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Complexity of Multi-Channel Electroencephalogram Signal Analysis in Childhood Absence Epilepsy.

Weng WC, Jiang GJ, Chang CF, Lu WY, Lin CY, Lee WT, Shieh JS - PLoS ONE (2015)

Bottom Line: The entropy values in the pre-ictal state were significantly higher than those in the ictal state.The MSE revealed more differences in analysis compared to the SamEn.In conclusion, MSE analysis is better than SamEn analysis to analyze complexity of EEG, and CI can be used to investigate the functional brain changes during absence seizures.

View Article: PubMed Central - PubMed

Affiliation: Department of Life Science, National Taiwan University, Taipei, Taiwan; Department of Pediatrics, National Taiwan University Hospital, Taipei, Taiwan; Department of Pediatrics, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Pediatric Neurology, National Taiwan University Children's Hospital, Taipei, Taiwan.

ABSTRACT
Absence epilepsy is an important epileptic syndrome in children. Multiscale entropy (MSE), an entropy-based method to measure dynamic complexity at multiple temporal scales, is helpful to disclose the information of brain connectivity. This study investigated the complexity of electroencephalogram (EEG) signals using MSE in children with absence epilepsy. In this research, EEG signals from 19 channels of the entire brain in 21 children aged 5-12 years with absence epilepsy were analyzed. The EEG signals of pre-ictal (before seizure) and ictal states (during seizure) were analyzed by sample entropy (SamEn) and MSE methods. Variations of complexity index (CI), which was calculated from MSE, from the pre-ictal to the ictal states were also analyzed. The entropy values in the pre-ictal state were significantly higher than those in the ictal state. The MSE revealed more differences in analysis compared to the SamEn. The occurrence of absence seizures decreased the CI in all channels. Changes in CI were also significantly greater in the frontal and central parts of the brain, indicating fronto-central cortical involvement of "cortico-thalamo-cortical network" in the occurrence of generalized spike and wave discharges during absence seizures. Moreover, higher sampling frequency was more sensitive in detecting functional changes in the ictal state. There was significantly higher correlation in ictal states in the same patient in different seizures but there were great differences in CI among different patients, indicating that CI changes were consistent in different absence seizures in the same patient but not from patient to patient. This implies that the brain stays in a homogeneous activation state during the absence seizures. In conclusion, MSE analysis is better than SamEn analysis to analyze complexity of EEG, and CI can be used to investigate the functional brain changes during absence seizures.

No MeSH data available.


Related in: MedlinePlus

The illustration of the coarse graining procedure for scales 1 to τ.
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pone.0134083.g001: The illustration of the coarse graining procedure for scales 1 to τ.

Mentions: Entropy was regarded as the index of the degree of randomness of data points. In the MSE analysis, the original EEG time series {x1,x2,…,xN} was first coarse-grained by the scale factor (SF) τ to get different new EEG time series (Fig 1). The EEG time series in different scales could be calculated as follows:yi(τ)=1τ∑jτi=(j−1)τ+1xi, 1≤j≤Nτ


Complexity of Multi-Channel Electroencephalogram Signal Analysis in Childhood Absence Epilepsy.

Weng WC, Jiang GJ, Chang CF, Lu WY, Lin CY, Lee WT, Shieh JS - PLoS ONE (2015)

The illustration of the coarse graining procedure for scales 1 to τ.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4526647&req=5

pone.0134083.g001: The illustration of the coarse graining procedure for scales 1 to τ.
Mentions: Entropy was regarded as the index of the degree of randomness of data points. In the MSE analysis, the original EEG time series {x1,x2,…,xN} was first coarse-grained by the scale factor (SF) τ to get different new EEG time series (Fig 1). The EEG time series in different scales could be calculated as follows:yi(τ)=1τ∑jτi=(j−1)τ+1xi, 1≤j≤Nτ

Bottom Line: The entropy values in the pre-ictal state were significantly higher than those in the ictal state.The MSE revealed more differences in analysis compared to the SamEn.In conclusion, MSE analysis is better than SamEn analysis to analyze complexity of EEG, and CI can be used to investigate the functional brain changes during absence seizures.

View Article: PubMed Central - PubMed

Affiliation: Department of Life Science, National Taiwan University, Taipei, Taiwan; Department of Pediatrics, National Taiwan University Hospital, Taipei, Taiwan; Department of Pediatrics, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Pediatric Neurology, National Taiwan University Children's Hospital, Taipei, Taiwan.

ABSTRACT
Absence epilepsy is an important epileptic syndrome in children. Multiscale entropy (MSE), an entropy-based method to measure dynamic complexity at multiple temporal scales, is helpful to disclose the information of brain connectivity. This study investigated the complexity of electroencephalogram (EEG) signals using MSE in children with absence epilepsy. In this research, EEG signals from 19 channels of the entire brain in 21 children aged 5-12 years with absence epilepsy were analyzed. The EEG signals of pre-ictal (before seizure) and ictal states (during seizure) were analyzed by sample entropy (SamEn) and MSE methods. Variations of complexity index (CI), which was calculated from MSE, from the pre-ictal to the ictal states were also analyzed. The entropy values in the pre-ictal state were significantly higher than those in the ictal state. The MSE revealed more differences in analysis compared to the SamEn. The occurrence of absence seizures decreased the CI in all channels. Changes in CI were also significantly greater in the frontal and central parts of the brain, indicating fronto-central cortical involvement of "cortico-thalamo-cortical network" in the occurrence of generalized spike and wave discharges during absence seizures. Moreover, higher sampling frequency was more sensitive in detecting functional changes in the ictal state. There was significantly higher correlation in ictal states in the same patient in different seizures but there were great differences in CI among different patients, indicating that CI changes were consistent in different absence seizures in the same patient but not from patient to patient. This implies that the brain stays in a homogeneous activation state during the absence seizures. In conclusion, MSE analysis is better than SamEn analysis to analyze complexity of EEG, and CI can be used to investigate the functional brain changes during absence seizures.

No MeSH data available.


Related in: MedlinePlus