spectrum algorithm was used to denoise reconstructed signals to further lessen the influence of noises; finally, the rub-impact fault between rotor and stator was identified by square demodulation spectrum (SDS) of denoised signal. The effectiveness of the proposed method has been proved by comparative analysis with other approaches as well as validation analysis of rub-impact fault signals in multiple situations.

Generalized Variable-Step Multiscale Lempel-Ziv Complexity: A Feature Extraction Tool for Bearing Fault Diagnosis

Feature extraction is a key step for fault diagnosis of rolling bearings based on vibration measurements. Lempel-Ziv complexity (LZC) has been widely applied to extracting fault features. However, LZC with single-scale analysis may result in the loss of fault information. Then, the multiscale LZC is proposed to uncover the multiscale features. Nevertheless, multiscale LZC would shorten the length of sequences, leading to inaccurate calculation results. Moreover, the mean operation during the coarse-grained process of the traditional multiscale analysis cannot reveal the dynamic changes of the original sequences, which may also result in the loss of potential information. Thus, this paper develops a feature extraction method called generalized variable-step multiscale LZC (GVSMLZC) for the vibration-based fault diagnosis of rolling bearings. GVSMLZC reveals more information and gains more robust complexity by improving the coarse-grained process. Moreover, GVSMLZC enhances the capability of mining dynamic characteristics by generalizing first-order moments to second-order moments. Based on the proposed GVSMLZC, fault type identification and fault detection of life-cycle data schemes are designed to realize the fault diagnosis of rolling bearings. Simulated and experimental results demonstrate that GVSMLZC outperforms multiscale LZC, variable-step multiscale LZC, and multiscale sample entropy in extracting bearing fault information.

Bearing fault severity assessment using variable-step multiscale fusion Lempel-Ziv complexity

Abstract Most research on bearing health condition monitoring is devoted to distinguish the defective condition from the healthy condition. In practice, the fault severity assessment is also critical for performing the prognostics and maintenance of bearings. Lempel-Ziv complexity (LZC) has been widely used for the bearing quantitative fault diagnosis. However, the original LZC extracts fault information only at the single scale and often fails to portray the fault features. Then, the multiscale LZC is proposed to more comprehensively extract the fault information. However, multiscale analysis would shorten the length of time series and lead to inaccurate calculation results as the scale factor increases. As such, this paper proposes a novel bearing fault severity assessment method using variable-step multiscale fusion Lempel-Ziv complexity (VSMFLZC) to facilitate the quantitative fault diagnosis of bearings. The variable step length strategy is developed in the proposed method to optimize the coarse-grained procedure. Then, Laplace score is applied to evaluate the features and weights at each scale to obtain the proposed VSMFLZC. By such a fusion algorithm, the sequence can be converted into a single but comprehensive evaluation indicator for the fault severity assessment. The experimental results indicate that the proposed method outperforms the original LZC and multiscale LZC, where the fault features can be more comprehensively extracted and the fault severity assessment of rolling bearing can be successfully realized.

Fault severity assessment for rotating machinery via improved Lempel–Ziv complexity based on variable-step multiscale analysis and equiprobable space partitioning

Fault severity assessment based on vibration measurements for rotating machinery is critical since it can reduce downtime and guarantee the reliability of the equipment. Lempel–Ziv complexity (LZC) has been widely used for the fault severity assessment. However, LZC is of a single-scale analysis and 0-1 encoding, which cannot fully explore the features of vibration signals measured from rotating machinery. This paper, thus, proposes an improved LZC based on the variable-step multiscale analysis (VSMA) and equiprobable space partitioning (ESP) strategies to fully explore features of vibrations of rotating machinery. The VSMA is proposed to overcome the drawback that the single-scale analysis fails to comprehensively uncover features and solve the problem that the traditional multiscale analysis significantly reduces the length of sequences. With the VSMA, a string of time series under different scales can be generated. The ESP is developed to transform the time series into symbolic series, with the capability of reserving the features of vibration signals and being more robust against noise, particularly for non-stationary signals. Then, the ESP based variable-step multiscale LZCs (i.e. ESP-VSMLZCs) are obtained. To fuse the obtained ESP-VSMLZCs and obtain a comprehensive indicator for fault severity assessment, Laplacian score weighting is adopted. As such, a single ESP based variable-step multiscale fusion LZC indicator can be obtained. The proposed indicator is verified by simulated data from a bearing dynamic model and experimental data measured from rotating machinery.

Application of Generalized Composite Multiscale Lempel–Ziv Complexity in Identifying Wind Turbine Gearbox Faults

Wind turbine gearboxes operate in harsh environments; therefore, the resulting gear vibration signal has characteristics of strong nonlinearity, is non-stationary, and has a low signal-to-noise ratio, which indicates that it is difficult to identify wind turbine gearbox faults effectively by the traditional methods. To solve this problem, this paper proposes a new fault diagnosis method for wind turbine gearboxes based on generalized composite multiscale Lempel–Ziv complexity (GCMLZC). Within the proposed method, an effective technique named multiscale morphological-hat convolution operator (MHCO) is firstly presented to remove the noise interference information of the original gear vibration signal. Then, the GCMLZC of the filtered signal was calculated to extract gear fault features. Finally, the extracted fault features were input into softmax classifier for automatically identifying different health conditions of wind turbine gearboxes. The effectiveness of the proposed method was validated by the experimental and engineering data analysis. The results of the analysis indicate that the proposed method can identify accurately different gear health conditions. Moreover, the identification accuracy of the proposed method is higher than that of traditional multiscale Lempel–Ziv complexity (MLZC) and several representative multiscale entropies (e.g., multiscale dispersion entropy (MDE), multiscale permutation entropy (MPE) and multiscale sample entropy (MSE)).

Intelligent Fault Diagnosis of Rotating Machinery Using Hierarchical Lempel-Ziv Complexity

The health condition monitoring of rotating machinery can avoid the disastrous failure and guarantee the safe operation. The vibration-based fault diagnosis shows the most attractive character for fault diagnosis of rotating machinery (FDRM). Recently, Lempel-Ziv complexity (LZC) has been investigated as an effective tool for FDRM. However, the LZC only performs single-scale analysis, which is not suitable to extract the fault features embedded in vibrational signal over multiple scales. In this paper, a novel complexity analysis algorithm, called hierarchical Lempel-Ziv complexity (HLZC), was developed to extract the fault characteristics of rotating machinery. The proposed HLZC method considers the fault information hidden in both low-frequency and high-frequency components, resulting in a more accurate fault feature extraction. The superiority of the proposed HLZC method in detecting the periodical impulses was validated by using simulated signals. Meanwhile, two experimental signals were utilized to prove the effectiveness of the proposed HLZC method in extracting fault information. Results show that the proposed HLZC method had the best diagnosing performance compared with LZC and multi-scale Lempel-Ziv complexity methods.

Intelligent fault identification of rotary machinery using refined composite multi-scale Lempel–Ziv complexity

Fault diagnosis of rotary machinery plays a significant role in the prognostic and health management system, which aims to identify the root causes of system failures and provide effective information for prognostics and maintenance. Recently, Lempel–Ziv complexity (LZC) method has been employed for fault diagnosis of rotary machinery. However, one actual problem is that LZC fails to account for the multiscale information inherent in measured vibration signals. We first introduce a method to compute the multi-scale LZC for a signal. However, the variance of LZC values becomes larger as the scale factor increases. To solve this actual problem, this paper proposes refined composite multi-scale Lempel-Ziv complexity (RCMLZC) to estimate the complexity. We find that the proposed RCMLZC method consistently yields better performance when analyzing three simulated noisy and impulsive signals. Based on RCMLZC, a novel intelligent fault diagnosis method is designed to recognize various fault types of rotating machinery. Comparative experiments are performed to confirm the effectiveness of proposed method including single fault and compound fault working conditions. Experimental results indicate that RCMLZC is more accurate than multi-scale LZC, multi-scale entropy, and LZC in extracting fault features from vibration signal and that RCMLZC performs best to recognize the various fault types of rotary machinery.

EEG Multiscale Complexity in Schizophrenia During Picture Naming.

Introduction: Patients with schizophrenia show cognitive deficits that are evident both behaviourally and with EEG recordings. Recent studies have suggested that non-linear analyses of EEG might more adequately reflect the complex, irregular, non-stationary behavior of neural processes than more traditional ERP measures. Non-linear analyses have been mainly applied to EEGs from patients at rest, whereas differences in complexity might be more evident during task performance.Objective: We aimed to investigate changes in non-linear brain dynamics of patients with schizophrenia during cognitive processing.Method: 18 patients and 17 matched healthy controls were asked to name pictures. EEG data were collected at rest and while they were performing a naming task. EEGs were analyzed with the classical Lempel-Ziv Complexity (LZC) and with the Multiscale LZC. Electrodes were grouped in seven regions of interest (ROI).Results: As expected, controls had fewer naming errors than patients. Regarding EEG complexity, the interaction between Group, Task and ROI indicated that patients showed higher complexity values in right frontal regions only at rest, where no differences in complexity between patients and controls were found during the naming task. EEG complexity increased from rest to task in controls in left temporal-parietal regions, while no changes from rest to task were observed in patients. Finally, differences in complexity between patients and controls depended on the frequency bands: higher values of complexity in patients at rest were only observed in fast bands, indicating greater heterogeneity in patients in local dynamics of neuronal assemblies.Conclusion: Consistent with previous studies, schizophrenic patients showed higher complexity than controls in frontal regions at rest. Interestingly, we found different modulations of brain complexity during a simple cognitive task between patients and controls. These data can be interpreted as indicating schizophrenia-related failures to adapt brain functioning to the task, which is reflected in poorer behavioral performance.Highlights: - We measured classical and multiscale Lempel-Ziv Complexity (LZCN and MLZC) of the EEG signal of patients with schizophrenia and controls at rest and while performing a cognitive task. - We found that patients and controls showed a different pattern of brain complexity depending on their cognitive state (at rest or under cognitive challenge). - Our results illustrate the value of the MLZC in the characterization of the pattern of brain complexity in schizophrenia on function of frequency bands. - Nonlinear methodologies of EEG analysis can help to characterize brain dysfunction in schizophrenia.

Fault Diagnosis of Rolling Element Bearing Using Multi-Scale Lempel-Ziv Complexity and Mahalanobis Distance Criterion

A new fault diagnosis technique for rolling element bearing using multi-scale Lempel-Ziv complexity (LZC) and Mahalanobis distance (MD) criterion is proposed in this study. A multi-scale coarse-graining process is used to extract fault features for various bearing fault conditions to overcome the limitation of the single stage coarse-graining process in the LZC algorithm. This is followed by the application of MD criterion to calculate the accuracy rate of LZC at different scales, and the best scale corresponding to the maximum accuracy rate is identified for fault pattern recognition. A comparison analysis with Euclidean distance (ED) criterion is also presented to verify the superiority of the proposed method. The result confirms that the fault diagnosis technique using a multi-scale LZC and MD criterion is more effective in distinguishing various fault conditions of rolling element bearings.

Generalized multiscale Lempel–Ziv complexity of cyclic alternating pattern during sleep

Increasing evidences show that multiscale complexity measure is an intuitive and effective measure in quantifying various physical and physiological states. In this study, we demonstrate that the classical algorithm of multiscale Lempel–Ziv complexity (multiscale LZC or MLZ) has a critical limitation in neglecting rapid rhythms in complex systems. To this end, simulations added with different levels of white noise are designed to examine whether or not MLZ calculation neglects the effects of high-frequency noise. In addition, an algorithm by obtaining coarse-grained multiscale LZC, so-called generalized multiscale LZC (gMLZ), is proposed to yield a spectrum of complexity. A series of simulated non-stationary signals are generated for comparing the performances between MLZ and gMLZ. Besides, cyclic alternating pattern (CAP), characterized by the excessive synchronization of neuronal activity, has been associated with its power and physiological states. To understand how the synchronization of neuronal activities in different phase-A subtypes in exerting an influence over its power and complexity, we analyze the gMLZ of the real CAP database and compare it to its power spectra as well as modified multiscale entropy (MMSE), which is one of the most well-known multiscale complexity-based measures. The novel algorithm reveals that the evaluated complexities in different phase-A subtypes are inversely related to both the power and excessive synchronization in different timescales in general. The impact of frequencies, sleep stages and pathophysiological conditions on these two complexity measures is also examined. The discerning abilities of different phase-A subtypes using coarse-grained complexity measures (gMLZ and MMSE) are more consistent than power across different time scales. Our approach makes up a deficiency in handling with high-frequency oscillations and enables us to examine complexities of nonlinear systems in a wide-range of timescales.

P383 Multiscale lempel-ZIV complexity in schizophrenia at rest and while performing a naming task

Schizophrenic patients show cognitive deficits which are evident in EEG measures. Recent studies have suggested that non-linear EEG indexes might more adequately reflect the complex, irregular, nonstationary behavior of neural processes in schizophrenia than more traditional ERP measures (Fernandez et al., 2011). So far, non-linear analyses have mainly been performed to EEGs from patients at rest, whereas differences in complexity might be more evident during task performance. In addition, most studies have explored complexity at a single time scale. Objective We aimed to investigate changes in nonlinear multiscale brain dynamics in schizophrenic patients during cognitive processing. Method 18 patients and 17 matched healthy controls were asked to name pictures. EEG data were collected at rest and while they were performing the task. EEGs were analyzed with the classical and the multiscale Lempel-Ziv Complexity. Electrodes were grouped in seven regions of interest (ROI). Results Controls had fewer naming errors than patients. More importantly, we found greater complexity values for patients than for controls in right frontal regions, at rest. The interaction between Group, Task and ROI indicated that differences between patients and controls were more evident when performing the task, with increments in brain complexity in left temporal parietal regions, only in the control group. Conclusions We found different modulations of brain complexity during a simple cognitive task between patients and controls. These data can be interpreted as a failure of patients to adapt their brain functioning to the task, which reflect in poorer behavioral performance.

EEG analysis via multiscale Lempel-Ziv complexity for seizure detection.

Robust seizure detection and seizure prediction continues to be a challenge. Lempel-Ziv Complexity (LZC) is one of the features that has shown to be relevant in seizure detection. Recent work has shown that augmenting LZC can be beneficial to emphasize variations in amplitude or frequency when analyzing biomedical signals. In this paper, we present a first look into evaluating the feasibility of using a recently proposed feature stemmed from LZC, namely the Multiscale Lempel-Ziv Complexity (MLZC) for seizure detection. MLZC does not allow the high-frequency signal components to be overwhelmed by the low frequency signal components when calculating complexity values. We compare MLZC and LZC for identifying seizures for three cases and show MLZC can provide a clear separation between non-ictal and ictal periods for all three cases using a single threshold over 7 recordings and 7 seizures per patient, whereas LZC provided such a clear separation for only one of the patients.

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 and multiscale Lempel-Ziv complexity in depression.

There is a high demand for objective indicators in diagnosis of depression as diagnosis of depression is still based on psychiatrist's subjective judgment. A nonlinear method Lempel Ziv Complexity (LZC) has been previously successfully used for detection of neuronal or mental disorders based on electroencephalographic (EEG) signals. However, the method overlooks the high frequency content of EEG signals. Therefore, this study is aimed to find out whether the use of Multiscale Lempel Ziv Complexity (MLZC), considering also high frequencies, could overcome the limitations of LZC and better differentiate depression. In current study the EEG recordings were carried out on the groups of depressive and healthy subjects of 11 volunteers each. The LZC and MLZC were calculated on resting EEG signals in eyes open condition from 30 channels at a length of 2 minutes. The results revealed the incapability of traditional LZC to differentiate depressive subjects from healthy controls in eyes open condition, while MLZC differentiated two groups in numerous channels at different frequencies, giving the highest classification accuracy in channel F3 (86 %) at frequencies 9 and 15.5 Hz. The results indicate that the high frequency information, which is lost in calculation of traditional LZC, has a great value in differentiating between depressive and control groups.