Novel multi-view Takagi–Sugeno–Kang fuzzy system for epilepsy EEG detection

Li, Yarong; Qian, Pengjiang; Wang, Shuihua; Wang, Shitong

doi:10.1007/s12652-021-03189-7

Cited by 2 publications

(1 citation statement)

References 42 publications

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“…Ari et al [59] also extracted the dispersion entropy using a WPD and trained an SVM classifier, obtaining 99.53% ACC in the Z-O-N-F-S problem. Other machine learning approaches that have been used along WPD (regarding the Bonn DB) are Graph-Based ELM [60] and TSK fuzzy system [61]. A modified version of the Wavelet Transform namely Scattering Transform was employed by Jiang et al [62] and achieved 99.87% ACC with a SVM classifier in the ZO-NF-S problem.…”

Section: A: Short-time Fourier Transformmentioning

confidence: 99%

Machine Learning Algorithms for Epilepsy Detection Based on Published EEG Databases: A Systematic Review

et al. 2023

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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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