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<p>Epilepsy with electrical status epilepticus during sleep (ESES) is a distinct form of epileptic encephalopathy in childhood, often associated with varying degrees of neurological dysfunction. While previous studies have reported brain functional abnormalities in epilepsy with ESES patients, the specific brain regions and potential neuroelectrical biomarkers remain unclear. This study aims to leverage visibility graph methodology to deeply analyze electroencephalogram (EEG) signals and explore potential neurophysiological biomarkers for ESES. Using graph theory based on the difference visibility graph (DVG), we constructed complex temporal networks from non-rapid eye movement (NREM) sleep EEG data of 18 epilepsy with ESES patients and 19 epilepsy without ESES (nonESES) patients. Degree entropy of the DVG was employed to quantify differences in the complexity between the two groups. Compared with non-ESES group, ESES group showed higher degree entropy of the DVG, with significant differences in the central region and left parietal region. This is the first time that DVG has been used to compare ESES and non-ESES groups. This study confirmed the effectiveness of the complex temporal network based on DVG in distinguishing ESES and non-ESES patients. Our findings offer promising potential biomarkers and theoretical support for the accurate diagnosis and longitudinal monitoring of ESES.</p>

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Complex temporal network analysis based on the difference visibility graph for epilepsy with and without electrical status epilepticus during sleep (ESES) patients

  • 期刊论文

  • 发表日期：

    2026年2月18日

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  • Zhipeng He,
  • Xinxin Peng,
  • Shishi Tang,
  • Yuxuan Li,
  • Yue Liu,
  • Rui Yang,
  • Jianping Man,
  • Ziyi Chen,
  • Yi Zhou

摘要

Epilepsy with electrical status epilepticus during sleep (ESES) is a distinct form of epileptic encephalopathy in childhood, often associated with varying degrees of neurological dysfunction. While previous studies have reported brain functional abnormalities in epilepsy with ESES patients, the specific brain regions and potential neuroelectrical biomarkers remain unclear. This study aims to leverage visibility graph methodology to deeply analyze electroencephalogram (EEG) signals and explore potential neurophysiological biomarkers for ESES. Using graph theory based on the difference visibility graph (DVG), we constructed complex temporal networks from non-rapid eye movement (NREM) sleep EEG data of 18 epilepsy with ESES patients and 19 epilepsy without ESES (nonESES) patients. Degree entropy of the DVG was employed to quantify differences in the complexity between the two groups. Compared with non-ESES group, ESES group showed higher degree entropy of the DVG, with significant differences in the central region and left parietal region. This is the first time that DVG has been used to compare ESES and non-ESES groups. This study confirmed the effectiveness of the complex temporal network based on DVG in distinguishing ESES and non-ESES patients. Our findings offer promising potential biomarkers and theoretical support for the accurate diagnosis and longitudinal monitoring of ESES.