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Quantified EEG for the Characterization of Epileptic Seizures versus Periodic Activity in Critically Ill Patients
Open AccessArticle

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

1
Department of Molecular Medicine, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185 Rome, Italy
2
BrainSigns srl, Lungotevere Michelangelo, 9, 00192 Rome, Italy
3
Department of Psychology, The University of Sheffield, International Faculty, City College, 54626 Thessaloniki, Greece
4
IRCCS Fondazione Santa Lucia, Neuroelectrical Imaging and BCI Lab, Via Ardeatina, 306, 00179 Rome, Italy
5
College of Computer Science and Technology, Hangzhou Dianzi University, Xiasha Higher Education Zone, Hangzhou 310018, China
*
Author to whom correspondence should be addressed.
Brain Sci. 2020, 10(4), 220; https://doi.org/10.3390/brainsci10040220
Received: 24 February 2020 / Revised: 3 April 2020 / Accepted: 6 April 2020 / Published: 8 April 2020
(This article belongs to the Special Issue Neurophysiological Techniques for Epilepsy)
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. View Full-Text
Keywords: high-frequency oscillations; HFO; machine learning; epilepsy; intracranial EEG high-frequency oscillations; HFO; machine learning; epilepsy; intracranial EEG
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Sciaraffa, N.; Klados, M.A.; Borghini, G.; Di Flumeri, G.; Babiloni, F.; Aricò, P. Double-Step Machine Learning Based Procedure for HFOs Detection and Classification. Brain Sci. 2020, 10, 220.

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