A permutation Lempel-Ziv complexity measure for EEG analysis

Bai, Yang; Liang, Zhenhu; Li, Xiaoli

doi:10.1016/j.bspc.2015.04.002

Cited by 79 publications

(75 citation statements)

References 43 publications

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“…Additionally, if the time intervals between samples are large, then only slow changes can be taken into account. There is evidence [34][35][36]51,55] showing that most information can be extracted using a time delay value if l = 1. Therefore, this resulted in the use of l = 1 in this study [55].…”

Section: Evaluation Of Ideal Input Parametersmentioning

confidence: 99%

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Permutation Entropy for the Characterisation of Brain Activity Recorded with Magnetoencephalograms in Healthy Ageing

Shumbayawonda

Fernández

Hughes

et al. 2017

Entropy

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Abstract:The characterisation of healthy ageing of the brain could help create a fingerprint of normal ageing that might assist in the early diagnosis of neurodegenerative conditions. This study examined changes in resting state magnetoencephalogram (MEG) permutation entropy due to age and gender in a sample of 220 healthy participants (98 males and 122 females, ages ranging between 7 and 84). Entropy was quantified using normalised permutation entropy and modified permutation entropy, with an embedding dimension of 5 and a lag of 1 as the input parameters for both algorithms. Effects of age were observed over the five regions of the brain, i.e., anterior, central, posterior, and left and right lateral, with the anterior and central regions containing the highest permutation entropy. Statistically significant differences due to age were observed in the different brain regions for both genders, with the evolutions described using the fitting of polynomial regressions. Nevertheless, no significant differences between the genders were observed across all ages. These results suggest that the evolution of entropy in the background brain activity, quantified with permutation entropy algorithms, might be considered an alternative illustration of a 'nominal' physiological rhythm.

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Section: Evaluation Of Ideal Input Parametersmentioning

confidence: 99%

“…Both the nmPE and nPE algorithms as highlighted by Bandt and Pompe [9], Bai and Li [35] and Bain et al [36] rely on the identification of the most ideal input parameters of embedded dimension and time delay (lag) i.e., m and l respectively.…”

Section: Evaluation Of Ideal Input Parametersmentioning

confidence: 99%

Permutation Entropy for the Characterisation of Brain Activity Recorded with Magnetoencephalograms in Healthy Ageing

Shumbayawonda

Fernández

Hughes

et al. 2017

Entropy

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“…PLZC is a complexity measure combining ordinal patterns and the LZC algorithm to evaluate changes in the dynamics of the EEG [28]. In this method, the order of the patterns (i.e., permutation vectors known as "motifs") is used in the symbolisation process of the time series.…”

Section: Permutation Lempel-ziv Complexitymentioning

confidence: 99%

“…This is especially the case when a subset of a time-series has a large amplitude: some fast-frequency components within this subset maybe be lost due to same symbol being assigned to them, hence some aspects may be overlooked [29,30]. All the aforementioned features of PLZC suggest that it is useful to analyse any time-series without a priori information on how the time-series are generated and also provides an advantage in computation performance [28].…”

Section: Introductionmentioning

confidence: 99%

“…Numerous algorithms including traditional non-linear analysis methods, such as correlation dimension [18,19], Lyapunov exponent [20,21] or entropy [22][23][24] and artificial neural networks analyses [25,26], have been used to investigate the inherently non-linear nature of the EEG signal, where brain dynamics change due to different physiological states, pathologies, and manipulations [27]. Permutation Lempel-Ziv complexity (PLZC) is a novel symbolic dynamics analysis method which combines using the order of the data points in the symbolisation of the signal and measuring the emergence of new patterns within this new symbolised time series [28]. PLZC may be particularly useful in analysing biomedical signals as no assumption about the probability distribution of the data is necessary.…”

Section: Introductionmentioning

confidence: 99%

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Characterisation of the Effects of Sleep Deprivation on the Electroencephalogram Using Permutation Lempel–Ziv Complexity, a Non-Linear Analysis Tool

Tosun

Abásolo

Stenson

et al. 2017

Entropy

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Specific patterns of brain activity during sleep and waking are recorded in the electroencephalogram (EEG). Time-frequency analysis methods have been widely used to analyse the EEG and identified characteristic oscillations for each vigilance state (VS), i.e., wakefulness, rapid-eye movement (REM) and non-rapid-eye movement (NREM) sleep. However, other aspects such as change of patterns associated with brain dynamics may not be captured unless a non-linear-based analysis method is used. In this pilot study, Permutation Lempel-Ziv complexity (PLZC), a novel symbolic dynamics analysis method, was used to characterise the changes in the EEG in sleep and wakefulness during baseline and recovery from sleep deprivation (SD). The results obtained with PLZC were contrasted with a related non-linear method, Lempel-Ziv complexity (LZC). Both measure the emergence of new patterns. However, LZC is dependent on the absolute amplitude of the EEG, while PLZC is only dependent on the relative amplitude due to symbolisation procedure and thus, more resistant to noise. We showed that PLZC discriminates activated brain states associated with wakefulness and REM sleep, which both displayed higher complexity, compared to NREM sleep. Additionally, significantly lower PLZC values were measured in NREM sleep during the recovery period following SD compared to baseline, suggesting a reduced emergence of new activity patterns in the EEG. These findings were validated using PLZC on surrogate data. By contrast, LZC was merely reflecting changes in the spectral composition of the EEG. Overall, this study implies that PLZC is a robust non-linear complexity measure, which is not dependent on amplitude variations in the signal, and which may be useful to further assess EEG alterations induced by environmental or pharmacological manipulations.

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Comparative study of feature extraction and classification based on dispersion Lempel‐Ziv complexity

Jiao

Geng

et al. 2021

Int J Numerical Modelling

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As an important complexity feature of signal, Lempel-Ziv complexity (LZC) has the advantage of simple-to-calculate, but it ignores amplitude information, and has low sensitivity at low amplitude. Dispersion Lempel-Ziv complexity (DLZC) is a recently proposed nonlinear dynamic method, it has the advantage of immunity to noise even at relatively large proportion of noise and has been used to describe different pathological states. In view of its good performance in the field of biomedicine, we introduce DLZC into the field of underwater acoustic and fault diagnosis, and propose a feature extraction method for ship and gear fault signals based on DLZC, then an intelligent classification method was proposed by combining DLZC with K-Nearest Neighbor (KNN) to further verify the effectiveness of the proposed feature extraction method, termed DLZC-KNN. We conducted comparative experiments on feature extraction and classification, respectively: for the feature extraction comparison experiment, we compared the proposed feature extraction method with other feature extraction methods, which are based on Lempel-Ziv complexity (LZC), permutation entropy (PE), dispersion entropy (DE), and fluctuation-based dispersion entropy (FDE); for the classification comparison experiment, we compare the impact of different features and different classifiers on the recognition rate and also discussed the influence of different parameters on the experiment. The results show that DLZC has a better representation of signal complexity, and the DLZC-KNN classification method gets a higher recognition rate than other comparative methods both in the field of fault diagnosis and underwater acoustic.

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A permutation Lempel-Ziv complexity measure for EEG analysis

Cited by 79 publications

References 43 publications

Permutation Entropy for the Characterisation of Brain Activity Recorded with Magnetoencephalograms in Healthy Ageing

Permutation Entropy for the Characterisation of Brain Activity Recorded with Magnetoencephalograms in Healthy Ageing

Characterisation of the Effects of Sleep Deprivation on the Electroencephalogram Using Permutation Lempel–Ziv Complexity, a Non-Linear Analysis Tool

Comparative study of feature extraction and classification based on dispersion Lempel‐Ziv complexity

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