Unsupervised learning from EEG data for epilepsy: A systematic literature review.

Journal: Artificial intelligence in medicine
PMID: 40022810

Abstract

BACKGROUND AND OBJECTIVES: Epilepsy is a neurological disorder characterized by recurrent epileptic seizures, whose neurophysiological signature is altered electroencephalographic (EEG) activity. The use of artificial intelligence (AI) methods on EEG data can positively impact the management of the disease, significantly improving diagnostic and prognostic accuracy as well as treatment outcomes. Our work aims to systematically review the available literature on the use of unsupervised machine learning methods on EEG data in epilepsy, focusing on methodological and clinical differences in terms of algorithms used and clinical applications.

Authors

  • Alexandra-Maria Tautan
  • Alexandra-Georgiana Andrei
    AI Multimedia Lab, CAMPUS Research Institute, National University of Science and Technology Politehnica Bucharest, Romania.
  • Carmelo Luca Smeralda
    Siena Brain Investigation & Neuromodulation Lab (Si-BIN Lab), Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Section, University of Siena, Siena, Italy.
  • Giampaolo Vatti
    Siena Brain Investigation & Neuromodulation Lab (Si-BIN Lab), Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Section, University of Siena, Siena, Italy.
  • Simone Rossi
  • Bogdan Ionescu

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