Detection of Tonic-Clonic Seizures using Wavelet Entropy of Scalp EEG

Epilepsy is the most common chronic neurologic disorder characterized by the recurrence of unprovoked seizures. These seizures are paroxysmal events that result from abnormal neuronal discharges and are categorized into various types based on the clinical manifestations and localization. Tonic-Cloni...

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Bibliographic Details
Main Authors: Mathew, Joseph, Ramakrishnan Manuskandan, Subha, Sivakumaran, N., Karthick, P.A.
Format: Conference Proceeding
Language:English
Subjects:
Electroencephalography
Entropy
Epilepsy
Feature extraction
Humans
Location awareness
Predictive models
Scalp
Seizures - diagnosis
Sensitivity
Support vector machines
Online Access:Request full text
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Summary:Epilepsy is the most common chronic neurologic disorder characterized by the recurrence of unprovoked seizures. These seizures are paroxysmal events that result from abnormal neuronal discharges and are categorized into various types based on the clinical manifestations and localization. Tonic-Clonic seizures (TCSZ) may lead to injuries, and constitute the major risk factor for sudden unexpected death in epilepsy (SUDEP), especially in unattended patients. Therapeutic decisions and clinical trials rely on Video EEG which is not practical outside of clinical setting. In this study, wavelet entropy of scalp EEG signals are utilized to discriminate the seizures with and without clinical manifestations. The scalp EEG records from the publically available Temple University Hospital (TUH) dataset are considered for this work. A sevenlevel, fourth order Daubechies (db4) wavelet is utilized for the decomposition of first four seconds of scalp EEG during seizures. The entropy is extracted from the resultant coefficients and are used to develop SVM based models. Most of the extracted features found to have significant differences (p
ISSN:2694-0604
DOI:10.1109/EMBC46164.2021.9630664