Convolutional Neural Network Classification of Rest EEG Signals among People with Epilepsy, Psychogenic Non Epileptic Seizures and Control Subjects

Michele Lo Giudice, Edoardo Ferlazzo, Nadia Mammone, Sara Gasparini, Vittoria Cianci, Angelo Pascarella, Anna Mammì, Danilo Mandic, Francesco Carlo Morabito and Umberto Aguglia ()
Additional contact information
Michele Lo Giudice: Department of Information Engineering, Infrastructure and Sustainable Energy (DIIES), University “Mediterranea” of Reggio Calabria, 89100 Reggio Calabria, Italy
Edoardo Ferlazzo: Department of Science Medical and Surgery, University of Catanzaro, 88100 Catanzaro, Italy
Nadia Mammone: Department of Civil, Energy, Environmental and Material Engineering (DICEAM), University “Mediterranea” of Reggio Calabria, 89100 Reggio Calabria, Italy
Sara Gasparini: Department of Science Medical and Surgery, University of Catanzaro, 88100 Catanzaro, Italy
Vittoria Cianci: Regional Epilepsy Center, Great Metropolitan Hospital “Bianchi-Melacrino-Morelli” of Reggio Calabria, 89100 Reggio Calabria, Italy
Angelo Pascarella: Department of Science Medical and Surgery, University of Catanzaro, 88100 Catanzaro, Italy
Anna Mammì: Department of Science Medical and Surgery, University of Catanzaro, 88100 Catanzaro, Italy
Danilo Mandic: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Francesco Carlo Morabito: Department of Civil, Energy, Environmental and Material Engineering (DICEAM), University “Mediterranea” of Reggio Calabria, 89100 Reggio Calabria, Italy
Umberto Aguglia: Department of Science Medical and Surgery, University of Catanzaro, 88100 Catanzaro, Italy

IJERPH, 2022, vol. 19, issue 23, 1-9

Abstract: Identifying subjects with epileptic seizures or psychogenic non-epileptic seizures from healthy subjects via interictal EEG analysis can be a very challenging issue. Indeed, at visual inspection, EEG can be normal in both cases. This paper proposes an automatic diagnosis approach based on deep learning to differentiate three classes: subjects with epileptic seizures (ES), subjects with non-epileptic psychogenic seizures (PNES) and control subjects (CS), analyzed by non-invasive low-density interictal scalp EEG recordings. The EEGs of 42 patients with new-onset ES, 42 patients with PNES video recorded and 19 patients with CS all with normal interictal EEG on visual inspection, were analyzed in the study; none of them was taking psychotropic drugs before registration. The processing pipeline applies empirical mode decomposition (EMD) to 5s EEG segments of 19 channels in order to extract enhanced features learned automatically from the customized convolutional neural network (CNN). The resulting CNN has been shown to perform well during classification, with an accuracy of 85.7%; these results encourage the use of deep processing systems to assist clinicians in difficult clinical settings.

Keywords: EEG; empirical mode decomposition; convolutional neural network; deep learning; PNES; epilepsy (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/23/15733/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/23/15733/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:23:p:15733-:d:984803

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().