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

Li, Jing; Yan, Jiaqing; Liu, Xianzeng; Ouyang, Gaoxiang

doi:10.3390/e16063049

Cited by 94 publications

(77 citation statements)

References 56 publications

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“…Comparison with the conventional entropy methods with a classification accuracy of 86.1%, the classification rate was 90% with a MPE index 2 . Similarly, Li Jing et al using permutation entropy, an examination was implemented to determine whether EEG data changes can detect various states of human absence epilepsy 1 . The average PE values have been shown to gradually decrease from no seizure to seizure state and provide evidence that three different phases of seizure in absence epilepsy can be detectable.…”

Section: Axis Of Least Inertiamentioning

confidence: 99%

“…About 80% of people with epilepsy live in developing countries. 1 Today, people with epilepsy and their families suffer from social discrimination and social stigma in many parts of the world. Epileptic seizures can be associated with impairment or loss of consciousness.…”

Section: Introductionmentioning

confidence: 99%

“…Epileptic seizures can be associated with impairment or loss of consciousness. 1,2 Absence seizures are a form of generalized seizures with Spike wave discharge (SWD) in EEG. 2 These rapid and sudden seizures are transient symptoms and/or signs of abnormal, excessive or synchronous neuronal activity in the brain.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Axis Of Least Inertiamentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…The EEG dataset was recorded from pediatric subjects with intractable seizures at Children's Hospital Boston. This database contains 22 subjects (17 females, ages 1.5-19; 5 males, ages [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] and can be downloaded from the PhysioNet website: http://www .physionet.org/pn6/chbmit/. The International 10-20 system of EEG electrode positions and nomenclature was used to collect these EEG recordings.…”

Section: Eeg Datasetsmentioning

confidence: 99%

“…Li et al used another order pattern based entropy method (called permutation entropy, PE) as an indicator to predict the epileptic seizures in genetic absence epilepsy rats, and the results showed that PE can successfully detect preseizure phase in 169 of 314 seizures (54%), which was higher than SE (21%) in [12]. Permutation entropy was also applied to analyzing EEG data in patients with epilepsy, which demonstrated that the EEG during the seizure-free phase is characterized by a higher value of permutation entropy than the EEG during the seizure phase [13,14].…”

Section: Introductionmentioning

confidence: 99%

Automated Epileptic Seizure Detection in Scalp EEG Based on Spatial-Temporal Complexity

Zhu

Zhao

et al. 2017

Complexity

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Epilepsy is a group of neurological disorders characterized by epileptic seizures, wherein electroencephalogram (EEG) is one of the most common technologies used to diagnose, monitor, and manage patients with epilepsy. A large number of EEGs have been recorded in clinical applications, which leads to visual inspection of huge volumes of EEG not routinely possible. Hence, automated detection of epileptic seizure has become a goal of many researchers for a long time. A novel method is therefore proposed to construct a patient-specific detector based on spatial-temporal complexity analysis, involving two commonly used entropy-based complexity analysis methods, which are permutation entropy (PE) and sample entropy (SE). The performance of spatial-temporal complexity method is evaluated on a shared dataset. Results suggest that the proposed epilepsy detectors achieve promising performance: the average sensitivities of PE and SE in 23 patients are 99% and 96.6%, respectively. Moreover, both methods can accurately recognize almost all the seizure-free EEG. The proposed method not only obtains a high accuracy rate but also meets the real-time requirements for its application on seizure detection, which suggests that the proposed method has the potential of detecting epileptic seizures in real time.

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Trajectories of brain entropy across lifetime estimated by resting state functional magnetic resonance imaging

Niu

Sun

Wang

et al. 2022

Human Brain Mapping

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The human brain is a complex system of interconnected brain regions that form functional networks with differing roles in cognition and behavior. However, the trajectories of these functional networks across development are unclear and designing a metric to track the complex trajectory of these characteristics throughout the lifespan is challenging. Here, permutation entropy (PE) was used to examine age‐related variations in functional magnetic resonance imaging (fMRI) in healthy subjects aged 6–85 from global, network, and nodal perspectives. The global PE followed an inverted U‐shaped trajectory that peaked at approximately age 40. The trajectory of the motor and somatosensory functional network was more consistent with a linear model and increased with age; other functional networks showed inverted U‐shaped trajectories that peaked between 25 and 52 years of age. All nodes showed inverted U‐shaped trajectories. Using cluster analysis, the peak ages of nodes were grouped into three clusters (at 24, 38, and 51 years). Overall, we characterized four aging trajectories: networks with a linear increase, early peak age, intermediate peak age, and older peak age. These findings suggest possible complexity in trajectories at critical age points regarding changes in related functional brain networks.

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

Cited by 94 publications

References 56 publications

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

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

Automated Epileptic Seizure Detection in Scalp EEG Based on Spatial-Temporal Complexity

Trajectories of brain entropy across lifetime estimated by resting state functional magnetic resonance imaging

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