Machine Learning Algorithms for Epilepsy Detection Based on Published EEG Databases: A Systematic Review
Ανδρέας Μιλτιάδους, Katerina D. Tzimourta, Νικόλαος Γιαννακέας, Markos G. Tsipouras, Euripidis Glavas, Konstantinos Kalafatakis, Alexandros T. Tzallas
Abstract
Epilepsy is the only neurological condition for which electroencephalography (EEG) is the primary diagnostic and important prognostic clinical tool. However, the manual inspection of EEG signals is a time-consuming procedure for neurologists. Thus, intense research has been made on creating machine learning methodologies for automated epilepsy detection. Also, many research or medical facilities have published databases of epileptic EEG signals to accommodate this research effort. The vast number of studies concerning epilepsy detection with EEG makes this systematic review necessary. It presents a detailed evaluation of the signal processing and classification methodologies employed on the different databases and provides valuable insights for future work. 190 studies were included in this systematic review according to the PRISMA guidelines, acquired from a systematic literature search in PubMed, Scopus, ScienceDirect and IEEE Xplore on 1st May 2021. Studies were examined based on the Signal Transformation technique, classification methodology and database for evaluation. Along with other findings, the increasing tendency to employ Convolutional Neural Networks that use a combination of Time-Frequency decomposition methodology images is noticed.