Publication
Heuristic Optimization of Deep and Shallow Classifiers: An Application for Electroencephalogram Cyclic Alternating Pattern Detection
| dc.contributor.author | Mendonça, Fábio | |
| dc.contributor.author | Mostafa, Sheikh Shanawaz | |
| dc.contributor.author | Freitas, Diogo | |
| dc.contributor.author | Dias, Fernando Morgado | |
| dc.contributor.author | Ravelo-García, Antonio G. | |
| dc.date.accessioned | 2023-06-01T14:00:22Z | |
| dc.date.available | 2023-06-01T14:00:22Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Methodologies for automatic non-rapid eye movement and cyclic alternating pattern analysis were proposed to examine the signal from one electroencephalogram monopolar derivation for the A phase, cyclic alternating pattern cycles, and cyclic alternating pattern rate assessments. A population composed of subjects free of neurological disorders and subjects diagnosed with sleep-disordered breathing was studied. Parallel classifications were performed for non-rapid eye movement and A phase estimations, examining a one-dimension convolutional neural network (fed with the electroencephalogram signal), a long short-term memory (fed with the electroencephalogram signal or with proposed features), and a feed-forward neural network (fed with proposed features), along with a finite state machine for the cyclic alternating pattern cycle scoring. Two hyper-parameter tuning algorithms were developed to optimize the classifiers. The model with long short-term memory fed with proposed features was found to be the best, with accuracy and area under the receiver operating characteristic curve of 83% and 0.88, respectively, for the A phase classification, while for the non-rapid eye movement estimation, the results were 88% and 0.95, respectively. The cyclic alternating pattern cycle classification accuracy was 79% for the same model, while the cyclic alternating pattern rate percentage error was 22%. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Mendonça, F.; Mostafa, S.S.; Freitas, D.; Dias, F. M.; Ravelo-García, A.G. Heuristic Optimization of Deep and Shallow Classifiers: An Application for Electroencephalogram Cyclic Alternating Pattern Detection. Entropy 2022, 24, 688. https:// doi.org/10.3390/e24050688 | pt_PT |
| dc.identifier.doi | 10.3390/e24050688 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.13/5213 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | MDPI | pt_PT |
| dc.relation | User Profiling: An AGM-Based Belief Revision Approach Applied to Dynamic of Profiles | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | 1D-CNN | pt_PT |
| dc.subject | ANN | pt_PT |
| dc.subject | CAP | pt_PT |
| dc.subject | HOSA | pt_PT |
| dc.subject | LSTM | pt_PT |
| dc.subject | . | pt_PT |
| dc.subject | Escola Superior de Tecnologias e Gestão | pt_PT |
| dc.subject | Faculdade de Ciências Exatas e da Engenharia | pt_PT |
| dc.title | Heuristic Optimization of Deep and Shallow Classifiers: An Application for Electroencephalogram Cyclic Alternating Pattern Detection | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | User Profiling: An AGM-Based Belief Revision Approach Applied to Dynamic of Profiles | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT//2021.07966.BD/PT | |
| oaire.citation.issue | 5 | pt_PT |
| oaire.citation.startPage | 688 | pt_PT |
| oaire.citation.title | Entropy | pt_PT |
| oaire.citation.volume | 24 | pt_PT |
| person.familyName | Silva Mendonça | |
| person.familyName | Mostafa | |
| person.familyName | Teixeira Freitas | |
| person.familyName | Morgado-Dias | |
| person.familyName | Ravelo-García | |
| person.givenName | Fábio Rúben | |
| person.givenName | Sheikh Shanawaz | |
| person.givenName | Diogo Nuno | |
| person.givenName | Fernando | |
| person.givenName | Antonio G. | |
| person.identifier | 34497 | |
| person.identifier | yfy16oUAAAAJ | |
| person.identifier.ciencia-id | 7F1E-8AE9-3098 | |
| person.identifier.ciencia-id | EE14-BEB3-F82B | |
| person.identifier.ciencia-id | 9C13-AF9C-25F3 | |
| person.identifier.ciencia-id | 7B14-DF07-AA6D | |
| person.identifier.orcid | 0000-0002-5107-3248 | |
| person.identifier.orcid | 0000-0002-7677-0971 | |
| person.identifier.orcid | 0000-0002-2351-8676 | |
| person.identifier.orcid | 0000-0001-7334-3993 | |
| person.identifier.orcid | 0000-0002-8512-965X | |
| person.identifier.rid | N-9228-2015 | |
| person.identifier.scopus-author-id | 55489640900 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
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