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.
Highlights
Absence epilepsy is a common generalized epilepsy in children [1]
Based on the evidence from these studies as well as our own previous studies, the present study aimed to investigate the role of Multiscale entropy (MSE) of EEG signals in children with absence seizures and clarify the variations of complexity index (CI) in their pre-ictal and ictal states
Recent advances in understanding the pathophysiology of absence epilepsy suggest that absence seizures may not be truly generalized seizures from the beginning, and various cortical or sub-cortical activation preceding generalized seizures have been documented [4,5,6,7, 9,10,11]
Summary
Absence epilepsy is a common generalized epilepsy in children [1]. Its characteristic features are the abrupt cessation of activities and consciousness impairment, with more or less automatisms [2]. The duration of a typical absence seizure is brief, lasting for seconds, without an aura or post-ictal impairment [2]. Electroencephalogram (EEG) is a non-invasive measurement that provides temporal and spatial information regarding the electrical activity of the brain that has been widely used to detect seizures in patients with epilepsy [3]. Ictal EEG in typical absence seizures is characterized by the sudden onset of 3 Hz generalized spike-wave complexes, while the inter-ictal EEG typically shows a normal background. Dynamic changes in the temporo-spatial course have been shown in typical absence seizures using simultaneous EEG and functional magnetic resonance imaging (EEG-fMRI) [9,10,11,12,13,14,15]. The detection of seizure dynamics from pre-ictal to ictal states cannot be reached solely using conventional EEG without advanced analysis
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