Prediction of seizure recurrence using electroencephalogram analysis with multiscale deep neural networks before withdrawal of antiepileptic drugs

Lung-Chang Lin; Ming-Yuh Chang; Yi-Hung Chiu; Ching-Tai Chiang; Rong-Ching Wu; Rei-Cheng Yang; Chen-Sen Ouyang

doi:10.1016/j.pedneo.2021.12.011

Prediction of seizure recurrence using electroencephalogram analysis with multiscale deep neural networks before withdrawal of antiepileptic drugs

Pediatr Neonatol. 2022 May;63(3):283-290. doi: 10.1016/j.pedneo.2021.12.011. Epub 2022 Mar 15.

Authors

Lung-Chang Lin¹, Ming-Yuh Chang², Yi-Hung Chiu³, Ching-Tai Chiang⁴, Rong-Ching Wu⁵, Rei-Cheng Yang⁶, Chen-Sen Ouyang⁷

Affiliations

¹ Department of Pediatrics, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung City, Taiwan; Department of Pediatrics, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung City, Taiwan.
² Departments of Pediatrics, Changhua Christian Hospital, Changhua, Taiwan.
³ Department of Information Engineering, I-Shou University, Kaohsiung City, Taiwan.
⁴ Department of Computer and Communication, National Pingtung University, Pingtung City, Taiwan.
⁵ Department of Electrical Engineering, I-Shou University, Kaohsiung City, Taiwan.
⁶ Department of Pediatrics, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung City, Taiwan; Department of Pediatrics, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung City, Taiwan. Electronic address: rechya@kmu.edu.tw.
⁷ Department of Information Engineering, I-Shou University, Kaohsiung City, Taiwan. Electronic address: ouyangcs@isu.edu.tw.

PMID: 35367151
DOI: 10.1016/j.pedneo.2021.12.011

Abstract

Background: The decision to continue or discontinue antiepileptic drug (AED) treatment in patients who are seizure free for a prolonged time is critical. Studies have used certain risk factors or electroencephalogram (EEG) findings to predict seizure recurrence after the withdrawal of AEDs. However, applicable biomarkers to guide the withdrawal of AEDs are lacking.

Methods: In this study, we used EEG analysis based on multiscale deep neural networks (MSDNN) to establish a method for predicting seizure recurrence after the withdrawal of AEDs. A total of 60 patients with epilepsy were divided into two groups (30 in the recurrence group and 30 in the non-recurrence group). All patients were seizure free for at least 2 years. Before AED withdrawal, an EEG was performed for each patient, which showed no epileptiform discharges. These EEG recordings were classified using MSDNN.

Results: We found that the performance indices of classification between recurrence and non-recurrence groups had a mean sensitivity, mean specificity, mean accuracy, and mean area under the receiver operating characteristic curve of 74.23%, 75.83%, 74.66%, and 82.66%, respectively.

Conclusion: Our proposed method is a promising tool to help physicians to predict seizure recurrence after AED withdrawal among seizure-free patients.

Keywords: antiepileptic drug; multiscale deep neural networks; withdrawal.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Anticonvulsants* / therapeutic use
Electroencephalography
Humans
Neural Networks, Computer
Recurrence
Seizures* / drug therapy

Substances

Anticonvulsants