Abstract
AbstractElectroencephalogram (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.
Funder
Core Research for Evolutional Science and Technology
Publisher
Springer Science and Business Media LLC
Reference164 articles.
1. Abd El-Samie FE, Alotaiby TN, Khalid MI, Alshebeili SA, Aldosari SA (2018) A review of EEG and MEG epileptic spike detection algorithms. IEEE Access 6:60673–60688
2. Acharya UR, Hagiwara Y, Deshpande SN, Suren S, Koh JEW, Oh SL, Arunkumar N, Ciaccio EJ, Lim CM (2019) Characterization of focal EEG signals: a review. Future Gener Comput Syst 91:290–299
3. Acır N, Güzeliş C (2004) Automatic spike detection in EEG by a two-stage procedure based on support vector machines. Comput Biol Med 34(7):561–575
4. Adjouadi M, Cabrerizo M, Ayala M, Sanchez D, Yaylali I, Jayakar P, Barreto A (2004) A new mathematical approach based on orthogonal operators for the detection of interictal spikes in epileptogenic data. Biomed Sci Instrum 40:175–180
5. Adjouadi M, Sanchez D, Cabrerizo M, Ayala M, Jayakar P, Yaylali I, Barreto A (2004) Interictal spike detection using the Walsh transform. IEEE Trans Biomed Eng 51(5):868–872
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