Amplitude-aware permutation entropy: Illustration in spike detection and signal segmentation

Azami, Hamed; Escudero, Javier

doi:10.1016/j.cmpb.2016.02.008

Cited by 146 publications

(172 citation statements)

References 43 publications

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“…Thus, given that PEn only considers ordinal structure of patterns, the amplitude of each sample is discarded. As an example, vectors X m (i) = {1, 7, 4} and X m (j) = {23, 34, 28} produce the same ordinal pattern π k = {0, 2, 1}, thus contributing equally to the final value of PEn [46]. Different alternatives to overcome this problem have been proposed [46,54,55], but they make use of a very similar mathematical basis.…”

Section: Permutation Entropymentioning

confidence: 99%

“…As an example, vectors X m (i) = {1, 7, 4} and X m (j) = {23, 34, 28} produce the same ordinal pattern π k = {0, 2, 1}, thus contributing equally to the final value of PEn [46]. Different alternatives to overcome this problem have been proposed [46,54,55], but they make use of a very similar mathematical basis. Hence, the most recently proposed amplitude-aware PEn (AAPEn) has been considered in this study [46].…”

Section: Permutation Entropymentioning

confidence: 99%

“…Different alternatives to overcome this problem have been proposed [46,54,55], but they make use of a very similar mathematical basis. Hence, the most recently proposed amplitude-aware PEn (AAPEn) has been considered in this study [46]. Briefly, the repetition probability of each pattern π k is estimated by considering its relative frequency, as well as the average absolute (AA) and relative amplitudes (RA) associated with the vectors X m .…”

Section: Permutation Entropymentioning

confidence: 99%

“…Interestingly, the information collected from the EEG signal with these indices has resulted in being complementary to the one obtained by SEn and QSEn in a variety of clinical contexts [42]. Hence, the main goal of the present work is to complement the previous QSEn analysis for automatic identification of distress by exploring the popular permutation entropy (PEn) and its most recent modification to consider data amplitude [45,46]. PEn is a simple and fast method that transforms the original time series into a symbolized sequence without any prior knowledge [45].…”

Section: Introductionmentioning

confidence: 99%

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Symbolic Analysis of Brain Dynamics Detects Negative Stress

García-Martínez

Martínez-Rodrigo

Zangróniz

et al. 2017

Entropy

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Abstract:The electroencephalogram (EEG) is the most common tool used to study mental disorders. In the last years, the use of this recording for recognition of negative stress has been receiving growing attention. However, precise identification of this emotional state is still an interesting unsolved challenge. Nowadays, stress presents a high prevalence in developed countries and, moreover, its chronic condition often leads to concomitant physical and mental health problems. Recently, a measure of time series irregularity, such as quadratic sample entropy (QSEn), has been suggested as a promising single index for discerning between emotions of calm and stress. Unfortunately, this index only considers repetitiveness of similar patterns and, hence, it is unable to quantify successfully dynamics associated with the data temporal structure. With the aim of extending QSEn ability for identification of stress from the EEG signal, permutation entropy (PEn) and its modification to be amplitude-aware (AAPEn) have been analyzed in the present work. These metrics assess repetitiveness of ordinal patterns, thus considering causal information within each one of them and obtaining improved estimates of predictability. Results have shown that PEn and AAPEn present a discriminant power between emotional states of calm and stress similar to QSEn, i.e., around 65%. Additionally, they have also revealed complementary dynamics to those quantified by QSEn, thus suggesting a synchronized behavior between frontal and parietal counterparts from both hemispheres of the brain. More precisely, increased stress levels have resulted in activation of the left frontal and right parietal regions and, simultaneously, in relaxing of the right frontal and left parietal areas. Taking advantage of this brain behavior, a discriminant model only based on AAPEn and QSEn computed from the EEG channels P3 and P4 has reached a diagnostic accuracy greater than 80%, which improves slightly the current state of the art. Moreover, because this classification system is notably easier than others previously proposed, it could be used for continuous monitoring of negative stress, as well as for its regulation towards more positive moods in controlled environments.

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Section: Permutation Entropymentioning

confidence: 99%

Section: Permutation Entropymentioning

confidence: 99%

Section: Permutation Entropymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Symbolic Analysis of Brain Dynamics Detects Negative Stress

García-Martínez

Martínez-Rodrigo

Zangróniz

et al. 2017

Entropy

View full text Add to dashboard Cite

show abstract

“…This is of importance as LZC analysis is dependent to amplitude changes which are typical in signals such as the EEG. 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%

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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Complexity Analysis in Health Informatics

Ahmed

2020

Intelligent Systems Reference Library

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Complexity can be interpreted as a manifestation of the intricate entwining or inter-connectivity of elements within a system and between a system and its surroundings. Complex adaptive systems (CAS) are comprised of multiple subsystems that exhibit nonlinear deterministic & stochastic characteristics, and are regulated hierarchically. Examples of CAS include stock markets, human heart or brain, weather and climate systems, internet etc. A system's complexity usually reflected in the dynamical fluctuations of the output generated by the free-running conditions. In this chapter, the definitional controversies for complexity are introduced and signal properties associated with complexity are reviewed. We then introduce some criteria used to classify complexity measures in the literature, and finally some representative complexity measures used in health informatics are described. Keywords Complexity • Long-range correlation • Multiscale entropy • Dynamical systems • Information-theoretic measures • Chaos-theoretic measures • Random fractal-theoretic measures • Randomness What is Complexity?The concept of complexity and the study of complex adaptive systems originated from a whole chain of interdisciplinary approaches, from the theory of nonlinear dynamical systems to information theory, statistical mechanics, biology, sociology, ecology, economics and others. Researchers in those areas have been trying to define complexity, nonetheless, no all-encompassing definition has emerged as yet.Murray Gell-Mann, the winner of 1969 Nobel Prize in physics, traces the meaning of complexity to the root of the word. The English word complex is derived from the Latin word complexus, meaning 'entwined' or 'braided together [1]. Similarly, the Oxford Dictionary defines something as 'complex' if it is 'made of (usually several) 1 http://www.necsi.edu/guide/concepts/complexity.html.

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Amplitude-aware permutation entropy: Illustration in spike detection and signal segmentation

Cited by 146 publications

References 43 publications

Symbolic Analysis of Brain Dynamics Detects Negative Stress

Symbolic Analysis of Brain Dynamics Detects Negative Stress

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

Complexity Analysis in Health Informatics

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