Permutation entropy of scalp EEG: A tool to investigate epilepsies

Ferlazzo, Edoardo; Mammone, Nadia; Cianci, Vittoria; Gasparini, Sara; Gambardella, Antonio; Labate, Angelo; Latella, Maria Adele; Sofia, Vito; Elia, Maurizio; Morabito, Francesco Carlo; Aguglia, Umberto

doi:10.1016/j.clinph.2013.06.023

Cited by 64 publications

(38 citation statements)

References 28 publications

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“…Ferlazzo et al also applied permutation entropy to analyse the EEG recordings from patients with typical absences and healthy subjects. Their results suggested patients showed a recurrent behavior of PE topography, with anterior brain regions constantly associated to high PE levels and posterior brain regions constantly associated to low PE levels, during both interictal and ictal phases [33]. Similar results have been reported by Rosso et al, who compared the functional networks in EEG background between absence epilepsy and healthy control individuals and identified which set of electrodes provide the maximum differentiation.…”

Section: Discussionsupporting

confidence: 74%

“…Mammone et al evaluated permutation entropy extracted from different electrodes in patients with typical absences and healthy subjects [32]. The results showed that the PE values gradually evolve towards the seizure phase and PE seems a useful tool to disclose abnormalities of cerebral electric activity not revealed by conventional EEG [32,33].…”

Section: Introductionmentioning

confidence: 99%

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Using Permutation Entropy to Measure the Changes in EEG Signals During Absence Seizures

Yan

Liu

et al. 2014

Entropy

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Abstract:In this paper, we propose to use permutation entropy to explore whether the changes in electroencephalogram (EEG) data can effectively distinguish different phases in human absence epilepsy, i.e., the seizure-free, the pre-seizure and seizure phases. Permutation entropy is applied to analyze the EEG data from these three phases, each containing 100 19-channel EEG epochs of 2 s duration. The experimental results show the mean value of PE gradually decreases from the seizure-free to the seizure phase and provides evidence that these three different seizure phases in absence epilepsy can be effectively distinguished. Furthermore, our results strengthen the view that most frontal electrodes carry useful information and patterns that can help discriminate among different absence seizure phases.

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Section: Discussionsupporting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Using Permutation Entropy to Measure the Changes in EEG Signals During Absence Seizures

Yan

Liu

et al. 2014

Entropy

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“…In a recent publication, Ferlazzo and collaborators [15] developed a topographical map of PE for patients with typical absences. They have showed that for this type of patient, a higher randomness in the fronto-temporal areas and a lower randomness in the posterior areas occur.…”

Section: Discussionmentioning

confidence: 99%

“…Introduced as a statistical measure in the realm of chaotic systems, PE describes complexity through a phase space reconstruction, which takes into account the non-linear behavior of a time series [11]. In the context of neurophysiology, the PE has been used to measure brain connectivity in epileptic disorders [12], to characterize epileptic seizures [13][14][15], to measure the effect of anesthetic drugs [16,17] and to study complexity in Alzheimer's disease EEGs [18], just to mention a few examples. In the same direction, we develop a method based on PE to characterize the changes that occur in EEG records obtained from a patient suffering generalized tonic-clonic seizures.…”

Section: Introductionmentioning

confidence: 99%

Permutation Entropy Applied to the Characterization of the Clinical Evolution of Epileptic Patients under PharmacologicalTreatment

Mateos

Díaz

Lamberti

2014

Entropy

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Different techniques originated in information theory and tools from nonlinear systems theory have been applied to the analysis of electro-physiological time series. Several clinically relevant results have emerged from the use of concepts, such as entropy, chaos and complexity, in analyzing electrocardiograms and electroencephalographic (EEG) records. In this work, we develop a method based on permutation entropy (PE) to characterize EEG records from different stages in the treatment of a chronic epileptic patient. Our results show that the PE is useful for clearly quantifying the evolution of the patient along a certain lapse of time and allows visualizing in a very convenient way the effects of the pharmacotherapy.

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“…Este método foi utilizado em estudos relacionados a fisiologia (NICOLAOU e GEORGIOU, 2012;FERLAZZO et al, 2014)), engenharia (YAN; LIU e GAO, 2012), geofísica (CONSOLINI e MICHELIS, 2014)), hidrologia (FAN et al, 2013;SERINALDI;ZUNINO e ROSSO, 2014;LANGE;ROSSO e HAUHS, 2013), finanças (BARIVIERA et al, 2015) e em estudos de sistemas caóticos .…”

Section: Entropia De Permutaçãounclassified

Análise da variabilidade espaço temporal da chuva mensal no estado de Pernambuco utilizando o método entropia de permutação

et al. 2018

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RESUMO: Neste artigo analisou-se a variabilidade espacial da dinâmica da chuva em escala mensal no estado de Pernambuco, utilizando o método entropia de permutação. Este método foi desenvolvido como uma medida da complexidade de séries temporais, considerando as correlações temporais entre os valores da série utilizando uma representação simbólica baseada nas comparações dos valores consecutivos. Os resultados mostraram que os valores da entropia diminuem com a distância do litoral, indicando maior variabilidade e menor previsibilidade das chuvas mensais nas regiões da zona da Mata e Agreste e menor variabilidade e maior previsibilidade das chuvas mensais nas regiões do Sertão Pernambucano. PALAVRAS-CHAVE: Complexidade; precipitação; Pernambuco. IntroduçãoA precipitação pluviométrica tem grande importância na caracterização do clima de uma região. O conhecimento da variabilidade espacial e temporal da precipitação

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Permutation entropy of scalp EEG: A tool to investigate epilepsies

Cited by 64 publications

References 28 publications

Using Permutation Entropy to Measure the Changes in EEG Signals During Absence Seizures

Using Permutation Entropy to Measure the Changes in EEG Signals During Absence Seizures

Permutation Entropy Applied to the Characterization of the Clinical Evolution of Epileptic Patients under PharmacologicalTreatment

Análise da variabilidade espaço temporal da chuva mensal no estado de Pernambuco utilizando o método entropia de permutação

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