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Determination of Antiepileptic Drugs Withdrawal Through EEG Hjorth Parameter Analysis.

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Title: Determination of Antiepileptic Drugs Withdrawal Through EEG Hjorth Parameter Analysis.
Authors: Ouyang, Chen-Sen1 (AUTHOR), Yang, Rei-Cheng2 (AUTHOR), Wu, Rong-Ching3 (AUTHOR), Chiang, Ching-Tai4 (AUTHOR), Lin, Lung-Chang5 (AUTHOR) lclin@kmu.edu.tw
Source: International Journal of Neural Systems. Nov2020, Vol. 30 Issue 11, pN.PAG-N.PAG. 16p.
Subjects: Anticonvulsants, Automated external defibrillation, People with epilepsy, Seizures (Medicine), Electroencephalography, Physicians, Brain-computer interfaces
Abstract: The decision to continue or to stop antiepileptic drug (AED) treatment in patients with prolonged seizure remission is a critical issue. Previous studies have used certain risk factors or electroencephalogram (EEG) findings to predict seizure recurrence after the withdrawal of AEDs. However, validated biomarkers to guide the withdrawal of AEDs are lacking. In this study, we used quantitative EEG analysis to establish a method for predicting seizure recurrence after the withdrawal of AEDs. A total of 34 patients with epilepsy were divided into two groups, 17 patients in the recurrence group and the other 17 patients in the nonrecurrence group. All patients were seizure free for at least two years. Before AED withdrawal, an EEG was performed for each patient that showed no epileptiform discharges. These EEG recordings were classified using Hjorth parameter-based EEG features. We found that the Hjorth complexity values were higher in patients in the recurrence group than in the nonrecurrence group. The extreme gradient boosting classification method achieved the highest performance in terms of accuracy, area under the curve, sensitivity, and specificity (84.76%, 88.77%, 89.67%, and 80.47%, respectively). Our proposed method is a promising tool to help physicians determine AED withdrawal for seizure-free patients. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:The decision to continue or to stop antiepileptic drug (AED) treatment in patients with prolonged seizure remission is a critical issue. Previous studies have used certain risk factors or electroencephalogram (EEG) findings to predict seizure recurrence after the withdrawal of AEDs. However, validated biomarkers to guide the withdrawal of AEDs are lacking. In this study, we used quantitative EEG analysis to establish a method for predicting seizure recurrence after the withdrawal of AEDs. A total of 34 patients with epilepsy were divided into two groups, 17 patients in the recurrence group and the other 17 patients in the nonrecurrence group. All patients were seizure free for at least two years. Before AED withdrawal, an EEG was performed for each patient that showed no epileptiform discharges. These EEG recordings were classified using Hjorth parameter-based EEG features. We found that the Hjorth complexity values were higher in patients in the recurrence group than in the nonrecurrence group. The extreme gradient boosting classification method achieved the highest performance in terms of accuracy, area under the curve, sensitivity, and specificity (84.76%, 88.77%, 89.67%, and 80.47%, respectively). Our proposed method is a promising tool to help physicians determine AED withdrawal for seizure-free patients. [ABSTRACT FROM AUTHOR]
ISSN:01290657
DOI:10.1142/S0129065720500367