Complex temporal network analysis based on the difference visibility graph for epilepsy with and without electrical...

Area of Science:

• Neuroscience
• Epileptology
• Computational Neuroscience

Background:

• Epilepsy with electrical status epilepticus during sleep (ESES) is a childhood epileptic encephalopathy with neurological dysfunction.
• Previous research indicates brain functional abnormalities in ESES, but specific biomarkers are unclear.

Purpose of the Study:

• To analyze electroencephalogram (EEG) signals using visibility graph methodology to identify neurophysiological biomarkers for ESES.
• To explore differences in EEG complexity between ESES and non-ESES patient groups.

Main Methods:

• Utilized difference visibility graph (DVG) to construct complex temporal networks from non-rapid eye movement (NREM) sleep EEG data.
• Analyzed EEG data from 18 ESES patients and 19 non-ESES patients.
• Quantified network complexity using degree entropy of the DVG.

Main Results:

• The ESES group exhibited higher DVG degree entropy compared to the non-ESES group.
• Significant differences in DVG degree entropy were observed in the central and left parietal regions.
• This study is the first to apply DVG methodology for comparing ESES and non-ESES groups.

Conclusions:

• DVG-based complex temporal networks effectively distinguish between ESES and non-ESES patients.
• Findings suggest potential neuroelectrical biomarkers for ESES diagnosis and monitoring.
• This research provides theoretical support for improved ESES management.