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Double-Step Machine Learning Based Procedure for HFOs Detection and Classification

Brain Sciences. 2020;10(220):220 DOI 10.3390/brainsci10040220

 

Journal Homepage

Journal Title: Brain Sciences

ISSN: 2076-3425 (Online)

Publisher: MDPI AG

LCC Subject Category: Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry

Country of publisher: Switzerland

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB, XML

 

AUTHORS


Nicolina Sciaraffa (Department of Molecular Medicine, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185 Rome, Italy)

Manousos A. Klados (Department of Psychology, The University of Sheffield, International Faculty, City College, 54626 Thessaloniki, Greece)

Gianluca Borghini (Department of Molecular Medicine, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185 Rome, Italy)

Gianluca Di Flumeri (Department of Molecular Medicine, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185 Rome, Italy)

Fabio Babiloni (Department of Molecular Medicine, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185 Rome, Italy)

Pietro Aricò (Department of Molecular Medicine, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185 Rome, Italy)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 11 weeks

 

Abstract | Full Text

The need for automatic detection and classification of high-frequency oscillations (HFOs) as biomarkers of the epileptogenic tissue is strongly felt in the clinical field. In this context, the employment of artificial intelligence methods could be the missing piece to achieve this goal. This work proposed a double-step procedure based on machine learning algorithms and tested it on an intracranial electroencephalogram (iEEG) dataset available online. The first step aimed to define the optimal length for signal segmentation, allowing for an optimal discrimination of segments with HFO relative to those without. In this case, binary classifiers have been tested on a set of energy features. The second step aimed to classify these segments into ripples, fast ripples and fast ripples occurring during ripples. Results suggest that LDA applied to 10 ms segmentation could provide the highest sensitivity (0.874) and 0.776 specificity for the discrimination of HFOs from no-HFO segments. Regarding the three-class classification, non-linear methods provided the highest values (around 90%) in terms of specificity and sensitivity, significantly different to the other three employed algorithms. Therefore, this machine-learning-based procedure could help clinicians to automatically reduce the quantity of irrelevant data.