Quantitative Analysis of Inter‑ and Intrahemispheric Coherence on Epileptic Electroencephalography Signal
Publish place: Journal of medical signals and sensors، Vol: 12، Issue: 2
Publish Year: 1401
نوع سند: مقاله ژورنالی
زبان: English
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JR_JMSI-12-2_008
تاریخ نمایه سازی: 28 تیر 1402
Abstract:
When an epileptic seizure occurs, the neuron’s activity of the brain is dynamically changed, which
affects the connectivity between brain regions. The connectivity of each brain region can be
quantified by electroencephalography (EEG) coherence, which measures the statistical correlation
between electrodes spatially separated on the scalp. Previous studies conducted a coherence analysis
of all EEG electrodes covering all parts of the brain. However, in an epileptic condition, seizures
occur in a specific region of the brain then spreading to other areas. Therefore, this study applies an
energy‑based channel selection process to determine the coherence analysis in the most active brain
regions during the seizure. This paper presents a quantitative analysis of inter‑ and intrahemispheric
coherence in epileptic EEG signals and the correlation with the channel activity to glean insights
about brain area connectivity changes during epileptic seizures. The EEG signals are obtained from
ten patients’ data from the CHB‑MIT dataset. Pair‑wise electrode spectral coherence is calculated
in the full band and five sub‑bands of EEG signals. The channel activity level is determined by
calculating the energy of each channel in all patients. The EEG coherence observation in the
preictal (Cohpre) and ictal (Cohictal) conditions showed a significant decrease of Cohictal in the most
active channel, especially in the lower EEG sub‑bands. This finding indicates that there is a strong
correlation between the decrease of mean spectral coherence and channel activity. The decrease of
coherence in epileptic conditions (Cohictal coherence and the channel activity. The
finding in this study demonstrates that the neuronal connectivity of epileptic EEG signals is suitable
to be analyzed in the more active brain regions.
Keywords:
Authors
Inung Wijayanto
Department of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta- School of Electrical Engineering, Telkom University, Bandung, Indonesia
Ruby Hartanto
Department of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta
Hanung Adi Ungroho
Department of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta