Convolutional Neural Network Classification of Rest EEG Signals among People with Epilepsy, Psychogenic Non Epileptic Seizures and Control Subjects Article Swipe
Michele Lo Giudice
,
Edoardo Ferlazzo
,
Nadia Mammone
,
Sara Gasparini
,
Vittoria Cianci
,
Angelo Pascarella
,
Anna Mammì
,
Danilo P. Mandic
,
Francesco Carlo Morabito
,
Umberto Aguglia
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/ijerph192315733
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/ijerph192315733
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ijerph192315733
- https://www.mdpi.com/1660-4601/19/23/15733/pdf?version=1669720274
- OA Status
- gold
- Cited By
- 10
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4310999354
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4310999354Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/ijerph192315733Digital Object Identifier
- Title
-
Convolutional Neural Network Classification of Rest EEG Signals among People with Epilepsy, Psychogenic Non Epileptic Seizures and Control SubjectsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-26Full publication date if available
- Authors
-
Michele Lo Giudice, Edoardo Ferlazzo, Nadia Mammone, Sara Gasparini, Vittoria Cianci, Angelo Pascarella, Anna Mammì, Danilo P. Mandic, Francesco Carlo Morabito, Umberto AgugliaList of authors in order
- Landing page
-
https://doi.org/10.3390/ijerph192315733Publisher landing page
- PDF URL
-
https://www.mdpi.com/1660-4601/19/23/15733/pdf?version=1669720274Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1660-4601/19/23/15733/pdf?version=1669720274Direct OA link when available
- Concepts
-
Electroencephalography, Ictal, Psychogenic disease, Epilepsy, Convolutional neural network, Scalp, Artificial intelligence, Psychology, Audiology, Computer science, Medicine, Neuroscience, Psychiatry, AnatomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 3, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
31Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.analysis | 15 |
| abstract_inverted_index.analyzed | 63, 99 |
| abstract_inverted_index.approach | 39 |
| abstract_inverted_index.channels | 126 |
| abstract_inverted_index.classes: | 47 |
| abstract_inverted_index.clinical | 172 |
| abstract_inverted_index.enhanced | 131 |
| abstract_inverted_index.features | 132 |
| abstract_inverted_index.learning | 43 |
| abstract_inverted_index.patients | 75, 80, 87 |
| abstract_inverted_index.pipeline | 114 |
| abstract_inverted_index.proposes | 35 |
| abstract_inverted_index.recorded | 84 |
| abstract_inverted_index.segments | 123 |
| abstract_inverted_index.seizures | 4, 8, 51, 57 |
| abstract_inverted_index.subjects | 1, 11, 48, 53, 61 |
| abstract_inverted_index.automatic | 37 |
| abstract_inverted_index.diagnosis | 38 |
| abstract_inverted_index.difficult | 171 |
| abstract_inverted_index.empirical | 116 |
| abstract_inverted_index.encourage | 160 |
| abstract_inverted_index.epileptic | 3, 50 |
| abstract_inverted_index.new-onset | 77 |
| abstract_inverted_index.resulting | 143 |
| abstract_inverted_index.settings. | 173 |
| abstract_inverted_index.clinicians | 169 |
| abstract_inverted_index.customized | 137 |
| abstract_inverted_index.interictal | 13, 67, 93 |
| abstract_inverted_index.processing | 113, 165 |
| abstract_inverted_index.Identifying | 0 |
| abstract_inverted_index.challenging | 20 |
| abstract_inverted_index.inspection, | 25, 97 |
| abstract_inverted_index.low-density | 66 |
| abstract_inverted_index.psychogenic | 6, 56 |
| abstract_inverted_index.recordings. | 70 |
| abstract_inverted_index.non-invasive | 65 |
| abstract_inverted_index.psychotropic | 108 |
| abstract_inverted_index.automatically | 134 |
| abstract_inverted_index.convolutional | 138 |
| abstract_inverted_index.decomposition | 118 |
| abstract_inverted_index.differentiate | 45 |
| abstract_inverted_index.non-epileptic | 7, 55 |
| abstract_inverted_index.registration. | 111 |
| abstract_inverted_index.classification, | 152 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5024644806 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 10 |
| corresponding_institution_ids | https://openalex.org/I36443711, https://openalex.org/I4210098397 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.6299999952316284 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.79235246 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |