Predictability analysis of absence seizures with permutation entropy

Li, Xiaoli; Ouyang, Gaoxiang; Richards, Douglas A.

doi:10.1016/j.eplepsyres.2007.08.002

Cited by 268 publications

(182 citation statements)

References 25 publications

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“…Earlier studies have shown that this kind of complexity measures is particularly suitable for non-stationary time series (Cao et al 2004;Keller et al 2014). They have been successfully applied to analyze epileptic and anesthetic EEG data (Ouyang et al 2010;Cao et al 2004;Keller et al 2014;Li et al 2007Li et al , 2008Li et al , 2010. In our previous study (Yi et al 2014), we have also used them to detect the complexity changes of EEG activity associated with manual acupuncture at acupoint ST 36 in healthy subjects.…”

Section: Introductionmentioning

confidence: 99%

Complexity of resting-state EEG activity in the patients with early-stage Parkinson’s disease

Wang

Deng

et al. 2016

Cogn Neurodyn

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To investigate the abnormal brain activities in the early stage of Parkinson's disease (PD), the electroencephalogram (EEG) signals were recorded with 20 channels from non-dementia PD patients (18 patients, 8 females) and age matched healthy controls (18 subjects, 8 females) during the resting state. Two methods based on the ordinal patterns of the recorded series, i.e., permutation entropy (PE) and order index (OI), were introduced to characterize the complexity of the cortical activities for two groups. It was observed that the resting-state EEG of PD patients showed lower PE and higher OI than healthy controls, which indicated that the early-stage PD caused the reduced complexity of EEG. We further applied two methods to determine the complexity of EEG rhythms in five sub-bands. The results showed that the gamma, beta and alpha rhythms of PD patients were characterized by lower PE and higher OI, i.e., reduced complexity, than healthy subjects. No significant differences were observed in theta or delta rhythms between two groups. The findings suggested that PE and OI were promising methods to detect the abnormal changes in the dynamics of EEG signals associated with early-stage PD. Further, such changes in EEG complexity may be the early markers of the cortical or subcortical dysfunction caused by PD.

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Section: Introductionmentioning

confidence: 99%

Complexity of resting-state EEG activity in the patients with early-stage Parkinson’s disease

Wang

Deng

et al. 2016

Cogn Neurodyn

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“…Proposed geometric properties can be calculated in accordance with equation (1) to the trajectory x (t) in dimension d. In practice, all d states are not accessible in the phase space, and only the vector of observations x (t) is available. 30 seizures with dynamic EEG changes in people with absence epilepsy is still open problem 16,17 . Additionally, understanding the transition of brain activity to epileptic seizures and the identification of some pioneering tasks is challenging 8,9 .…”

Section: Feature Extractionmentioning

confidence: 99%

“…[13][14][15] However, the prediction of sudden and rapid seizures with dynamic EEG changes in people with absence epilepsy is still open problem. 16,17 Additionally, understanding the transition of brain activity to epileptic seizures and the identification of some pioneering tasks is challenging. 8,9,18 Sitnikova and van Luijtelaar et al showed that SWD activity in albino rats was associated with the precursor delta and theta short activities in cortex and thalamus.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, nonlinear time series analysis methods are presented to identify epileptic seizure states. 16,[21][22][23][24][25][26] To a certain extent, these methods mainly include the Lyapunov exponent and the correlation dimension, which is able to extract the properties of the useful EEG data to provide evidence to confirm the existence of a previous state of seizure in temporal lobe epilepsy. [21][22][23][24] Ouyang et al used recurrent quantification analysis to distinguish between states in GEARS genetic model of absence epilepsy and show that certainty in seizure periods is higher than in two other states.…”

Section: Introductionmentioning

confidence: 99%

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Detection and Prediction of Absence Seizures Based on Nonlinear Analysis of the EEG in Wag/Rij Animal Model

Lashkari

Sheikhani

Golpayegani

et al. 2018

Int Clin Neurosci J

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Background: Epilepsy is a common neurological disorder with a prevalence of 1% of the world population. Absence epilepsy is a form of generalized seizures with Spike wave discharge in EEG. Epileptic patients have frequent absence seizures that cause immediate loss of consciousness. Methods: In this study, it has been tried to explore whether EEG changes can effectively detect epilepsy in animal model applying non-linear features. To predict the occurrence of absence epilepsy, a long-term EEG signal has been recorded from frontal cortex in seven Wag/Rij rats. After preprocessing, the data was transferred to the phase space to extract the brain system dynamic and geometric properties of this space. Finally, the ability of each features to predict and detect absence epilepsy with two criteria of predictive time and the accuracy of detection and its results were compared with previous studies. Results:The results indicate that the brain system dynamic changes during the transition from freeseizure to pre-seizure and then seizure. Proposed approach diagnostic characteristics yielded 97% accuracy of absence epilepsy diagnosis indicating that due to the nonlinear and complex nature of the system and the brain signal, the use of methods consistent with this nature is important in understanding the dynamic transfer between different epileptic seizures. Conclusion: By changing the state of the absence Seizures, the dynamics are changing, and the results of this research can be useful in real-time applications such as predicting epileptic seizures.

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“…17, no. 3, September 2017 ateness for analyzing EEG signals [10][11][12]. However, since the PE is based on single scale computation, it fails to reflect the dynamical characteristics across multiple temporal scales.…”

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confidence: 99%

Improved Multiscale Permutation Entropy Measure for Analysis of Brain Waves

Choi

2017

IJFIS

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This paper presents a novel multiscale entropy measure for analyzing brain rhythms, called electroencephalograms (EEGs), with an aim of unveiling the underlying dynamics of EEG over multiple time scales. This work is inspired by the known fact that neurological signals such as EEG has distinct dynamics over different temporal scales. To reflect the nonlinear and nonstationary nature of EEGs, the recently developed empirical mode decomposition is incorporated, allowing an EEG to be decomposed into its inherent multiple temporal scale elements, referred to as intrinsic mode functions (IMFs). By computing the permutation entropy of each IMF in a time-dependent manner and averaging them over multiple scales, it yields a data-adaptive multiscale permutation entropy (DAMPE) measure for analyzing the brain waves. Simulation and experimental results show that the proposed DAMPE is efficient to reveal the dynamical changes over multiple scales.

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Predictability analysis of absence seizures with permutation entropy

Cited by 268 publications

References 25 publications

Complexity of resting-state EEG activity in the patients with early-stage Parkinson’s disease

Complexity of resting-state EEG activity in the patients with early-stage Parkinson’s disease

Detection and Prediction of Absence Seizures Based on Nonlinear Analysis of the EEG in Wag/Rij Animal Model

Improved Multiscale Permutation Entropy Measure for Analysis of Brain Waves

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