Epileptic seizure focus detection from interictal electroencephalogram: a survey

Cognitive Neurodynamics https://doi.org/10.1007/s11571-022-09816-z (0123456789().,-volV)(0123456789().,-volV) REVIEW PAPER Epileptic seizure focus detection from interictal electroencephalogram: a survey Md. Rabiul Islam1,7 • Xuyang Zhao2 • Yao Miao2 • Hidenori Sugano3 • Toshihisa Tanaka1,2,3,4,5,6 Received: 15 February 2021 / Revised: 15 April 2022 / Accepted: 21 April 2022 Ó The Author(s) 2022 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  Interictal electroencephalogram (EEG)  Seizure focus  Ripple and fast ripple  Phase amplitude coupling (PAC)  High-frequency oscillation (HFOs)  Interictal epileptiform discharges (IEDs)  Neural network Introduction & Toshihisa Tanaka 1 Institute of Global Innovation Research, Tokyo University of Agriculture and Technology, Tokyo, Japan 2 Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan 3 Department of Neurosurgery, Epilepsy Center, Juntendo University, Tokyo, Japan 4 Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan 5 RIKEN Center for Brain Science, Saitama, Japan 6 RIKEN Center for Advanced Intelligent Project, Tokyo, Japan 7 Center for Precision Medicine, The University of Texas Health, San Antonio, USA Epilepsy, one of the most common neurological disorders, can affect people of any age, race, or ethnic background. According to the latest study by the World Health Organization (WHO), approximately 65 million people worldwide are affected by epilepsy, and there are an estimated 2.4 million new cases each year (Giannakakis et al. 2014; Levesque et al. 2017; Stafstrom and Carmant 2015). Epilepsy, defined as repeated and unpredictable seizures, causes social impairment and a high risk of death (Fisher et al. 2014; Pati and Alexopoulos 2010). Childhood epilepsy also seriously impacts the development of the brain by reducing learning ability and mental growth. Epileptologists generally classify seizures as either focal or generalized based on abnormal brain activities (Ngugi et al. 2011; van Mierlo et al. 2014). To control epileptic seizures, epileptologists prescribe anti-epileptic drugs. When these medicines fail to control the seizures, surgical removal of the epileptic focus may be the patient’s best chance for seizure freedom. 123 Cognitive Neurodynamics According to the clinical guidelines related to epilepsy surgery, the epileptic seizure focus is the cortex area from which the seizures originate (Lüders et al. 2006). The identification and surgical removal of the focus must be resected (inactivated or completely disconnected) for complete seizure freedom. Standard diagnostic methods include inspection of seizure semiology, high-resolution magnetic resonance imaging (MRI), and EEG. The scalp EEG is a non-invasive method of recording electrical activity by placing electrodes on the scalp using the international standard 10–20 system (Paul 2018). It provides one of the promising ways to identify the epileptic seizure focus before surgical intervention. However, when epileptologists cannot determine an epileptic seizure focus using non-invasive methods, they indicate to use intracranial EEGs (iEEGs) with the implantation of intracranial electrodes during both interictal and ictal phases. Before the epileptic focus resection for complete abolition of seizures, the epilepsy surgeon should consider integrating the multi-channel intracranial electrodes to these ‘‘area of cortex’’ and recording iEEG signals until collecting enough data from habitual seizures to analyze. The epileptologists then need to inspect the multi-channel iEEG data to identify seizure onset zones (SOZs) from within the recorded cortex area. During these inspections, the epileptologists need to analyze and label all long-term multi-channel iEEG data, the manual detection of which is challenging and time-consuming. The success of the epileptic focus resection for seizure freedom depends on accurate detection of the seizure focus. A key to achieving good results with resection surgery is identifying and resecting the area that may cause seizures. Such areas are called epileptogenic zones (EGZs). Currently, there is no single, non-invasive test method that can identify those areas. Approximately 20–30 percent of patients suffer from recurrent seizures after surgery (Elsharkawy et al. 2011). In the endeavor to design computer-aided diagnosis tools, both non-invasive and invasive iEEGs are promising procedures. Automatic detection of seizure focus is highly desired, as it would reduce the epileptologist’s workload and would, along with other tests, increase confidence in related medical decisions. Also, computer-aided and datadriven approaches may provide a way of revealing a mechanism of epileptogenesis. For designing the computer-aided systems based on the different types of EEG modalities, Some recent studies used biomarkers, including high-frequency oscillations (HFOs)(Zijlmans et al. 2011; Jacobs et al. 2009; Urrestarazu et al. 2007), phase-amplitude coupling (PAC)(Guirgis et al. 2015; Motoi et al. 2018; Amiri et al. 2019), interictal epileptiform discharges (IEDs) (Staley and 123 Dudek 2006; Elsharkawy et al. 2011) while others utilized feature-extraction methods (Sharma et al. 2015b; Akter et al. 2020a, 2019; Itakura and Tanaka 2017). In biomarker-related studies to identify epileptic seizure focus, the computer-aided solutions have combined the epilepsy biomarkers in EEG signals with advanced signal and machine-learning approaches. The epilepsy biomarkers in EEGs are essential for identifying the epileptic seizure focus within conventional clinical systems. H (...truncated)