Epileptic seizure focus detection from interictal electroencephalogram: a survey

Md Rabiul Islam; Xuyang Zhao; Yao Miao; Hidenori Sugano; Toshihisa Tanaka

doi:10.1007/s11571-022-09816-z

Epileptic seizure focus detection from interictal electroencephalogram: a survey

Cogn Neurodyn. 2023 Feb;17(1):1-23. doi: 10.1007/s11571-022-09816-z. Epub 2022 May 18.

Authors

Md Rabiul Islam^{1

2}, Xuyang Zhao³, Yao Miao³, Hidenori Sugano⁴, Toshihisa Tanaka^{1

3

4

5

6

7}

Affiliations

¹ Institute of Global Innovation Research, Tokyo University of Agriculture and Technology, Tokyo, Japan.
² Center for Precision Medicine, The University of Texas Health, San Antonio, USA.
³ Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan.
⁴ Department of Neurosurgery, Epilepsy Center, Juntendo University, Tokyo, Japan.
⁵ Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan.
⁶ RIKEN Center for Brain Science, Saitama, Japan.
⁷ RIKEN Center for Advanced Intelligent Project, Tokyo, Japan.

Abstract

Electroencephalogram (EEG) is one of most effective clinical diagnosis modalities for the localization of epileptic focus. Most current AI solutions use this modality to analyze the EEG signals in an automated manner to identify the epileptic seizure focus. To develop AI system for identifying the epileptic focus, there are many recently-published AI solutions based on biomarkers or statistic features that utilize interictal EEGs. In this review, we survey these solutions and find that they can be divided into three main categories: (i) those that use of biomarkers in EEG signals, including high-frequency oscillation, phase-amplitude coupling, and interictal epileptiform discharges, (ii) others that utilize feature-extraction methods, and (iii) solutions based upon neural networks (an end-to-end approach). We provide a detailed description of seizure focus with clinical diagnosis methods, a summary of the public datasets that seek to reduce the research gap in epilepsy, recent novel performance evaluation criteria used to evaluate the AI systems, and guidelines on when and how to use them. This review also suggests a number of future research challenges that must be overcome in order to design more efficient computer-aided solutions to epilepsy focus detection.

Keywords: Epilepsy; High-frequency oscillation (HFOs); Interictal electroencephalogram (EEG); Interictal epileptiform discharges (IEDs); Neural network; Phase amplitude coupling (PAC); Ripple and fast ripple; Seizure focus.

Publication types

Review