Why the study?
Manual inspection of EEG signals is time-consuming, leading to numerous machine learning studies for automated epilepsy detection that require systematic evaluation.
This systematic review highlights the growing use of Convolutional Neural Networks and Time-Frequency decomposition in machine learning algorithms for automated epilepsy detection using EEG databases.
May inform automated EEG epilepsy detection tools; extends ML literature by quantifying the CNN time-frequency trend.
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.
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Μιλτιάδους et al. (2022) studied this question.
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