Using recurrence plot for determinism analysis of EEG recordings in genetic absence epilepsy rats

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

doi:10.1016/j.clinph.2008.04.005

Cited by 105 publications

(58 citation statements)

References 52 publications

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“…The permutation entropy is a promising method to reveal dynamic changes in absence EEG. This result is consistent with our previous studies in the rat model [31,40]. A previous analysis of single-channel EEG has demonstrated that EEG epochs prior to seizures exhibit a higher degree of regularity/predictability than that in seizure-free EEG epochs, but they present a lower degree than that in seizure EEG epochs [41].…”

Section: Discussionsupporting

confidence: 82%

“…Therefore, understanding the transition of brain activity toward an absence seizure, called pre-seizure, is a very demanding task [6,39]. Our previous analysis of dynamic changes in the EEG of GAERS has demonstrated that EEG epochs prior to seizures exhibit a higher degree of regularity/predictability than seizure-free EEG epochs, but they present a lower degree than that in seizure EEG epochs [40,41]. Sitnikova and Luijtelaar showed that the spike-wave discharges (SWD) activity (in Wistar Albino Glaxo/Rijswijk rats) is preceded by short lasting delta and theta precursor activities in cortex and thalamus, but the combination rarely occurs during control periods [42,43].…”

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: 82%

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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“…For example, it is found that the complexity of the EEG activity is relatively lower in quiet sleep and highest during wakefulness (Carrozzi et al 2004). It is also found that the complexity of EEG is higher in seizure-free state than that in seizure state (Ouyang et al 2008). What is more, Bian et al have shown that MA at ST36 can increase the complexity of EEG signals, which can be regards as a characteristic parameter to distinguish the states during acupuncture and before acupuncture (Bian et al 2011).…”

Section: Introductionmentioning

confidence: 81%

Multi-scale order recurrence quantification analysis of EEG signals evoked by manual acupuncture in healthy subjects

Wang

Bian

et al. 2012

Cogn Neurodyn

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To explore the effects of manual acupuncture (MA) on brain activities, we design an experiment that acupuncture at acupoint ST36 of right leg with four different frequencies to obtain electroencephalograph (EEG) signals. Many studies have demonstrated that the complexity of EEG can reflect the states of brain function, so we propose to adopt order recurrence quantification analysis combined with discrete wavelet transform, to analyze the dynamical characteristics of different EEG rhythms under acupuncture, further to explore the effects of MA on the complexity of brain activities from multi-scale point of view. By analyzing the complexity of five EEG rhythms, it is found that the complexity of delta rhythm during acupuncture is lower than before acupuncture, and for alpha rhythm that is higher, but for beta, theta and gamma rhythms there are no obvious changes. All of those effects are especially obvious during acupuncture with frequency of 200 times/min. Furthermore, the determinism extracted from delta, alpha and gamma rhythms can be regarded as a characteristic parameter to distinguish the state acupuncture at 200 times/min and the state before acupuncture. These results can provide a theoretical support for selecting appropriate acupuncture frequency for patients in clinical, and the proposed methods have the potential of exploring the effects of acupuncture on brain activities.

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“…The evolutionary haracteristics of the EEG signal based on the dynamics of urbulence is still an open issue 27,28 . Using geometric eatures, regardless of any assumptions about the type of ynamics of the basis, provides studies independent of the ypothesis of chaos and therefore independent of the nalysis of alternative data 29 .…”

Section: Center Of Gravitymentioning

confidence: 99%

“…[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. 27 Multiscale permutation entropy (MPE) was used to describe the dynamic properties of EEG on various human absence epilepsy and the ability to classify MPE by linear discrimination analysis (LDA) was evaluated. Comparison with the conventional entropy methods with a classification accuracy of 86.1%, the classification rate was 90% with a MPE index.…”

Section: Introductionmentioning

confidence: 99%

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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Using recurrence plot for determinism analysis of EEG recordings in genetic absence epilepsy rats

Cited by 105 publications

References 52 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

Multi-scale order recurrence quantification analysis of EEG signals evoked by manual acupuncture in healthy subjects

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

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